Robot Perception and Learning

Lab Meeting Dec. 29, 2011 (David): Semantic fusion of laser and vision in pedestrian detection (PR 2010)

2011-12-28T14:19:00.004+08:00

Lab Meeting Dec. 29, 2011 (David): Semantic fusion of laser and vision in pedestrian detection (PR 2010)

Luciano Oliveira, Urbano Nunes, Paulo Peixoto, Marco Silva, Fernando Moita

Abstract
Fusion of laser and vision in object detection has been accomplished by two main approaches: (1) independent integration of sensor-driven features or sensor-driven classifiers, or (2) a region of interest (ROI) is found by laser segmentation and an image classifier is used to name the projected ROI. Here, we propose a novel fusion approach based on semantic information, and embodied on many levels. Sensor fusion is based on spatial relationship of parts-based classifiers, being performed via a Markov logic network. The proposed system deals with partial segments, it is able to recover depth information even if the laser fails, and the integration is modeled through contextual information—characteristics not found on previous approaches. Experiments in pedestrian detection demonstrate the effectiveness of our method over data sets gathered in urban scenarios.

Paper Link

Local Link

Lab Meeting Dec. 22, 2011 (Wang Li): Fast Point Feature Histograms (FPFH) for 3D Registration (ICRA 2009)

2011-12-21T19:47:00.002+08:00

Fast Point Feature Histograms (FPFH) for 3D Registration

Radu Bogdan Rusu
Nico Blodow
Michael Beetz

Abstract

In this paper, we modify the mathematical expressions of Point Feature Histograms (PFH), and perform a rigorous analysis on their robustness and complexity for the problem of 3D registration. More concretely, we present optimizations that reduce the computation times drastically by either caching previously computed values or by revising their theoretical formulations. The latter results in a new type of local features, called Fast Point Feature Histograms (FPFH), which retain most of the discriminative power of the PFH. Moreover, we propose an algorithm for the online computation of FPFH features, demonstrate their efficiency for 3D registration, and propose a new sample consensus based method for bringing two datasets into the convergence basin of a local non-linear optimizer: SAC-IA (SAmple Consensus Initial Alignment).

Paper Link

Lab Meeting December 22nd, 2011 (Jeff): Towards Semantic SLAM using a Monocular Camera

2011-12-21T17:56:00.004+08:00

Title: Towards Semantic SLAM using a Monocular Camera

Authors: Javier Civera, Dorian G´alvez-L´opez, L. Riazuelo, Juan D. Tard´os, and J. M. M. Montiel

Abstract:

Monocular SLAM systems have been mainly focused on producing geometric maps just composed of points or edges; but without any associated meaning or semantic content.
In this paper, we propose a semantic SLAM algorithm that merges in the estimated map traditional meaningless points with known objects. The non-annotated map is built using only the information extracted from a monocular image sequence. The known object models are automatically computed from a sparse set of images gathered by cameras that may be different from the SLAM camera. The models include both visual appearance and tridimensional information. The semantic or annotated part of the map –the objects– are estimated using the information in the image sequence and the precomputed object models.

The proposed algorithm runs an EKF monocular SLAM parallel to an object recognition thread. This latest one informs of the presence of an object in the sequence by searching
for SURF correspondences and checking afterwards their geometric compatibility. When an object is recognized it is inserted in the SLAM map, being its position measured and hence refined by the SLAM algorithm in subsequent frames. Experimental results show real-time performance for a handheld camera imaging a desktop environment and for a camera
mounted in a robot moving in a room-sized scenario.

Link:
IEEE International Conference on Intelligent Robots and Systems(IROS), 2011
LocalLink
http://webdiis.unizar.es/~jcivera/papers/civera_etal_iros11.pdf

Lab Meeting Dec. 15, 2011 (Alan): Two-View Motion Segmentation with Model Selection and Outlier Removal by RANSAC-Enhanced Dirichlet ... (IJCV 2010)

2011-12-15T07:49:00.003+08:00

Title: Two-View Motion Segmentation with Model Selection and Outlier Removal by RANSAC-Enhanced Dirichlet Process Mixture Models (IJCV 2010)

Authors: Yong-Dian Jian, Chu-Song Chen

Abstract:
We propose a novel motion segmentation algorithm based on mixture of Dirichlet process (MDP) models. In contrast to previous approaches, we consider motion segmentation and its model selection regarding to the number of motion models as an inseparable problem. Our algorithm can simultaneously infer the number of motion models, estimate the cluster memberships of correspondences, and identify the outliers. The main idea is to use MDP models to fully exploit the geometric consistencies before making premature decisions about the number of motion models. To handle outliers, we incorporate RANSAC into the inference process of MDP models. In the experiments, we compare the proposed algorithm with naive RANSAC, GPCA and Schindler’s method on both synthetic data and real image data. The experimental results show that we can handlemore motions and have satisfactory performance in the presence of various levels of noise and outlier.

Link

Lab Meeting Dec. 8, 2011 (Jim): Execution of a Dual-Object (Pushing) Action with Semantic Event Chains

2011-12-05T17:24:00.002+08:00

Title: “Execution of a Dual-Object (Pushing) Action with Semantic Event Chains”
Authors: Aksoy Eren Erdal, Dellen Babette, Tamosiunaite Minija, and Wörgötter Florentin
In IEEE-RAS Int. Conf. on Humanoid Robots, pp.576-583

Abstract:
Here we present a framework for manipulation execution based on the so called “Semantic Event Chain” which is an abstract description of relations between the objects in the scene. It captures the change of those relations during a manipulation and thereby provides the decisive temporal anchor points by which a manipulation is critically defined. Using semantic event chains a model of a manipulation can be learned. We will show that it is possible to add the required control parameters (the spatial anchor points) to this model, which can then be executed by a robot in a fully autonomous way. The process of learning and execution of semantic event chains is explained using a box pushing example

Link

Lab Meeting November 24, 2011 (Hank): A Large-Scale Hierarchical Multi-View RGB-D Object Dataset (ICRA 2011)

2011-11-24T06:54:00.002+08:00

Authors: K. Lai, L. Bo, X. Ren, and D. Fox.
Title:A Large-Scale Hierarchical Multi-View RGB-D Object Dataset
In: Proc. of International Conference on Robotics and Automation (ICRA), 2011

Abstract:
Over the last decade, the availability of public image repositories and recognition benchmarks has enabled rapid progress in visual object category and instance detection. Today we are witnessing the birth of a new generation of sensing technologies capable of providing high quality synchronized videos of both color and depth, the RGB-D (Kinectstyle) camera. With its advanced sensing capabilities and the potential for mass adoption, this technology represents an opportunity to dramatically increase robotic object recognition, manipulation, navigation, and interaction capabilities. In this paper, we introduce a large-scale, hierarchical multi-view object dataset collected using an RGB-D camera. The dataset contains 300 objects organized into 51 categories and has been made publicly available to the research community so as to enable rapid progress based on this promising technology. This paper describes the dataset collection procedure and introduces techniques for RGB-D based object recognition and detection, demonstrating that combining color and depth information substantially improves quality of results.

link

Lab Meeting November 24, 2011 (Jimmy): Tracking Mobile Users in Wireless Networks via Semi-Supervised Co-Localization (TPAMI 2011)

2011-11-23T23:56:00.003+08:00

Title: Tracking Mobile Users in Wireless Networks via Semi-Supervised Co-Localization
Authors: Jeffrey Junfeng Pan, Sinno Jialin Pan, Jie Yin, Lionel M. Ni, and Qiang Yang
In: TPAMI 2011

Abstract
Recent years have witnessed growing popularity of sensor and sensor-network technologies, supporting important practical applications. One of the fundamental issues is how to accurately locate a user with few labelled data in a wireless sensor network, where a major difﬁculty arises from the need to label large quantities of user location data, which in turn requires knowledge about the locations of signal transmitters, or access points. To solve this problem, we have developed a novel machine-learning-based approach that combines collaborative ﬁltering with graph-based semi-supervised learning to learn both mobile-users’ locations and the locations of access points. Our framework exploits both labelled and unlabelled data from mobile devices and access points. In our two-phase solution, we ﬁrst build a manifold-based model from a batch of labelled and unlabelled data in an ofﬂine training phase and then use a weighted k-nearest-neighbor method to localize a mobile client in an online localization phase. We extend the two-phase co-localization to an online and incremental model that can deal with labelled and unlabelled data that come sequentially and adapt to environmental changes. Finally, we embed an action model to the framework such that additional kinds of sensor signals can be utilized to further boost the performance of mobile tracking. Compared to other state-of-the-art systems, our framework has been shown to be more accurate while requiring less calibration effort in our experiments performed at three different test-beds.

[pdf]

Lab Meeting November 17, 2011 (Chih-Chung): Motion Planning under Uncertainty for Robotic Tasks with Long Time Horizons (IJRR 2011)

2011-11-16T20:10:00.005+08:00

Authors: Hanna Kurniawati, Yanzhu Du, David Hsu and Wee Sun Lee.

Abstract:
Motion planning with imperfect state information is a crucial capability for autonomous robots to operate reliably in uncertain and dynamic environments. Partially observable Markov decision processes (POMDPs) provide a principled general framework for planning under uncertainty. Using probabilistic sampling, point-based POMDP solvers have drastically improved the speed of POMDP planning, enabling us to handle moderately complex robotic tasks. However, robot motion planning tasks with long time horizons remains a severe obstacle for even the fastest point-based POMDP solvers today. This paper proposes Milestone Guided Sampling (MiGS), a new point-based POMDP solver,which exploits state space information to reduce e ective planning horizons. MiGS samples a set of points, called milestones, from a robot's state space and constructs a simpli ed representation of the state space from the sampled milestones. It then uses this representation of the state space to guide
sampling in the belief space and tries to capture the essential features of the belief space with a small number of sampled points. Preliminary results are very promising. We tested MiGS in simulation on several di cult POMDPs that model distinct robotic tasks with long time horizons in both 2-D and 3-D environments. These POMDPs are impossible to solve with the fastest point-based solvers today, but MiGS solved them in a few minutes.

Link

Lab Meeting November 03, 2011 (David): Real-Time Multi-Person Tracking with Detector Assisted Structure Propagation (ICCV'11 Workshop)

2011-11-02T14:50:00.003+08:00

Lab Meeting November 03, 2011 (David): Real-Time Multi-Person Tracking with Detector Assisted Structure Propagation (ICCV'11 Workshop)

Authors: Dennis Mitzel and Bastian Leibe

Abstract:
Classical tracking-by-detection approaches require a robust object detector that needs to be executed in each frame. However the detector is typically the most computationally expensive component, especially if more than one object class needs to be detected. In this paper we investigate how the usage of the object detector can be reduced by using stereo range data for following detected objects over time. To this end we propose a hybrid tracking framework consisting of a stereo based ICP (Iterative Closest Point) tracker and a high-level multi-hypothesis tracker. Initiated by a detector response, the ICP tracker follows individual pedestrians over time using just the raw depth information. Its output is then fed into the high-level tracker that is responsible for solving long-term data association and occlusion handling. In addition, we propose to constrain the detector to run only on some small regions of interest (ROIs) that are extracted from a 3D depth based occupancy map of the scene. The ROIs are tracked over time and only newly appearing ROIs are evaluated by the detector. We present experiments on real stereo sequences recorded from a moving camera setup in urban scenarios and show that our proposed approach achieves state of the art performance

Link

Lab Meeting October 27, 2011 (ShaoChen): A multiple hypothesis people tracker for teams of mobile robots (ICRA 2010)

2011-10-26T13:23:00.000+08:00

Title: A multiple hypothesis people tracker for teams of mobile robots (ICRA 2010)

Authors: Tsokas, N.A. and Kyriakopoulos, K.J.

Abstract: This paper tackles the problem of tracking walking people with multiple moving robots equipped with laser rangefinders. We present an adaptation to the classic Multiple Hypothesis Tracking method, which allows for one-to-many associations between targets and measurements in each cycle and is thus capable of operating in a multi-sensor scenario. In the context of two experiments, the successful integration of our tracking algorithm to a dual-robot setup is assessed.

Link

Lab Meeting October 13, 2011 (Alan): A Model-Selection Framework for Multibody Structure-and-Motion of Image Sequences (IJCV 2008)

2011-10-12T09:43:00.003+08:00

Title: A Model-Selection Framework for Multibody Structure-and-Motion of Image Sequences (IJCV 2008)

Authors: Konrad Schindler, David Suter and Hanzi Wang

Abstract: Given an image sequence of a scene consisting of multiple rigidly moving objects, multi-body structureand-motion (MSaM) is the task to segment the image feature tracks into the different rigid objects and compute the multiple-view geometry of each object.We present a framework for multibody structure-and-motion based on model selection. In a recover-and-select procedure, a redundant set of hypothetical scene motions is generated. Each subset of this pool of motion candidates is regarded as a possible explanation of the image feature tracks, and the most likely explanation is selected with model selection. The framework is
generic and can be used with any parametric camera model, or with a combination of different models. It can deal with sets of correspondences, which change over time, and it is robust to realistic amounts of outliers. The framework is demonstrated for different camera and scene models.

Link

Lab Meeting October 13th, 2011 (Jeff): Object Mapping, Recognition, and Localization from Tactile Geometry

2011-10-11T21:50:00.005+08:00

Title: Object Mapping, Recognition, and Localization from Tactile Geometry

Authors: Zachary Pezzementi, Caitlin Reyda, and Gregory D. Hager

Abstract:

We present a method for performing object recognition using multiple images acquired from a tactile sensor. The method relies on using the tactile sensor as an imaging device, and builds an object representation based on mosaics of tactile measurements. We then describe an algorithm that is able to recognize an object using a small number of tactile sensor readings. Our approach makes extensive use of sequential state estimation techniques from the mobile robotics literature, whereby we view the object recognition problem as one of estimating a consistent location within a set of object maps. We examine and test approaches based on both traditional
particle filtering and histogram filtering. We demonstrate both the mapping and recognition / localization techniques on a set of raised letter shapes using real tactile sensor data.

Link:
IEEE International Conference on Robotics and Automation(ICRA), 2011
LocalLink
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5980363

Lab Meeting Sep. 29, 2011 (Wang Li): A Coarse-to-fine Approach for Fast Deformable Object Detection (CVPR 2011)

2011-09-28T18:30:00.003+08:00

A Coarse-to-fine Approach for Fast Deformable Object Detection

Marco Pedersoli
Andrea Vedaldi
Jordi González

Abstract

We present a method that can dramatically accelerate object detection with part based models. The method is based on the observation that the cost of detection is likely to be dominated by the cost of matching each part to the image, and not by the cost of computing the optimal configuration of the parts as commonly assumed. Therefore accelerating detection requires minimizing the number of part-to-image comparisons. To this end we propose a multiple-resolutions hierarchical part based model and a corresponding coarse-to-fine inference procedure that recursively eliminates from the search space unpromising part placements. We evaluate our method extensively on the PASCAL VOC and INRIA datasets, demonstrating a very high increase in the detection speed with little degradation of the accuracy.

Paper Link

Lab Meeting September 22nd, 2011 (Jimmy): Vector Field SLAM

2011-09-21T16:47:00.002+08:00

Title: Vector Field SLAM
Authors: Jens-Steffen Gutmann, Gabriel Brisson, Ethan Eade, Philip Fong and Mario Munich
In: ICRA 2010

Abstract
Localization in unknown environments using low-cost sensors remains a challenge. This paper presents a new localization approach that learns the spatial variation of an observed continuous signal. We model the signal as a piecewise linear function and estimate its parameters using a simultaneous localization and mapping (SLAM) approach. We apply our framework to a sensor measuring bearing to active beacons where measurements are systematically distorted due to occlusion and signal reﬂections of walls and other objects present in the environment. Experimental results from running GraphSLAM and EKF-SLAM on manually collected sensor measurements as well as on data recorded on a vacuum-cleaner robot validate our model.

[pdf]

Lab Meeting September 22nd, 2011 (Jim): Learning the semantics of object–action relations by observation

2011-09-18T15:51:00.004+08:00

Title: Learning the semantics of object–action relations by observation
Author: Eren Erdal Aksoy, Alexey Abramov, Johannes Dörr, Kejun Ning, Babette Dellen, and Florentin Wörgötter
The International Journal of Robotics Research 2011;30 1229-1249

Abstract:
Recognizing manipulations performed by a human and the transfer and execution of this by a robot is a difficult problem. We address this in the current study by introducing a novel representation of the relations between objects at decisive time points during a manipulation. Thereby, we encode the essential changes in a visual scenery in a condensed way such that a robot can recognize and learn a manipulation without prior object knowledge. To achieve this we continuously track image segments in the video and construct a dynamic graph sequence. Topological transitions of those graphs occur whenever a spatial relation between some segments has changed in a discontinuous way and these moments are stored in a transition matrix called the semantic event chain (SEC). We demonstrate that these time points are highly descriptive for distinguishing between different manipulations. Employing simple sub-string search algorithms, SECs can be compared and type-similar manipulations can be recognized with high confidence. As the approach is generic, statistical learning can be used to find the archetypal SEC of a given manipulation class. ...

http://ijr.sagepub.com/content/30/10/1229.full.pdf+html

Lab Meeting September 9th, 2011 (Steven): Learning Generic Invariances in Object Recognition: Translation and Scale

2011-09-09T12:39:00.003+08:00

Title: Learning Generic Invariances in Object Recognition: Translation and Scale

Authors: Joel Z Leibo, Jim Mutch, Lorenzo Rosasco, Shimon Ullman4, and Tomaso Poggio

Abstract:

Invariance to various transformations is key to object recognition but existing definitions of invariance are somewhat confusing while discussions of invariance are often confused. In this report, we provide an operational definition of invariance by formally defining perceptual tasks as classification problems. The definition should be appropriate for physiology, psychophysics and computational modeling.

For any specific object, invariance can be trivially “learned” by memorizing a sufficient number of example images of the transformed object. While our formal definition of invariance also covers such cases, this report focuses instead on invariance from very few images and mostly on invariances from one example. Image-plane invariances – such as translation, rotation and scaling – can be computed from a single image for any object. They are called generic since in principle they can be hardwired or learned (during development) for any object.

In this perspective, we characterize the invariance range of a class of feedforward architectures for visual recognition that mimic the hierarchical organization of the ventral stream.

We show that this class of models achieves essentially perfect translation and scaling invariance for novel images. In this architecture a new image is represented in terms of weights of ”templates” (e.g. “centers” or “basis functions”) at each level in the hierarchy. Such a representation inherits the invariance of each template, which is implemented through replication of the corresponding “simple” units across positions or scales and their “association” in a “complex” unit. We show simulations on real images that characterize the type and number of templates needed to support the invariant recognition of novel objects. We find that 1) the templates need not be visually similar to the target objects and that 2) a very small number of them is sufficient for good recognition.
These somewhat surprising empirical results have intriguing implications for the learning of invariant recognition during the development of a biological organism, such as a human baby. In particular, we conjecture that invariance to translation and scale may be learned by the association – through temporal contiguity – of a small number of primal templates, that is patches extracted from the images of an object moving on the retina across positions and scales. The number of templates can later be augmented by bootstrapping mechanisms using the correspondence provided by the primal templates – without the need of temporal contiguity.

Link

Lab Meeting September 9th, 2011 (Chih Chung): Identification and Representation of Homotopy (RSS 2011 Best paper)

2011-09-08T20:38:00.002+08:00

title: Identification and Representation of Homotopy
Classes of Trajectories for Search-based Path
Planning in 3D

Authors: Subhrajit Bhattacharya, Maxim Likhachev and Vijay Kumar

Abstract: There are many applications in motion planning
where it is important to consider and distinguish between
different homotopy classes of trajectories. Two trajectories are
homotopic if one trajectory can be continuously deformed into
another without passing through an obstacle, and a homotopy
class is a collection of homotopic trajectories. In this paper
we consider the problem of robot exploration and planning in
three-dimensional configuration spaces to (a) identify and classify
different homotopy classes; and (b) plan trajectories constrained
to certain homotopy classes or avoiding specified homotopy
classes. In previous work [1] we have solved this problem for
two-dimensional, static environments using the Cauchy Integral
Theorem in concert with graph search techniques. The robot
workspace is mapped to the complex plane and obstacles are poles
in this plane. The Residue Theorem allows the use of integration
along the path to distinguish between trajectories in different
homotopy classes. However, this idea is fundamentally limited
to two dimensions. In this work we develop new techniques to
solve the same problem, but in three dimensions, using theorems
from electromagnetism. The Biot-Savart law lets us design an
appropriate vector field, the line integral of which, using the
integral form of Ampere’s Law, encodes information about
homotopy classes in three dimensions. Skeletons of obstacles
in the robot world are extracted and are modeled by currentcarrying
conductors. We describe the development of a practical
graph-search based planning tool with theoretical guarantees
by combining integration theory with search techniques, and
illustrate it with examples in three-dimensional spaces such as
two-dimensional, dynamic environments and three-dimensional
static environments.

link

Lab Meeting September 2nd, 2011 (David): Multiclass Multimodal Detection and Tracking in Urban Environments

2011-09-01T12:00:00.000+08:00

Title: Multiclass Multimodal Detection and Tracking in Urban Environments

Author: Luciano Spinello, Rudolph Triebel and Roland Siegwart

Abstract:
This paper presents a novel approach to detect and track people and cars based on the combined information retrieved from a camera and a laser range scanner. Laser data points are classified by using boosted Conditional Random Fields (CRF), while the image based detector uses an extension of the Implicit Shape Model (ISM), which learns a codebook of local descriptors from a set of hand-labeled images and uses them to vote for centers of detected objects. Our extensions to ISM include the learning of object parts and template masks to obtain more distinctive votes for the particular object classes. The detections from both sensors are then fused and the objects are tracked using a Kalman Filter with multiple motion models. Experiments conducted in real-world urban scenarios demonstrate the effectiveness of our approach.

Link:
IJRR copy
localcopy

Lab Meeting August 19th, 2011 (Jeff): A Robust Qualitative Planner for Mobile Robot Navigation Using Human-Provided Maps

2011-08-18T10:31:00.003+08:00

Title: A Robust Qualitative Planner for Mobile Robot Navigation Using Human-Provided Maps

Authors: Danelle C. Shah and Mark E. Campbell

Abstract:

A novel method for controlling a mobile robot using qualitative inputs in the context of an approximate map, such as one sketched by a human, is presented. By defining a desired trajectory with respect to observable landmarks, human operators can send semi-autonomous robots into areas for which a truth map is not available. Waypoint planning is formulated as a quadratic optimization problem, resulting in robot trajectories in the true environment that are qualitatively similar to those provided by the human. The algorithm is implemented both in simulation and on a mobile robot platform in several different environments. A sensitivity analysis is performed, illustrating how the method is robust to uncertainties, even large sketch distortions, and allows the robot to adapt and re-plan according to its most current perception of the world.

Link:
IEEE International Conference on Robotics and Automation(ICRA), 2011
LocalLink

A robot that flies like a bird | Video on TED.com

2011-07-23T12:43:00.000+08:00

A robot that flies like a bird | Video on TED.com

Lab meeting June 29th (Jim): A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning

2011-06-27T21:01:00.002+08:00

Title: A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning

Stephane Ross, Geoffrey Gordon, and J. Andrew (Drew) Bagnell

Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS), April, 2011.

Abstracts:
Sequential prediction problems such as imitation learning, where future observations depend on previous predictions (actions), violate the common i.i.d. assumptions made in statistical learning. ... In this paper, we propose a new iterative algorithm, which trains a stationary deterministic policy, that can be seen as a no regret algorithm in an online learning setting. We show that any such no regret algorithm, combined with additional reduction assumptions, must find a policy with good performance under the distribution of observations it induces in such sequential settings.

Link

Lab Meeting June 22th (Chih-Chung):Minimum Snap Trajectory Generation and Control for Quadrotors (ICRA2011,best paper)

2011-06-20T10:24:00.004+08:00

Title: Minimum Snap Trajectory Generation and Control for Quadrotors

Authors: Daniel Mellinger and Vijay Kumar

Abstracts:
We address the controller design and the trajectory
generation for a quadrotor maneuvering in three
dimensions in a tightly constrained setting typical of indoor
environments. In such settings, it is necessary to allow for
significant excursions of the attitude from the hover state and
small angle approximations cannot be justified for the roll
and pitch. We develop an algorithm that enables the real-time
generation of optimal trajectories through a sequence of 3-D
positions and yaw angles, while ensuring safe passage through
specified corridors and satisfying constraints on velocities,
accelerations and inputs. A nonlinear controller ensures the
faithful tracking of these trajectories. Experimental results
illustrate the application of the method to fast motion (5-10
body lengths/second) in three-dimensional slalom courses.

[link]

Lab Meeting June 15th (Shao-Chen): Distributed Robust Data Fusion Based on Dynamic Voting (ICRA2011)

2011-06-15T02:30:00.000+08:00

Title: Distributed Robust Data Fusion Based on Dynamic Voting

Authors: Eduardo Montijano, Sonia Mart´ınez and Carlos Sagues

Abstract:

Data association mistakes, estimation and measurement errors are some of the factors that can contribute to incorrect observations in robotic sensor networks. In order to act reliably, a robotic network must be able to fuse and correct its perception of the world by discarding any outlier information. This is a difﬁcult task if the network is to be deployed remotely and the robots do not have access to groundtruth sites or manual calibration. In this paper, we present a novel, distributed scheme for robust data fusion in autonomous robotic networks. The proposed method adapts the RANSAC algorithm to exploit measurement redundancy, and enables robots determine an inlier observation with local communications. Different hypotheses are generated and voted for using a dynamic consensus algorithm. As the hypotheses are computed, the robots can change their opinion making the voting process dynamic. Assuming that at least one hypothesis is initialized with only inliers, we show that the method converges to the maximum likelihood of all the inlier observations in a general instance. Several simulations exhibit the good performance of the algorithm, which also gives acceptable results in situations where the conditions to guarantee convergence do not hold.

[link]

Lab Meeting June 15th (David): Sparse Scene Flow Segmentation for Moving Object Detection (Intelligent Vehicles Symposium 2011)

2011-06-14T21:12:00.002+08:00

Title: Sparse Scene Flow Segmentation for Moving Object Detection (Intelligent Vehicles Symposium 2011)

Authors: P. Lenz, J. Ziegler, A. Geiger, M. Roser

Abstract:
Modern driver assistance systems such as collision avoidance or intersection assistance need reliable information on the current environment. Extracting such information from camera-based systems is a complex and challenging task for inner city taffic scenarios. This paper presents an approach for object detection utilizing sparse scene flow. For consecutive stereo images taken from a moving vehicle, corresponding interest points are extracted. Thus, for every interest point, disparity and optical flow values are known and consequently, scene flow can be calculated. Adjacent interest points describing a similar scene flow are considered to belong to one rigid object. The proposed method does not rely on object classes and allows for a robust detection of dynamic objects in traffic scenes. Leading vehicles are continuously detected for several frames. Oncoming objects are detected within five frames after their appearance.

Link: http://www.rainsoft.de/publications/iv11b.pdf

Lab Meeting June 8th, 2011 (Jeff): Incremental Construction of the Saturated-GVG for Multi-Hypothesis Topological SLAM

2011-06-07T20:11:00.002+08:00

Title: Incremental Construction of the Saturated-GVG for Multi-Hypothesis
Topological SLAM

Authors: Tong Tao, Stephen Tully, George Kantor, and Howie Choset

Abstract:

The generalized Voronoi graph (GVG) is a topological representation of an environment that can be incrementally constructed with a mobile robot using sensor-based control. However, because of sensor range limitations, the GVG control law will fail when the robot moves into a large open area. This paper discusses an extended GVG approach to topological navigation and mapping: the saturated generalized Voronoi graph (S-GVG), for which the robot employs an additional wall-following behavior to navigate along obstacles at the range limit of the sensor. In this paper, we build upon previous work related to the S-GVG and provide two important contributions: 1) a rigorous discussion of the control laws and algorithm modifications that are necessary for incremental construction of the S-GVG with a mobile robot, and 2) a method for incorporating the S-GVG into a novel multi-hypothesis SLAM algorithm for loop-closing and localization. Experiments with a wheeled mobile robot in an office-like environment validate the ffectiveness of the proposed approach.

Link:
IEEE International Conference on Robotics and Automation(ICRA), 2011
http://www.cs.cmu.edu/~biorobotics/papers/icra11_tao.pdf
LocalLink

Lab Meeting June 1, 2011 (Alan): Semantic Structure from Motion (CVPR 2011)

2011-06-01T06:00:00.002+08:00

Title: Semantic Structure from Motion (CVPR 2011)

Authors: Sid Yingze Bao and Silvio Savarese

Abstract

Conventional rigid structure from motion (SFM) addresses the problem of recovering the camera parameters (motion) and the 3D locations (structure) of scene points, given observed 2D image feature points. In this paper, we propose a new formulation called Semantic Structure From Motion (SSFM). In addition to the geometrical constraints provided by SFM, SSFM takes advantage of both semantic and geometrical properties associated with objects in the scene (Fig. 1). These properties allow us to recover not only the structure and motion but also the 3D locations, poses, and categories of objects in the scene. We cast this problem as a max-likelihood problem where geometry (cameras, points, objects) and semantic information (object classes) are simultaneously estimated. The key intuition is that, in addition to image features, the measurements of objects across views provide additional geometrical constraints that relate cameras and scene parameters. These constraints make the geometry estimation process more robust and, in turn, make object detection more accurate. Our framework has the unique ability to: i) estimate camera poses only from object detections, ii) enhance camera pose estimation, compared to feature-point-based SFM algorithms, iii) improve object detections given multiple uncalibrated images, compared to independently detecting objects in single images. Extensive quantitative results on three datasets – LiDAR cars, street-view pedestrians, and Kinect office desktop – verify our theoretical claims.

Link

Lab Meeting June 1, 2011 (Wang Li): Articulated pose estimation with flexible mixtures-of-parts (CVPR 2011)

2011-05-31T21:54:00.004+08:00

Articulated pose estimation with flexible mixtures-of-parts

Yi Yang
Deva Ramanan

Abstract

We describe a method for human pose estimation in static images based on a novel representation of part models. Notably, we do not use articulated limb parts, but rather capture orientation with a mixture of templates for each part. We describe a general, flexible mixture model for capturing contextual co-occurrence relations between parts, augmenting standard spring models that encode spatial relations. We show that such relations can capture notions of local rigidity. When co-occurrence and spatial relations are tree-structured, our model can be efficiently optimized with dynamic programming. We present experimental results on standard benchmarks for pose estimation that indicate our approach is the state-of-the-art system for pose estimation, outperforming past work by 50% while being orders of magnitude faster.

Paper Link

ICRA 2011 Awards

2011-05-16T12:36:00.009+08:00

Best Manipulation Paper

WINNER! Characterization of Oscillating Nano Knife for Single Cell Cutting by Nanorobotic Manipulation System Inside ESEM: Yajing Shen, Masahiro Nakajima, Seiji Kojima, Michio Homma, Yasuhito Ode, Toshio Fukuda [pdf]
Wireless Manipulation of Single Cells Using Magnetic Microtransporters: Mahmut Selman Sakar, Edward Steager, Anthony Cowley, Vijay Kumar, George J Pappas
Hierarchical Planning in the Now: Leslie Kaelbling, Tomas Lozano-Perez
Selective Injection and Laser Manipulation of Nanotool Inside a Specific Cell Using Optical Ph Regulation and Optical Tweezers: Hisataka Maruyama, Naoya Inoue, Taisuke Masuda, Fumihito Arai
Configuration-Based Optimization for Six Degree-Of-Freedom Haptic Rendering for Fine Manipulation: Dangxiao Wang, Xin Zhang, Yuru Zhang, Jing Xiao

Best Vision Paper

Model-Based Localization of Intraocular Microrobots for Wireless Electromagnetic Control: Christos Bergeles, Bradley Kratochvil, Bradley J. Nelson
Fusing Optical Flow and Stereo in a Spherical Depth Panorama Using a Single-Camera Folded Catadioptric Rig: Igor Labutov, Carlos Jaramillo, Jizhong Xiao
3-D Scene Analysis Via Sequenced Predictions Over Points and Regions: Xuehan Xiong, Daniel Munoz, James Bagnell, Martial Hebert
Fast and Accurate Computation of Surface Normals from Range Images: Hernan Badino, Daniel Huber, Yongwoon Park, Takeo Kanade
WINNER! Sparse Distance Learning for Object Recognition Combining RGB and Depth Information: Kevin Lai, Liefeng Bo, Xiaofeng Ren, Dieter Fox [pdf]

Best Automation Paper

WINNER! Automated Cell Manipulation: Robotic ICSI: Zhe Lu, Xuping Zhang, Clement Leung, Navid Esfandiari, Robert Casper, Yu Sun [pdf]
Efficient AUV Navigation Fusing Acoustic Ranging and Side-Scan Sonar: Maurice Fallon, Michael Kaess, Hordur Johannsson, John Leonard
Vision-Based 3D Bicycle Tracking Using Deformable Part Model and Interacting Multiple Model Filter: Hyunggi Cho, Paul E. Rybski, Wende Zhang
High-Accuracy GPS and GLONASS Positioning by Multipath Mitigation Using Omnidirectional Infrared Camera: Taro Suzuki, Mitsunori Kitamura, Yoshiharu Amano, Takumi Hashizume
Deployment of a Point and Line Feature Localization System for an Outdoor Agriculture Vehicle: Jacqueline Libby, George Kantor

Best Medical Robotics Paper

Design of Adjustable Constant-Force Forceps for Robot-Assisted Surgical Manipulation: Chao-Chieh Lan, Jung-Yuan Wang
Design Optimization of Concentric Tube Robots Based on Task and Anatomical Constraints: Chris Bedell, Jesse Lock, Andrew Gosline, Pierre Dupont
GyroLock - First in Vivo Experiments of Active Heart Stabilization Using Control Moment Gyro (CMG): Julien Gagne, Olivier Piccin, Edouard Laroche, Michele Diana, Jacques Gangloff
Metal MEMS Tools for Beating-Heart Tissue Approximation: Evan Butler, Chris Folk, Adam Cohen, Nikolay Vasilyev, Rich Chen, Pedro del Nido, Pierre Dupont
WINNER! An Articulated Universal Joint Based Flexible Access Robot for Minimally Invasive Surgery: Jianzhong Shang, David Noonan, Christopher Payne, James Clark, Mikael Hans Sodergren, Ara Darzi, Guang-Zhong Yang [pdf]

Best Conference Paper

WINNER! Minimum Snap Trajectory Generation and Control for Quadrotors: Daniel Mellinger, Vijay Kumar [pdf]
Autonomous Multi-Floor Indoor Navigation with a Computationally Constrained Micro Aerial Vehicle: Shaojie Shen, Nathan Michael, Vijay Kumar
Dexhand : A Space Qualfied Multi-Fingered Robotic Hand: Maxime Chalon, Armin Wedler, Andreas Baumann, Wieland Bertleff, Alexander Beyer, Jörg Butterfass, Markus Grebenstein, Robin Gruber, Franz Hacker, Erich Krämer, Klaus Landzettel, Maximilian Maier, Hans-Juergen Sedlmayr, Nikolaus Seitz, Fabian Wappler, Bertram Willberg, Thomas Wimboeck, Frederic Didot, Gerd Hirzinger
Time Scales and Stability in Networked Multi-Robot Systems: Mac Schwager, Nathan Michael, Vijay Kumar, Daniela Rus
Bootstrapping Bilinear Models of Robotic Sensorimotor Cascades: Andrea Censi, Richard Murray

KUKA Service Robotics Best Paper

Distributed Coordination and Data Fusion for Underwater Search: Geoffrey Hollinger, Srinivas Yerramalli, Sanjiv Singh, Urbashi Mitra, Gaurav Sukhatme
WINNER! Dynamic Shared Control for Human-Wheelchair Cooperation: Qinan Li, Weidong Chen, Jingchuan Wang [pdf]
Towards Joint Attention for a Domestic Service Robot -- Person Awareness and Gesture Recognition Using Time-Of-Flight Cameras: David Droeschel, Jorg Stuckler, Dirk Holz, Sven Behnke
Electromyographic Evaluation of Therapeutic Massage Effect Using Multi-Finger Robot Hand: Ren C. Luo, Chih-Chia Chang

Best Video

Catching Flying Balls and Preparing Coffee: Humanoid Rollin'Justin Performs Dynamic and Sensitive Tasks: Berthold Baeuml, Florian Schmidt, Thomas Wimboeck, Oliver Birbach, Alexander Dietrich, Matthias Fuchs, Werner Friedl, Udo Frese, Christoph Borst, Markus Grebenstein, Oliver Eiberger, Gerd Hirzinger
Recent Advances in Quadrotor Capabilities: Daniel Mellinger, Nathan Michael, Michael Shomin, Vijay Kumar
WINNER! High Performance of Magnetically Driven Microtools with Ultrasonic Vibration for Biomedical Innovations: Masaya Hagiwara, Tomohiro Kawahara, Lin Feng, Yoko Yamanishi, Fumihito Arai [pdf]

Best Cognitive Robotics Paper

WINNER! Donut As I Do: Learning from Failed Demonstrations: Daniel Grollman, Aude Billard [pdf]
A Discrete Computational Model of Sensorimotor Contingencies for Object Perception and Control of Behavior: Alexander Maye, Andreas Karl Engel
Skill Learning and Task Outcome Prediction for Manipulation: Peter Pastor, Mrinal Kalakrishnan, Sachin Chitta, Evangelos Theodorou, Stefan Schaal
Integrating Visual Exploration and Visual Search in Robotic Visual Attention: The Role of Human-Robot Interaction: Momotaz Begum, Fakhri Karray

Lab Meeting May 3rd (Andi): Face/Off: Live Facial Puppetry

2011-05-03T12:17:00.003+08:00

Thibaut Weise, Hao Li, Luc Van Gool, Mark Pauly

Proceedings of the Eighth ACM SIGGRAPH / Eurographics Symposium on Computer Animation 2009, Best Paper Award

We present a complete integrated system for live facial puppetry that enables high-resolution real-time facial expression tracking with transfer to another person's face. The system utilizes a real-time structured light scanner that provides dense 3D data and texture. A generic template mesh, fitted to a rigid reconstruction of the actor's face, is tracked offline in a training stage through a set of expression sequences. These sequences are used to build a person-specific linear face model that is subsequently used for online face tracking and expression transfer. Even with just a single rigid pose of the target face, convincing real-time facial animations are achievable. The actor becomes a puppeteer with complete and accurate control over a digital face.

[paper]

Lab Meeting May 3( KuenHan ), Multiple Targets Tracking in World Coordinate with a Single, Minimally Calibrated Camera (ECCV 2010)

2011-05-02T16:10:00.002+08:00

Title: Multiple Targets Tracking in World Coordinate with a Single, Minimally Calibrated Camera. ( ECCV 2010, poster)
Author: Wongun Choi, Silvio Savarese.

Abstract:
Tracking multiple objects is important in many application
domains. We propose a novel algorithm for multi-object tracking that
is capable of working under very challenging conditions such as min-
imal hardware equipment, uncalibrated monocular camera, occlusions
and severe background clutter. To address this problem we propose a
new method that jointly estimates object tracks, estimates correspond-
ing 2D/3D temporal trajectories in the camera reference system as well
as estimates the model parameters (pose, focal length, etc) within a
coherent probabilistic formulation. Since our goal is to estimate stable
and robust tracks that can be univocally associated to the object IDs,
we propose to include in our formulation an interaction (attraction and
repulsion) model that is able to model multiple 2D/3D trajectories in
space-time and handle situations where objects occlude each other. We
use a MCMC particle ltering algorithm for parameter inference and
propose a solution that enables accurate and e cient tracking and cam-
era model estimation. Qualitative and quantitative experimental results
obtained using our own dataset and the publicly available ETH dataset
shows very promising tracking and camera estimation results.

Link
Website

2011-05-02T11:27:00.001+08:00

NTU PAL Thesis Defense: Mobile Robot Localization in Large-scale Dynamic Environments

2011-04-20T21:37:00.005+08:00

Mobile Robot Localization in Large-scale Dynamic Environments

Shao-Wen Yang

Doctoral Dissertation Defense

Department of Computer Science and Information Engineering

National Taiwan University

Time: Thursday, 19 May, 2011 at 8:00AM +0800 (CST)

Location: R542, Der-Tian Hall

Advisor: Chieh-Chih Wang

Thesis Committee:

Li-Chen Fu

Jane Yung-Jen Hsu

Han-Pang Huang

Ta-Te Lin

Chu-Song Chen, Sinica

Jwu-Sheng Hu, NCTU

John J. Leonard, MIT

Abstract:

Localization is the most fundamental problem to providing a mobile robot with autonomous capabilities. Whilst simultaneous localization and mapping (SLAM) and moving object tracking (MOT) have attracted immense attention in the last decade, the focus of robotics continues to shift from stationary robots in a factory automation environment to mobile robots operating in human-inhabited environments. State of the art relying on the static world assumption can fail in the real environment that is typically dynamic. Specifically, the real environment is challenging for mobile robots due to the variety of perceptual inconsistency over space and time. Development of situational awareness is particularly important so that the mobile robots can adapt quickly to changes in the environment.

In this thesis, we explore the problem of mobile robot localization in the real world in theory and practice, and show that localization can benefit from both stationary and dynamic entities.

The performance of ego-motion estimation depends on the consistency between sensory information at successive time steps, whereas the performance of localization relies on the consistency between the sensory information and the a priori map. The inconsistencies make a robot unable to robustly determine its location in the environment. We show that mobile robot localization, as well as ego-motion estimation, and moving object detection are mutually beneficial. Most importantly, addressing the inconsistencies serves as the basis for mobile robot localization, and forms a solid bridge between SLAM and MOT.

Localization, as well as moving object detection, is not only challenging but also difficult to evaluate quantitatively due to the lack of a realistic ground truth. As the key competencies for mobile robotic systems are localization and semantic context interpretation, an annotated data set, as well as an interactive annotation tool, is released to facilitate the development, evaluation and comparison of algorithms for localization, mapping, moving object detection, moving object tracking, etc.

In summary, a unified stochastic framework is introduced to solve the problems of motion estimation and motion segmentation simultaneously in highly dynamic environments in real time. A dual-model localization framework that uses information from both the static scene and dynamic entities is proposed to improve the localization performance by explicitly incorporating, rather than filtering out, moving object information. In the ample experiment, a sub-meter accuracy is achieved, without the aid of GPS, which is adequate for autonomous navigation in crowded urban scenes. The empirical results suggest that the performance of localization can be improved when handling the changing environment explicitly.

Download:

Thesis draft: http://any.csie.ntu.edu.tw/thesis/yang_thesis-v1_0.pdf

Lab Meeting April 20, 2011 (fish60): Donut as I do: Learning from failed demonstrations

2011-04-17T19:30:00.004+08:00

Title: Donut as I do: Learning from failed demonstrations In: 2011 IEEE International Conference on Robotics and Automation Authors: Grollman, Daniel (Ecole Polytechnique Federale de Lausanne), Billard, Aude (EPFL) Abstract The canonical Robot Learning from Demonstration scenario has a robot observing human demonstrations of a task or behavior in a few situations, and then developing a generalized controller. ... However, the underlying assumption is that the demonstrations are successful, and are appropriate to reproduce. We, instead, consider the possibility that the human has failed in their attempt, and their demonstration is an example of what not to do. Thus, instead of maximizing the similarity of generated behaviors to those of the demonstrators, we examine two methods that deliberately avoid repeating the human's mistakes. Link

Lab Meeting April 13, 2011 (Will): Hilbert Space Embeddings of Hidden Markov Models (ICML2010)

2011-04-12T08:55:00.002+08:00

Titile: Hilbert Space Embeddings of Hidden Markov Model

In: ICML 2010

Authors: Le Song, Byron Boots, Sajid Siddiqi, Geoffrey Gordon, Alex Smola

Abstract

Hidden Markov Models (HMMs) are important tools for modeling sequence data. However, they are restricted to discrete latent states, and are largely restricted to Gaussian and discrete observations. And, learning algorithms for HMMs have predominantly relied on local search heuristics, with the exception of spectral methods such as those described below. We propose a nonparametric HMM that extends traditional HMMs to structured and non-Gaussian continuous distributions. Furthermore, we derive a local-minimum-free kernel spectral algorithm for learning these HMMs. We apply our method to robot vision data, slot car inertial sensor data and audio event classification data, and show that in these applications, embedded HMMs exceed the previous state-of-the-art performance.

[pdf]

Lab Meeting April 13, 2011 (Jimmy): WiFi-SLAM Using Gaussian Process Latent Variable Models (IJCAI2007)

2011-04-12T00:19:00.003+08:00

Title: WiFi-SLAM Using Gaussian Process Latent Variable Models
In: IJCAI 2007
Authors: Brian Ferris, Dieter Fox, and Neil Lawrence

Abstract
WiFi localization, the task of determining the physical location of a mobile device from wireless signal strengths, has been shown to be an accurate method of indoor and outdoor localization and a powerful building block for location-aware applications. However, most localization techniques require a training set of signal strength readings labeled against a ground truth location map, which is prohibitive to collect and maintain as maps grow large. In this paper we propose a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GPLVM) to determine the latent-space locations of unlabeled signal strength data. We show how GPLVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topological connectivity graph from a signal strength sequence which, in combination with the learned Gaussian Process signal strength model, can be used to perform efficient localization.

[pdf]

Lab Meeting March 30, 2011 (Chih-Chung): Progress Report

2011-03-29T17:34:00.001+08:00

I will show my recent work of moving target tracking and following, using laser scanner and PIONEER3 robot.

Lab Meeting March 30, 2011 (Chung-Han): Progress Report

2011-03-29T15:35:00.003+08:00

I will show the updated ground-truth annotation system with the newly collected data set.

Lab Meeting March 23, 2011 (David): Object detection and tracking for autonomous navigation in dynamic environments (IJRR 2010)

2011-03-22T14:40:00.000+08:00

Title: Object detection and tracking for autonomous navigation in dynamic environments (IJRR 2010)

Authors: Andreas Ess, Konrad Schindler, Bastian Leibe, Luc Van Gool

Abstract:
We address the problem of vision-based navigation in busy inner-city locations, using a stereo rig mounted on a mobile platform. In this scenario semantic information becomes important: rather than modeling moving objects as arbitrary obstacles, they should be categorized and tracked in order to predict their future behavior. To this end, we combine classical geometric world mapping with object category detection and tracking. Object-category-specific detectors serve to find instances of the most important object classes (in our case pedestrians and cars). Based on these detections, multi-object tracking recovers the objects' trajectories, thereby making it possible to predict their future locations, and to employ dynamic path planning. The approach is evaluated on challenging, realistic video sequences recorded at busy inner-city locations.

Link

Lab Meeting March 23, 2011 (Shao-Chen): A Comparison of Track-to-Track Fusion Algorithms for Automotive Sensor Fusion (MFI2008)

2011-03-22T13:55:00.000+08:00

Title: A Comparison of Track-to-Track Fusion Algorithms for Automotive Sensor Fusion (MFI2008, Multisensor Fusion and Integration for Intelligent Systems)

Authors: Stephan Matzka and Richard Altendorfer

Abstract:

In exteroceptive automotive sensor fusion, sensor data are usually only available as processed, tracked object data and not as raw sensor data. Applying a Kalman filter to such data leads to additional delays and generally underestimates the fused objects' covariance due to temporal correlations of individual sensor data as well as inter-sensor correlations. We compare the performance of a standard asynchronous Kalman filter applied to tracked sensor data to several algorithms for the track-to-track fusion of sensor objects of unknown correlation, namely covariance union, covariance intersection, and use of cross-covariance. For the simulation setup used in this paper, covariance intersection and use of cross-covariance turn out to yield significantly lower errors than a Kalman filter at a comparable computational load.

Link

Lab Meeting March 16th, 2011 (Andi): 3D Deformable Face Tracking with a Commodity Depth Camera

2011-03-14T13:50:00.001+08:00

Qin Cai , David Gallup , Cha Zhang and Zhengyou Zhang

Abstract: Recently, there has been an increasing number of depth cameras available at commodity prices. These cameras can usually capture both color and depth images in real-time, with limited resolution and accuracy. In this paper, we study the problem of 3D deformable face tracking with such commodity depth cameras. A regularized maximum

likelihood deformable model fitting (DMF) algorithm is developed, with special emphasis on handling the noisy input depth data. In particular, we present a maximum likelihood solution that can accommodate sensor noise represented by an arbitrary covariance matrix, which allows more elaborate modeling of the sensor’s accuracy. Furthermore, an 1 regularization scheme is proposed based on the semantics of the deformable face model, which is shown to be very effective in improving the tracking results. To track facial movement in subsequent frames, feature points in the texture images are matched across frames and integrated into the DMF framework seamlessly. The effectiveness of the proposed method is demonstrated with multiple sequences with ground truth information.

Full Paper

Lab Meeting March 9th, 2011(KuoHuei): progress report

2011-03-09T08:59:00.000+08:00

I will present my progress on Neighboring Objects Interaction models and tracking system.

Lab Meeting March 9, 2011 (Wang Li): Real-time Identification and Localization of Body Parts from Depth Images (ICRA 2010)

2011-03-08T15:47:00.002+08:00

Real-time Identification and Localization of Body Parts from Depth Images

Christian Plagemann
Varun Ganapathi
Daphne Koller
Sebastian Thrun

Abstract

We deal with the problem of detecting and identifying body parts in depth images at video frame rates. Our solution involves a novel interest point detector for mesh and range data that is particularly well suited for analyzing human shape. The interest points, which are based on identifying geodesic extrema on the surface mesh, coincide with salient points of the body, which can be classified using local shape descriptors. Our approach also provides a natural way of estimating a 3D orientation vector for a given interest point. This can be used to normalize the local shape descriptors to simplify the classification problem as well as to directly estimate the orientation of body parts in space.
Experiments show that our interest points in conjunction with a boosted patch classifier are significantly better in detecting body parts in depth images than state-of-the-art sliding-window based detectors.

Paper Link

Article: Perception beyond the Here and Now

2011-03-03T08:51:00.000+08:00

by Albrecht Schmidt, Marc Langheinrich, and Kristian Kersting
Computer, February 2011, pp. 86–88

A multitude of senses provide us with information about the here and now. What we see, hear, and feel in turn shape how we perceive our surroundings and understand the world. Our senses are extremely limited, however, and ever since humans began creating and using technology, they have tried to enhance their natural perception in various ways. (pdf)

Lab Meeting March 2nd, 2011 (Jeff): Observability-based Rules for Designing Consistent EKF SLAM Estimators

2011-02-28T23:09:00.002+08:00

Title: Observability-based Rules for Designing Consistent EKF SLAM Estimators

Authors: Guoquan P. Huang, Anastasios Mourikis, and Stergios I. Roumeliotis

Abstract:

In this work, we study the inconsistency problem of extended Kalman filter (EKF)-based simultaneous localization and mapping (SLAM) from the perspective of observability. We analytically prove that when the Jacobians of the process and measurement models are evaluated at the latest state estimates during every time step, the linearized error-state system employed in the EKF has an observable subspace of dimension higher than that of the actual, non-linear, SLAM system. As a result, the covariance estimates of the EKF undergo reduction in
directions of the state space where no information is available, which is a primary cause of the inconsistency. Based on these theoretical results, we propose a general framework for improving the consistency of EKF-based SLAM. In this framework, the EKF linearization points are selected in a way that ensures that the resulting linearized system model has an observable subspace of appropriate dimension. We describe two algorithms that are instances of this paradigm. In the first, termed observability constrained (OC)-EKF, the linearization points are selected so as to minimize their expected errors (i.e. the difference between the linearization point and the true state) under the observability constraints. In the second, the filter Jacobians are calculated using the first-ever available estimates for all state variables. This latter approach is termed first-estimates Jacobian (FEJ)-EKF. The proposed algorithms have been tested both in simulation and experimentally, and are shown to significantly outperform the standard EKF both in terms of accuracy and consistency.

Link:
The International Journal of Robotics Research(IJRR), Vol.5 April 2010
http://ijr.sagepub.com/content/29/5/502.full.pdf+html

Meka's M-1 Mobile Manipulator

2011-02-20T09:16:00.000+08:00

Meka M1 Mobile Manipulator vimeo from Meka Robotics on Vimeo.

Lab Meeting February 14, 2011 (fish60): Feature Construction for Inverse Reinforcement Learning

2011-02-09T15:32:00.002+08:00

Title: Feature Construction for Inverse Reinforcement Learning
Sergey Levine, Zoran Popović, Vladlen Koltun
NIPS 2010

Abstract:
The goal of inverse reinforcement learning is to find a reward function for a
Markov decision process, given example traces from its optimal policy. Current
IRL techniques generally rely on user-supplied features that form a concise basis
for the reward. We present an algorithm that instead constructs reward features
from a large collection of component features, by building logical conjunctions of
those component features that are relevant to the example policy. Given example
traces, the algorithm returns a reward function as well as the constructed features.

Link

Lab Meeting February 14, 2011 (Alan): Multibody Structure-from-Motion in Practice (PAMI 2010)

2011-02-09T10:36:00.003+08:00

Title: Multibody Structure-from-Motion in Practice (PAMI 2010)
Authors: Kemal Egemen Ozden, Konrad Schindler, and Luc Van Gool

Abstract—Multibody structure from motion (SfM) is the extension of classical SfM to dynamic scenes with multiple rigidly moving objects. Recent research has unveiled some of the mathematical foundations of the problem, but a practical algorithm which can handle realistic sequences is still missing. In this paper, we discuss the requirements for such an algorithm, highlight theoretical issues and practical problems, and describe how a static structure-from-motion framework needs to be extended to handle real dynamic scenes. Theoretical issues include different situations in which the number of independently moving scene objects changes: Moving objects can enter or leave the field of view, merge into the static background (e.g., when a car is parked), or split off from the background and start moving independently. Practical issues arise due to small freely moving foreground objects with few and short feature tracks. We argue that all of these difficulties need to be handled online as structure-from-motion estimation progresses, and present an exemplary solution using the framework of probabilistic model-scoring.

Link

Lab Meeting January 17( KuenHan ), Moving Object Detection by Multi-View Geometric Techniques from a Single Camera Mounted Robot (IROS 2009)

2011-01-17T09:58:00.002+08:00

Title: Moving Object Detection by Multi-View Geometric Techniques from a Single Camera Mounted Robot ( IROS 2009)
Author: Abhijit Kundu, K Madhava Krishna and Jayanthi Sivaswamy

Abstract:
The ability to detect, and track multiple moving
objects like person and other robots, is an important prerequisite
for mobile robots working in dynamic indoor environments.
We approach this problem by detecting independently moving
objects in image sequence from a monocular camera mounted
on a robot. We use multi-view geometric constraints to classify
a pixel as moving or static. The first constraint, we use, is the
epipolar constraint which requires images of static points to
lie on the corresponding epipolar lines in subsequent images.
In the second constraint, we use the knowledge of the robot
motion to estimate a bound in the position of image pixel along
the epipolar line. This is capable of detecting moving objects
followed by a moving camera in the same direction, a so-called
degenerate configuration where the epipolar constraint fails.
To classify the moving pixels robustly, a Bayesian framework
is used to assign a probability that the pixel is stationary
or dynamic based on the above geometric properties and
the probabilities are updated when the pixels are tracked in
subsequent images. The same framework also accounts for the
error in estimation of camera motion. Successful and repeatable
detection and pursuit of people and other moving objects in
realtime with a monocular camera mounted on the Pioneer
3DX, in a cluttered environment confirms the efficacy of the
method.

Link

Lab Meeting January 10th, 2011(Jimmy) : Accurate Image Localization Based on Google Maps Street View (ECCV 2010)

2011-01-09T19:52:00.005+08:00

Title: Accurate Image Localization Based on Google Maps Street View
Authors: Amir Roshan Zamir, Mubarak Shah
In ECCV 2010

Abstract
Finding an image's exact GPS location is a challenging computer vision problem that has many real-world applications. In this paper, we address the problem of fi nding the GPS location of images with an accuracy which is comparable to hand-held GPS devices. We leverage a structured data set of about 100,000 images build from Google Maps Street View as the reference images. We propose a localization method in which the SIFT descriptors of the detected SIFT interest points in the reference images are indexed using a tree. In order to localize a query image, the tree is queried using the detected SIFT descriptors in the query image. A novel GPS-tag-based pruning method removes the less reliable descriptors. Then, a smoothing step with an associated voting scheme is utilized; this allows each query descriptor to vote for the location its nearest neighbor belongs to, in order to accurately localize the query image. A parameter called Confidence of Localization which is based on the Kurtosis of the distribution of votes is de fined to determine how reliable the localization of a particular image is. In addition, we propose a novel approach to localize groups of images accurately in a hierarchical manner. First, each image is localized individually; then, the rest of the images in the group are matched against images in the neighboring area of the found first match. The fi nal location is determined based on the Confidence of Localization parameter. The proposed image group localization method can deal with very unclear queries which are not capable of being geolocated individually.

[pdf]

Lab Meeting January 3rd, 2011(Will) : Neural Prothesis & Realtime Bayes Tracking

2011-01-03T12:24:00.000+08:00

Topic: Neural Prothesis & Realtime Bayes Tracking

Neural prothesis is a field that use brain to control motors to help disable people.
I'll report my survey on the neural prothesis decoding algorithm.

Po-Wei

Lab Meeting January 3rd, 2011(David) :Vision-Based Behavior Prediction in Urban Traffic Environments by Scene Categorization (BMVC 2010)

2010-12-26T22:40:00.001+08:00

Title: Vision-Based Behavior Prediction in Urban Traffic Environments by Scene Categorization (BMVC 2010)

Authors: Martin Heracles, Fernando Martinelli and Jannik Fritsch

Abstract:
We propose a method for vision-based scene understanding in urban traffic environments that predicts the appropriate behavior of a human driver in a given visual scene. The method relies on a decomposition of the visual scene into its constituent objects by image segmentation and uses segmentation-based features that represent both their identity and spatial properties. We show how the behavior prediction can be naturally formulated as scene categorization problem and how ground truth behavior data for learning a classifier can be automatically generated from any monocular video sequence recorded from a moving vehicle, using structure from motion techniques. We evaluate our method both quantitatively and qualitatively on the recently proposed CamVid dataset, predicting the appropriate velocity and yaw rate of the car as well as their appropriate change for both day and dusk sequences. In particular, we investigate the impact of the underlying segmentation and the number of behavior classes on the quality of these predictions

link

Lab Meeting December 27, 2010(Chih Chung) : Lozano-Perez. Belief space planning assuming maximum likelihood observations.(RSS 2010)

2010-12-22T13:51:00.002+08:00

Title:Belief space planning assuming maximum likelihood observations

Authors:Robert Platt Jr., Russ Tedrake, Leslie Kaelbling, Tomas Lozano-Perez

Abstract:
We cast the partially observable control problem as
a fully observable underactuated stochastic control problem in
belief space and apply standard planning and control techniques.
One of the difficulties of belief space planning is modeling the
stochastic dynamics resulting from unknown future observations.
The core of our proposal is to define deterministic beliefsystem
dynamics based on an assumption that the maximum
likelihood observation (calculated just prior to the observation)
is always obtained. The stochastic effects of future observations
are modelled as Gaussian noise. Given this model of the dynamics,
two planning and control methods are applied. In the first, linear
quadratic regulation (LQR) is applied to generate policies in the
belief space. This approach is shown to be optimal for linear-
Gaussian systems. In the second, a planner is used to find locally
optimal plans in the belief space. We propose a replanning
approach that is shown to converge to the belief space goal
in a finite number of replanning steps. These approaches are
characterized in the context of a simple nonlinear manipulation
problem where a planar robot simultaneously locates and grasps
an object.

link

Lab Meeting December 20, 2010(Chung-Han): progress report

2010-12-19T18:52:00.002+08:00

I will report my progress on ground-truth annotation.

Lab Meeting December 13, 2010(ShaoChen): DDF-SAM: Fully Distributed SLAM using Constrained Factor Graphs(IROS2010)

2010-12-12T23:09:00.001+08:00

Title: DDF-SAM: Fully Distributed SLAM using Constrained Factor Graphs

Authors: Alexander Cunningham, Manohar Paluri, and Frank Dellaert

Abstract:

We address the problem of multi-robot distributed SLAM with an extended Smoothing and Mapping (SAM) approach to implement Decentralized Data Fusion (DDF). We present DDF-SAM, a novel method for efﬁciently and robustly distributing map information across a team of robots, to achieve scalability in computational cost and in communication bandwidth and robustness to node failure and to changes in network topology. DDF-SAM consists of three modules: (1) a local optimization module to execute single-robot SAM and condense the local graph; (2) a communication module to collect and propagate condensed local graphs to other robots, and (3) a neighborhood graph optimizer module to combine local graphs into maps describing the neighborhood of a robot. We demonstrate scalability and robustness through a simulated example, in which inference is consistently faster than a comparable naive approach.

[link]

Lab Meeting December 6th, 2010(Nicole): Acoustic Source Localization and Tracking Using Track Before Detect

2010-12-06T11:22:00.001+08:00

Title: Acoustic Source Localization and Tracking Using Track Before Detect
(IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, 2010)

Authors: Maurice F. Fallon, Simon Godsill

Abstract:
Particle Filter-based Acoustic Source Localization algorithms attempt to track the position of a sound source—one or more people speaking in a room—based on the current data from a microphone array as well as all previous data up to that point. This paper first discusses some of the inherent behavioral traits of the steered beamformer localization function. Using conclusions drawn from that study, a multitarget methodology for acoustic source tracking based on the Track Before Detect (TBD) framework is introduced. The algorithm also implicitly evaluates source activity using a variable appended to the state vector. Using the TBD methodology avoids the need to identify a set of source measurements and also allows for a vast increase in the number of particles used for a comparitive computational load which results in increased tracking stability in challenging recording environments. An evaluation of tracking performance is given using a set of real speech recordings with two simultaneously active speech sources.

[link]

Lab Meeting December 6th, 2010(KuoHuei): progress report

2010-12-06T08:52:00.001+08:00

I will present my progress on Neighboring Objects Interaction models.

Lab Meeting November 29, 2010 (Wang Li): Adaptive Pose Priors for Pictorial Structures (CVPR 2010)

2010-11-28T23:12:00.000+08:00

Adaptive Pose Priors for Pictorial Structures

Benjamin Sapp
Chris Jordan
Ben Taskar

Abstract

The structure and parameterization of a pictorial structure model is often restricted by assuming tree dependency structure and unimodal, data-independent pairwise interactions, which fail to capture important patterns in the data. On the other hand, local methods such as kernel density estimation provide nonparametric flexibility but require large amounts of data to generalize well. We propose a simple semi-parametric approach that combines the tractability of pictorial structure inference with the flexibility of non-parametric methods by expressing a subset of model parameters as kernel regression estimates from a learned sparse set of exemplars. This yields query-specific, image-dependent pose priors. We develop an effective shape-based kernel for upper-body pose similarity and propose a leave-one-out loss function for learning a sparse subset of exemplars for kernel regression. We apply our techniques to two challenging datasets of human figure parsing and advance the state-of-the-art (from 80% to 86% on the Buffy dataset), while using only 15% of the training data as exemplars.

Paper Link

Lab Meeting November 29th, 2010 (Jeff): Sub-Meter Indoor Localization in Unmodified Environments with Inexpensive Sensors

2010-11-27T16:57:00.004+08:00

Title: Sub-Meter Indoor Localization in Unmodified Environments with Inexpensive Sensors

Authors: Morgan Quigley, David Stavens, Adam Coates, and Sebastian Thrun

Abstract:

The interpretation of uncertain sensor streams for localization is usually considered in the context of a robot. Increasingly, however, portable consumer electronic devices, such as smartphones, are equipped with sensors including WiFi radios, cameras, and inertial measurement units (IMUs). Many tasks typically associated with robots, such as localization, would be valuable to perform on such devices. In this paper, we present an approach for indoor localization exclusively using the low-cost sensors typically found on smartphones. Environment modification is not needed. We rigorously evaluate our method using ground truth acquired using a laser range scanner. Our evaluation includes overall accuracy and a comparison of the contribution of individual sensors. We find experimentally that fusion of multiple sensor modalities is necessary for optimal performance and demonstrate sub-meter localization accuracy.

Link:
IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS), October 2010
http://www-cs.stanford.edu/people/dstavens/iros10/quigley_etal_iros10.pdf
or
local_copy

Video:
http://www.cs.stanford.edu/people/dstavens/iros10/quigley_etal_iros10.mp4

Lab Meeting November 22, 2010 (Andi): Three-Dimensional Mapping with Time-of-Flight Cameras

2010-11-22T00:41:00.002+08:00

Title: Three-Dimensional Mapping with Time-of-Flight Cameras

Authors: Stefan May, David Droeschel, Dirk Holz, Stefan Fuchs, Ezio Malis, Andreas Nuechter and Joachim Hertzberg

Journal of Field Robotics 2009

Abstract: This article investigates the use of time-of-flight (ToF) cameras in mapping tasks for autonomous mobile robots, in particular in simultaneous localization and mapping (SLAM) tasks. Although ToF cameras are in principle an attractive type of sensor for threedimensional (3D) mapping owing to their high rate of frames of 3D data, two features make them difficult as mapping sensors, namely, their restricted field of view and influences on the quality of range measurements by high dynamics in object reflectivity; in addition, currently available models suffer from poor data quality in a number of aspects. The paper first summarizes calibration and filtering approaches for improving the accuracy, precision, and robustness of ToF cameras independent of their intended usage. Then, several ego motion estimation approaches are applied or adapted, respectively, in order to provide a performance benchmark for registering ToF camera data. As a part of this, an extension to the iterative closest point algorithm has been developed that increases the robustness under restricted field of view and under larger displacements. Using an indoor environment, the paper provides results from SLAM experiments using these approaches in comparison. It turns out that the application of ToF cameras is feasible to SLAM tasks, although this type of sensor has a complex error characteristic.

Link

Lab Meeting November 22, 2010 (Alan): Temporary Maps for Robust Localization in Semi-static Environments (IROS 2010)

2010-11-21T17:22:00.001+08:00

Title: Temporary Maps for Robust Localization in Semi-static Environments (IROS 2010)

Authors: Daniel Meyer-Delius, Jurgen Hess, Giorgio Grisetti, Wolfram Burgard

Abstract—Accurate and robust localization is essential for the successful navigation of autonomous mobile robots. The majority of existing localization approaches, however, is based on the assumption that the environment is static which does not hold for most practical application domains. In this paper, we present a localization framework that can robustly track a robot’s pose even in non-static environments. Our approach keeps track of the observations caused by unexpected objects in the environment using temporary local maps. It relies both on these temporary local maps and on a reference map of the environment for estimating the pose of the robot. Experimental results demonstrate that by exploiting the observations caused by unexpected objects our approach outperforms standard localization methods for static environments.

Link: pdf

Lab Meeting November 15( KuenHan ), 3D Reconstruction of a Moving Point from a Series of 2D Projections (ECCV 2010)

2010-11-15T00:22:00.002+08:00

Title :3D Reconstruction of a Moving Point from a Series of 2D Projections
Author: Hyun Soo Park, Takaaki Shiratori, Iain Matthews, and Yaser Sheikh

Abstract

This paper presents a linear solution for reconstructing the 3D trajectory of a moving point from its correspondence in a collection of 2D perspective images, given the 3D spatial pose and time of capture of the cameras that produced each image. Triangulation-based solutions do not apply, as multiple views of the point may not exist at each instant in time. A geometric analysis of the problem is presented and a criterion, called reconstructibility, is defined to precisely characterize the cases when reconstruction is possible, and how accurate it can be. We apply the linear reconstruction algorithm to reconstruct the time evolving 3D structure of several real-world scenes, given a collection of non-coincidental 2D images.

Link

Lab Meeting November 15, 2010 (fish60): Unfreezing the Robot: Navigation in Dense, Interacting Crowds

2010-11-14T18:35:00.002+08:00

Title: Unfreezing the Robot: Navigation in Dense, Interacting Crowds(IROS 2010)
Author: Peter Trautman and Andreas Krause

Abstract—In this paper, we study the safe navigation of a mobile robot through crowds of dynamic agents with uncertain trajectories. Existing algorithms suffer from the “freezing robot” problem: once the environment surpasses a certain level of complexity, the planner decides that all forward paths are unsafe, and the robot freezes in place (or performs unnecessary aneuvers) to avoid collisions. ... In this work, we demonstrate that both the individual prediction and the predictive uncertainty have little to do with the frozen robot problem. Our key insight is that dynamic agents solve the frozen robot problem by engaging in “joint collision avoidance”: They cooperatively make room to create feasible trajectories. We develop IGP, a nonparametric statistical model based on Dependent Output Gaussian Processes that can estimate crowd interaction from data. Our model naturally captures the non-Markov nature of agent trajectories, as well as their goal-driven navigation. We then show how planning in this model can be efficiently implemented using particle based inference.

Link

CMU PhD Thesis Defense: Geolocation with Range: Robustness, Efficiency and Scalability

2010-11-01T09:28:00.002+08:00

CMU RI PhD Thesis Defense
Joseph A. Djugash
Geolocation with Range: Robustness, Efficiency and Scalability
November 05, 2010, 10:00 a.m., NSH 1507

Abstract

This thesis explores the topic of geolocation with range. A robust method for localization and SLAM (Simultaneous Localization and Mapping) is proposed. This method uses a polar parameterization of the state to achieve accurate estimates of the nonlinear and multi-modal distributions in range-only systems. Several experimental evaluations on real robots reveal the reliability of this method.

Scaling such a system to large network of nodes, increases the computational load on the system due to the increased state vector. To alleviate this problem, we propose the use of a distributed estimation algorithm based on the belief propagation framework. This method distributes the estimation task, such that each node only estimates its local network, greatly reducing the computation performed by any individual node. However, the method does not provide any guarantees on the convergence of its solution in general graphs. Convergence is only guaranteed for non-cyclic graphs (ie. trees). Thus, an extension of this approach which reduces any arbitrary graph to a spanning tree is presented. This enables the proposed decentralized localization method to provide guarantees on its convergence.

Scaling in the traditional sense involves extensions to deal with growth in the size of the operating environment. In large, feature-less environments, maintaining a globally consistent estimate of a group of mobile agents is difficult. In this thesis, a novel multi-robot coordination strategy is proposed. Based on the observability analysis of the system, the propose controller achieves the tight coordination necessary to obtain an accurate global estimate. The proposed approach is demonstrated using both simulation and experimental testing with real robots.

[LINK][PDF]

Thesis Committee
Sanjiv Singh, Chair
George Kantor
Howie Choset
Wolfram Burgard, University of Freiburg

Lab Meeting November 1, 2010 (Will): Visual Event Recognition in Videos by Learning from Web Data (CVPR 2010)

2010-10-31T01:34:00.003+08:00

Title: Visual Event Recognition in Videos by Learning from Web Data (CVPR 2010)
Author: Lixin Duan, Dong Xu, Ivor W. Tsang, Jiebo Luo

Abstract:
We propose a visual event recognition framework for consumer domain videos by leveraging a large amount of loosely labeled web videos (e.g., from YouTube). First, we propose a new aligned space-time pyramid matching method to measure the distances between two video clips, where each video clip is divided into space-time volumes over multiple levels. We calculate the pairwise distances between any two volumes and further integrate the information from different volumes with Integer-ﬂow Earth Mover’s Distance (EMD) to explicitly align the volumes. Second, we propose a new cross-domain learning method in order to 1) fuse the information from multiple pyramid levels and features (i.e., space-time feature and static SIFT feature) and 2) cope with the considerable variation in feature dis- tributions between videos from two domains (i.e., web do- main and consumer domain). For each pyramid level and each type of local features, we train a set of SVM classiﬁers based on the combined training set from two domains using multiple base kernels of different kernel types and parameters, which are fused with equal weights to obtain an average classiﬁer. Finally, we propose a cross-domain learning method, referred to as Adaptive Multiple Kernel Learning (A-MKL), to learn an adapted classiﬁer based on multiple base kernels and the prelearned average classifiers by minimizing both the structural risk functional and the mismatch between data distributions from two domains. Extensive experiments demonstrate the effectiveness of our proposed framework that requires only a small number of labeled consumer videos by leveraging web data.

link: [http://vc.sce.ntu.edu.sg/index_files/a-mkl.pdf]

local link: [https://pal.csie.ntu.edu.tw/pub2/Conferences/2010_CVPR/data/papers/1358.pdf#page=1]

Lab meeting Nov. 01 2010, (Chih-Chung) POMDPs for robotic tasks with mixed observability (RSS 2009)

2010-10-29T13:10:00.004+08:00

Title:POMDPs for robotic tasks with mixed observability
Author:Sylvie C.W.Ong, Shao Wei Png, David Hsu and Wee Sun Lee.

Abstract:
Partially observable Markov decision processes
(POMDPs) provide a principled mathematical framework for
motion planning of autonomous robots in uncertain and dynamic
environments. They have been successfully applied to
various robotic tasks, but a major challenge is to scale up
POMDP algorithms for more complex robotic systems. Robotic
systems often have mixed observability: even when a robot’s
state is not fully observable, some components of the state
may still be fully observable. Exploiting this, we use a factored
model to represent separately the fully and partially observable
components of a robot’s state and derive a compact lowerdimensional
representation of its belief space. We then use this
factored representation in conjunction with a point-based algorithm
to compute approximate POMDP solutions. Separating
fully and partially observable state components using a factored
model opens up several opportunities to improve the efficiency
of point-based POMDP algorithms. Experiments show that on
standard test problems, our new algorithm is many times faster
than a leading point-based POMDP algorithm.

link

News: University of Chicago, Cornell Researchers Develop Universal Robotic Gripper

2010-10-28T08:14:00.005+08:00

Robotic hands are usually just that -- hands -- but some researchers from the University of Chicago and Cornell University (with a little help from iRobot) have taken a decidedly different approach for their so-called universal robotic gripper. As you can see above, the gripper is actually a balloon that can conform to and grip just about any small object, and hang onto it firmly enough to pick it up. What's the secret? After much testing, the researchers found that ground coffee was the best substance to fill the balloon with -- to grab an object, the gripper simply creates a vacuum in the balloon (much like a vacuum-sealed bag of coffee), and it's then able to let go of the object just by releasing the vacuum. Simple, but it works. Head on past the break to check it out in action. [via engadget]

Lab meeting Oct. 25 2010, (David) Threat-aware Path Planning in Uncertain Urban Environments (IROS 2010)

2010-10-25T11:27:00.002+08:00

Title: Threat-aware Path Planning in Uncertain Urban Environments

Authors: Georges S. Aoude, Brandon D. Luders, Daniel S. Levine, and Jonathan P. How

Abstract:
This paper considers the path planning problem
for an autonomous vehicle in an urban environment populated
with static obstacles and moving vehicles with uncertain intents.
We propose a novel threat assessment module, consisting of
an intention predictor and a threat assessor, which augments
the host vehicle’s path planner with a real-time threat value
representing the risks posed by the estimated intentions of
other vehicles. This new threat-aware planning approach is
applied to the CL-RRT path planning framework, used by the
MIT team in the 2007 DARPA Grand Challenge. The strengths
of this approach are demonstrated through simulation and
experiments performed in the RAVEN testbed facilities

[local copy]

[link ]

[local video]

[video]

Lab meeting Oct. 11 2010, (Shao-Chen) Consistent data association in multi-robot systems with limited communications(RSS 2010)

2010-10-11T12:02:00.000+08:00

Title: Consistent data association in multi-robot systems with limited communications

Authors: Rosario Aragues,Eduardo Montijano, and Carlos Sagues

Abstract:

In this paper we address the data association
problem of features observed by a robot team with limited communications.
At every time instant, each robot can only exchange
data with a subset of the robots, its neighbors. Initially, each
robot solves a local data association with each of its neighbors.
After that, the robots execute the proposed algorithm to agree
on a data association between all their local observations which
is globally consistent. One inconsistency appears when chains of
local associations give rise to two features from one robot being
associated among them. The contribution of this work is the
decentralized detection and resolution of these inconsistencies.
We provide a fully decentralized solution to the problem. This
solution does not rely on any particular communication topology.
Every robot plays the same role, making the system robust to
individual failures. Information is exchanged exclusively between
neighbors. In a finite number of iterations, the algorithm finishes
with a data association which is free of inconsistent associations.
In the experiments, we show the performance of the algorithm
under two scenarios. In the first one, we apply the resolution
and detection algorithm for a set of stochastic visual maps. In
the second, we solve the feature matching between a set of images
taken by a robotic team.

[link]

Lab meeting Oct. 11th 2010, (Nicole) Improvement in Listening Capability for Humanoid Robot HRP-2(ICRA 2010)

2010-10-11T08:58:00.002+08:00

Title: Improvement in Listening Capability for Humanoid Robot HRP-2 (ICRA2010)

Authors: Toru Takahashi, Kazuhiro Nakadai, Kazunori Komatani, Tetsuya Ogata and Hiroshi G. Okuno.

Abstract:

This paper describes improvement of sound source separation for a simultaneous automatic speech recognition (ASR) system of a humanoid robot. A recognition error in the system is caused by a separation error and interferences of other sources. In separability, an original geometric source separation (GSS) is improved. Our GSS uses a measured robot’s head related transfer function (HRTF) to estimate a separation matrix. As an original GSS uses a simulated HRTF calculated based on a distance between microphone and sound source, there is a large mismatch between the simulated and the measured transfer functions. The mismatch causes a severe degradation of recognition performance.

Faster convergence speed of separation matrix reduces separation error. Our approach gives a nearer initial separation matrix based on a measured transfer function from an optimal separation matrix than a simulated one. As a result, we expect that our GSS improves the convergence speed. Our GSS is also able to handle an adaptive step-size parameter.

These new features are added into open source robot audition software (OSS) called”HARK” which is newly updated as version 1.0.0. The HARK has been installed on a HRP-2 humanoid with an 8-element microphone array. The listening capability of HRP-2 is evaluated by recognizing a target speech signal which is separated from a simultaneous speech signal by three talkers. The word correct rate (WCR) of ASR improves by 5 points under normal acoustic environments and by 10 points under noisy environments. Experimental results show that HARK 1.0.0 improves the robustness against noises.

[link]

Lab meeting Oct. 11th 2010, (Andi) Dynamic 3D Scene Analysis for Acquiring Articulated Scene Models

2010-10-11T00:59:00.003+08:00

Dynamic 3D Scene Analysis for Acquiring Articulated Scene Models

ICRA 2010

Agnes Swadzba, Niklas Beuter, Sven Wachsmuth, and Franz Kummert

Abstract:

In this paper we present a new system for a mobile robot to generate an articulated scene model by analyzing complex dynamic 3D scenes. The system extracts essential knowledge about the foreground, like moving persons, and the background, which consists of all visible static scene parts. In contrast to other 3D reconstruction approaches, we suggest to additionally distinguish between static parts, like walls, and movable objects like chairs or doors. The discrimination supports the reconstruction process and additionally, delivers important information about interaction objects. Here, the movable object detection is realized object independent by analyzing changes in the scenery. Furthermore, in the proposed system the background scene is feedbacked to the tracking part yielding a much better tracking and detection result which improves again the 3D reconstruction. We show in our experiments that we are able to provide a sound background model and to extract simultaneously persons and object regions representing chairs, doors, and even smaller movable objects.

paper link

News: Google's Self-Driving Cars

2010-10-10T09:06:00.000+08:00

Google: We've Been Secretly Building And Testing Robot Cars That Drive Themselves
By Sebastian Thrun, Google
Read the full article.

Google Cars Drive Themselves, in Traffic
By John Markoff, The New York Times
Read the full article.

News: MIT Media Lab Medical Mirror

2010-10-08T13:14:00.003+08:00

MIT ~~Medical~~ Media Lab Mirror tells your pulse with a webcam

Mirror mirror on the wall, who has the highest arterial palpation of them all? If you went to MIT you might be able to answer that question thanks to the work of grad student Ming-Zher Poh, who has found a way to tell your pulse with just a simple webcam and some software. By looking at minute changes in the brightness of the face, the system can find the beating of your heart even at a low resolution, comparable to the results of a traditional FDA-approved pulse monitor. Right now the mirror above is just a proof of concept, but the idea is that the hospital beds or surgery rooms of tomorrow might be able to monitor a patient's pulse without requiring any wires or physical contact, encouraging news for anyone who has ever tried to sleep whilst wearing a heart monitor. [via Engadget]

Lab Meeting October 4th, 2010 (Jeff): Progress Report

2010-10-03T16:19:00.002+08:00

I will represent the progress in RFID SLAM with some indexing value to show the performance.

Lab Meeting October 4th, 2010(KuoHuei): progress report

2010-10-03T14:47:00.002+08:00

I will present the Neighboring Object Interacting Tracking, including modeling, learning, inference, and some results.

Lab Meeting September 27, 2010 (Wang Li): Monocular 3D Pose Estimation and Tracking by Detection (CVPR 2010)

2010-09-27T01:39:00.002+08:00

Monocular 3D Pose Estimation and Tracking by Detection

Mykhaylo Andriluka
Stefan Roth
Bernt Schiele

Abstract
Automatic recovery of 3D human pose from monocular
image sequences is a challenging and important research
topic with numerous applications. Although current methods
are able to recover 3D pose for a single person in controlled
environments, they are severely challenged by realworld
scenarios, such as crowded street scenes. To address
this problem, we propose a three-stage process building on
a number of recent advances. The first stage obtains an initial
estimate of the 2D articulation and viewpoint of the person
from single frames. The second stage allows early data
association across frames based on tracking-by-detection. The third and
final stage uses those tracklet-based estimates as robust image
observations to reliably recover 3D pose. We demonstrate
state-of-the-art performance on the HumanEva II
benchmark, and also show the applicability of our approach
to articulated 3D tracking in realistic street conditions.

Paper Link

Lab Meeting September 20, 2010 (Kuen-Han): Scale Drift-Aware Large Scale Monocular SLAM (RSS 2010)

2010-09-19T13:23:00.002+08:00

Title: Scale Drift-Aware Large Scale Monocular SLAM

Author: Hauke Strasdat, J.M.M. Montiel, Andrew J. Davison

Abstract—State of the art visual SLAM systems have recently
been presented which are capable of accurate, large-scale and
real-time performance, but most of these require stereo vision.
Important application areas in robotics and beyond open up
if similar performance can be demonstrated using monocular
vision, since a single camera will always be cheaper, more
compact and easier to calibrate than a multi-camera rig.
With high quality estimation, a single camera moving through
a static scene of course effectively provides its own stereo
geometry via frames distributed over time. However, a classic
issue with monocular visual SLAM is that due to the purely
projective nature of a single camera, motion estimates and map
structure can only be recovered up to scale. Without the known
inter-camera distance of a stereo rig to serve as an anchor, the
scale of locally constructed map portions and the corresponding
motion estimates is therefore liable to drift over time.
In this paper we describe a new near real-time visual SLAM
system which adopts the continuous keyframe optimisation approach
of the best current stereo systems, but accounts for
the additional challenges presented by monocular input. In
particular, we present a new pose-graph optimisation technique
which allows for the efficient correction of rotation, translation
and scale drift at loop closures. Especially, we describe the
Lie group of similarity transformations and its relation to the
corresponding Lie algebra. We also present in detail the system’s
new image processing front-end which is able accurately to track
hundreds of features per frame, and a filter-based approach
for feature initialisation within keyframe-based SLAM. Our
approach is proven via large-scale simulation and real-world
experiments where a camera completes large looped trajectories.

link

Lab Meeting September 20, 2010 (Alan): Probabilistic Surveillance with Multiple Active Cameras (ICRA 2010)

2010-09-19T00:55:00.002+08:00

Title: Probabilistic Surveillance with Multiple Active Cameras (ICRA 2010)

Authors: Eric Sommerlade and Ian Reid

Abstract:

In this work we present a consistent probabilistic approach to control multiple, but diverse pan-tilt-zoom cameras concertedly observing a scene. There are disparate goals to this control: the cameras are not only to react to objects moving about, arbitrating conflicting interests of target resolution and trajectory accuracy, they are also to anticipate the appearance of new targets.

We base our control function on maximisation of expected mutual information gain, which to our knowledge is novel to the field of computer vision in the context of multiple pan-tilt-zoom camera control. This information theoretic measure yields a utility for each goal and parameter setting, making the use of physical or computational resources comparable. Weighting this utility allows to prioritise certain objectives or targets in the control.

The resulting behaviours in typical situations for multicamera systems, such as camera hand-off, acquisition of closeups and scene exploration, are emergent but intuitive. We quantitatively show that without the need for hand crafted rules they address the given objectives.

Link

News: Quadrocopters from UPenn

2010-09-18T16:21:00.003+08:00

Quadrocopters can fly through thrown hoops, the end really is nigh (video) via Engadget

Lab Meeting September 13th, 2010(fish60): progress report

2010-09-13T00:19:00.001+08:00

I will briefly show what I have done these days with the review of LEARCH algorithm.

Lab Meeting September 13th, 2010(Gary): AAM based Face Tracking with Temporal Matching and Face Segmentation(CVPR 2010)

2010-09-11T14:53:00.002+08:00

Title:
AAM based Face Tracking with Temporal Matching and Face Segmentation

Authors:
Mingcai Zhou, Lin Liang, Jian Sun, Yangsheng Wang

Abstract:

Active Appearance Model (AAM) based face tracking has
advantages of accurate alignment, high efficiency, and
effectiveness for handling face deformation. However, AAM
suffers from the generalization problem and has difficulties
in images with cluttered backgrounds. In this paper, we in-
troduce two novel constraints into AAM fitting to address
the above problems. We first introduce a temporal matching
constraint in AAM fitting. In the proposed fitting scheme,
the temporal matching enforces an inter-frame local ap-
pearance constraint between frames. The resulting model
takes advantage of temporal matching's good generalizabil-
ity, but does not suffer from the mismatched points. To make
AAM more stable for cluttered backgrounds, we introduce a
color-based face segmentation as a soft constraint. Both
constraints effectively improve the AAM tracker's perfor-
mance, as demonstrated with experiments on various chal-
lenging real-world videos.

link

PhD Thesis Defense: David Silver [Learning Preference Models for Autonomous Mobile Robots in Complex Domains]

2010-09-08T14:55:00.002+08:00

PhD Thesis Defense: David Silver
Learning Preference Models for Autonomous Mobile Robots in Complex Domains
Carnegie Mellon University
September 13, 2010, 12:30 p.m., NSH 1507

Abstract
Achieving robust and reliable autonomous operation even in complex unstructured environments is a central goal of field robotics. ...
This thesis presents the development and application of machine learning techniques that automate the construction and tuning of preference models within complex mobile robotic systems. Utilizing the framework of inverse optimal control, expert examples of robot behavior can be used to construct models that generalize demonstrated preferences and reproduce similar behavior. Novel learning from demonstration approaches are developed that offer the possibility of significantly reducing the amount of human interaction necessary to tune a system, while also improving its final performance. Techniques to account for the inevitability of noisy and imperfect demonstration are presented, along with additional methods for improving the efficiency of expert demonstration and feedback.

The effectiveness of these approaches is confirmed through application to several real world domains, such as the interpretation of static and dynamic perceptual data in unstructured environments and the learning of human driving styles and maneuver preferences. ... These experiments validate the potential applicability of the developed algorithms to a large variety of future mobile robotic systems.

Link

Lab Meeting September 7th, 2010 (Jimmy): Learning to Recognize Objects from Unseen Modalities

2010-09-06T16:17:00.002+08:00

Title: Learning to Recognize Objects from Unseen Modalities
In ECCV2010

Authors: C. Mario Christoudias, Raquel Urtasun, Mathieu Salzmann and Trevor Darrell

Abstract
In this paper we investigate the problem of exploiting multiple sources of information for object recognition tasks when additional modalities that are not present in the labeled training set are available for inference. This scenario is common to many robotics sensing applications and is in contrast with the assumption made by existing approaches that require at least some labeled examples for each modality. To leverage the previously unseen features, we make use of the unlabeled data to learn a mapping from the existing modalities to the new ones. This allows us to predict the missing data for the labeled examples and exploit all modalities using multiple kernel learning. We demonstrate the e ectiveness of our approach on several multi-modal tasks including object recognition from multi-resolution imagery, grayscale and color images, as well as images and text. Our approach outperforms multiple kernel learning on the original modalities, as well as nearest-neighbor and bootstrapping schemes.

[pdf]

Lab Meeting September 7th, 2010 (Will(柏崴)): Efficient Computation of Robust Low-Rank Matrix Approximations in the Presence of Missing Data using the L1 Norm (CVPR2010)

2010-09-05T14:21:00.000+08:00

Title: Efficient Computation of Robust Low-Rank Matrix Approximations in the Presence of Missing Data using the L1 Norm

Authors: Anders Eriksson and Anton van den Hengel

Abstract:

The calculation of a low-rank approximation of a matrix is a fundamental operation in many computer vision applications. The workhorse of this class of problems has long been the Singular Value Decomposition. However, in the presence of missing data and outliers this method is not applicable, and unfortunately, this is often the case in practice.

In this paper we present a method for calculating the low-rank factorization of a matrix which minimizes the L1 norm in the presence of missing data. Our approach represents a generalization the Wiberg algorithm, one of the more convincing methods for factorization under the L2 norm. By utilizing the differentiability of linear programs, we can extend the underlying ideas behind this approach to include this class of L1 problems as well. We show that the proposed algorithm can be efficiently implemented using existing optimization software. We also provide preliminary experiments on synthetic as well as real world data with very convincing results.

[link]

[local link]

Lab Meeting August 31st, 2010 (zhi-zhong(執中)): Efficient Planning under Uncertainty for a Target-Tracking Micro-Aerial Vehicle (ICRA'10)

2010-08-28T00:21:00.003+08:00

Title: Efficient Planning under Uncertainty for a Target-Tracking Micro-Aerial Vehicle

Authors: Ruijie He, Abraham Bachrach and Nicholas Roy

Abstract:

A helicopter agent has to plan trajectories to track multiple ground targets from the air. The agent has partial information of each target’s pose, and must reason about its uncertainty of the targets’ poses when planning subsequent actions.

We present an online, forward-search algorithm for planning under uncertainty by representing the agent’s belief of each target’s pose as a multi-modal Gaussian belief. We exploit this parametric belief representation to directly compute the distribution of posterior beliefs after actions are taken. This analytic computation not only enables us to plan in problems with continuous observation spaces, but also allows the agent to search deeper by considering policies composed of multistep action sequences; deeper searches better enable the agent to keep the targets well-localized. We present experimental results in simulation, as well as demonstrate the algorithm on an actual quadrotor helicopter tracking multiple vehicles on a road network constructed indoors.

local copy : [link]

[link]

Lab Meeting August 31st, 2010 (David): Scene Understanding in a Large Dynamic Environment through a Laser-based Sensing (ICRA'10)

2010-08-28T00:10:00.003+08:00

Title:

Scene Understanding in a Large Dynamic Environment through a Laser-based Sensing

Authors:

Huijing Zhao, Yiming Liu, Xiaolong Zhu, Yipu Zhao, Hongbin Zha

Abstract:

It became a well known technology that a map of complex environment containing low-level geometric primitives (such as laser points) can be generated using a robot with laser scanners. This research is motivated by the need of obtaining semantic knowledge of a large urban outdoor environment after the robot explores and generates a low-level sensing data set. An algorithm is developed with the data represented in a range image, while each pixel can be converted into a 3D coordinate. Using an existing segmentation method that models only geometric homogeneities, the data of a single object of complex geometry, such as people, cars, trees etc., is partitioned into different segments. Such a segmentation result will greatly restrict the capability of object recognition. This research proposes a framework of simultaneous segmentation and classification of range image, where the classification of each segment is conducted based on its geometric properties, and homogeneity of each segment is evaluated conditioned on each object class. Experiments are presented using the data of a large dynamic urban outdoor environment, and performance of the algorithm is evaluated.

local copy : [link]

[link]

Lab Meeting August 23rd, 2010 (Nicole): Evaluating Real-time Audio Localization Algorithms for Artificial Audition in Robotics (IROS'09)

2010-08-23T18:31:00.003+08:00

Title: Evaluating Real-time Audio Localization Algorithms for Artificial Audition in Robotics

Authors: Anthony Badali,Jean-Marc Valin,Francois Michaud,and Parham Aarabi

Abstract:
Although research on localization of sound sources using microphone arrays has been carried out for years, providing such capabilities on robots is rather new. Artificial audition systems on robots currently exist, but no evaluation of the methods used to localize sound sources has yet been conducted. This paper presents an evaluation of various real-time audio localization algorithms using a medium-sized micro-phone array which is suitable for applications in robotics. Thetechniques studied here are implementations and enhancements of steered response power - phase transform beamformers, which represent the most popular methods for time difference of arrival audio localization. In addition, two different grid topologies for implementing source direction search are also compared. Results show that a direction refinement procedure can be used to improve localization accuracy and that more efficient and accurate direction searches can be performed using a uniform triangular element grid rather than the typical rectangular element grid.

local copy : [link]
[link]

Lab Meeting August 23rd, 2010 (ShaoChen): Distributed Nonlinear Estimation for Robot Localization using Weighted Consensus (ICRA'10)

2010-08-23T14:12:00.000+08:00

Title: Distributed Nonlinear Estimation for Robot Localization using Weighted Consensus

Authors: Andrea Simonetto, Tam´as Keviczky and Robert Babuˇska

Abstract:

Distributed linear estimation theory has received increased
attention in recent years due to several promising
industrial applications. Distributed nonlinear estimation, however
is still a relatively unexplored ﬁeld despite the need in
numerous practical situations for techniques that can handle
nonlinearities. This paper presents a uniﬁed way of describing
distributed implementations of three commonly used nonlinear
estimators: the Extended Kalman Filter, the Unscented Kalman
Filter and the Particle Filter. Leveraging on the presented
framework, we propose new distributed versions of these
methods, in which the nonlinearities are locally managed by
the various sensors whereas the different estimates are merged
based on a weighted average consensus process. The proposed
versions are shown to outperform the few published ones in
two robot localization test cases.

[link]

Lab Meeting August 10th, 2010 (KuoHuel): An Online Approach: Learning-Semantic-Scene-by-Tracking and Tracking-by-Learning-Semantic-Scene (CVPR'10)

2010-08-10T09:00:00.004+08:00

Title: An Online Approach: Learning-Semantic-Scene-by-Tracking and

Tracking-by-Learning-Semantic-Scene

Authors: Xuan Song, Xiaowei Shao, Huijing Zhao, Jinshi Cui, Ryosuke Shibasaki and Hongbin Zha

Abstract:

Learning the knowledge of scene structure and tracking

a large number of targets are both active topics of computer

vision in recent years, which plays a crucial role in surveil-

lance, activity analysis, object classification and etc. In

this paper, we propose a novel system which simultaneously

performs the Learning-Semantic-Scene and Tracking, and

makes them supplement each other in one framework. The

trajectories obtained by the tracking are utilized to continu-

ally learn and update the scene knowledge via an online un-

supervised learning. On the other hand, the learned knowl-

edge of scene in turn is utilized to supervise and improve

the tracking results. Therefore, this “adaptive learning-

tracking loop” can not only perform the robust tracking in

high density crowd scene, dynamically update the knowl-

edge of scene structure and output semantic words, but also

ensures that the entire process is completely automatic and

online. We successfully applied the proposed system into the

JR subway station of Tokyo, which can dynamically obtain

the semantic scene structure and robustly track more than

150 targets at the same time.

[pdf]

Lab Meeting August 10th, 2010 (Jeff): FAB-MAP + RatSLAM: Appearance-based SLAM for Multiple Times of Day

2010-08-09T16:50:00.005+08:00

Title: FAB-MAP + RatSLAM: Appearance-based SLAM for Multiple Times of Day

Authors: Arren J. Glover, William P. Maddern, Michael J. Milford, and Gordon F. Wyeth

Abstract:

Appearance-based mapping and localisation is especially challenging when separate processes of mapping and localisation occur at different times of day. The problem is exacerbated in the outdoors where continuous change in sun angle can drastically affect the appearance of a scene. We confront this challenge by fusing the probabilistic local feature based data association method of FAB-MAP with the pose cell filtering and experience mapping of RatSLAM. We evaluate the effectiveness of our amalgamation of methods using five datasets captured throughout the day from a single camera driven through a network of suburban streets. We show further results when the streets are re-visited three weeks later, and draw conclusions on the value of the system for lifelong mapping.

Link:
IEEE International Conference on Robotics and Automation(ICRA), May 2010
http://eprints.qut.edu.au/31569/1/c31569.pdf
or
local_copy

CVPR 2010 Awards

2010-08-04T22:58:00.002+08:00

This post is to provide links to the best paper awards in CVPR 2010.

Best Student Paper

Visual Event Recognition in Videos by Learning from Web Data: Lixin Duan, Dong Xu, Ivor Wai-Hung Tsang, and Jiebo Luo

Best Paper Honorable Mention

Modeling Mutual Context of Object and Human Pose in Human-Object Interaction Activities: Bangpeng Yao and Li Fei-Fei

Best Paper

Efficient Computation of Robust Low-Rank Matrix Approximations in the Presence of Missing Data using the L1 Norm: Anders Eriksson and Anton van den Hengel

Longuet-Higgins Prize

Efficient Matching of Pictorial Structures: Pedro F. Felzenszwalb and Daniel P. Huttenlocher
Real-Time Tracking of Non-Rigid Objects Using Mean Shift: Dorin Comaniciu, Visvanathan Ramesh, and Peter Meer

Lab Meeting August 3rd, 2010 (Wang Li): Modeling Mutual Context of Object and Human Pose in Human-Object Interaction Activities (CVPR 2010)

2010-08-02T20:04:00.002+08:00

Modeling Mutual Context of Object and Human Pose in Human-Object Interaction Activities

Bangpeng Yao
Li Fei-Fei

Abstract
Detecting objects in cluttered scenes and estimating articulated human body parts are two challenging problems in computer vision. We observe, however, that objects and human poses can serve as mutual context to each other – recognizing one facilitates the recognition of the other.
In this paper, we propose a new random field model to encode the mutual context of objects and human poses in human-object interaction activities. We then cast the model learning task as a structure learning problem, of which the structural connectivity between the object, the overall human pose and different body parts are estimated through a structure search approach, and the parameters of the model are estimated by a new max-margin algorithm.
On a sports data set of six classes of human-object interactions, we show that our mutual context model significantly outperforms state-of-the-art in detecting very difficult objects and human poses.

Paper Link

Lab Meeting 8 / 3, 2010 (Alan) - Mapping Indoor Environments Based on Human Activity (ICRA 2010)

2010-07-29T13:55:00.002+08:00

Title: Mapping Indoor Environments Based on Human Activity (ICRA 2010)

Authors: Slawomir Grzonka, Frederic Dijoux, Andreas Karwath, Wolfram Burgard

Abstract

We present a novel approach to build approximate maps of structured environments utilizing human motion and activity. Our approach uses data recorded with a data suit which is equipped with several IMUs to detect movements of a person and door opening and closing events. In our approach we interpret the movements as motion constraints and door handling events as landmark detections in a graph-based SLAM framework. As we cannot distinguish between individual doors, we employ a multi-hypothesis approach on top of the SLAM system to deal with the high data-association uncertainty. As a result, our approach is able to accurately and robustly recover the trajectory of the person. We additionally take advantage of the fact that people traverse free space and that doors separate rooms to recover the geometric structure of the environment after the graph optimization. We evaluate our approach in several experiments carried out with different users and in environments of different types.

Link: pdf

Lab Meeting 07/27, 2010(Kuen-Han) Non-Rigid Structure from Locally-Rigid Motion (CVPR,2010)

2010-07-26T16:48:00.002+08:00

Title: Non-Rigid Structure from Locally-Rigid Motion
Authors: Jonathan Taylor Allan D. Jepson Kiriakos N. Kutulakos

Abstract:
We introduce locally-rigid motion, a general framework for
solving the M-point, N-view structure-from-motion problem
for unknown bodies deforming under orthography. The
key idea is to first solve many local 3-point, N-view rigid
problems independently, providing a “soup” of specific,
plausibly rigid, 3D triangles. The main advantage here is
that the extraction of 3D triangles requires only very weak
assumptions: (1) deformations can be locally approximated
by near-rigid motion of three points (i.e., stretching not
dominant) and (2) local motions involve some generic rotation
in depth. Triangles from this soup are then grouped
into bodies, and their depth flips and instantaneous relative
depths are determined. Results on several sequences,
both our own and from related work, suggest these conditions
apply in diverse settings—including very challenging
ones (e.g., multiple deforming bodies). Our starting point
is a novel linear solution to 3-point structure from motion,
a problem for which no general algorithms currently exist.

paper

Lab Meeting July 20, 2010 (fish60): What if the Irresponsible Teachers Are Dominating? A Method of Training on Samples and Clustering on Teachers

2010-07-24T15:57:00.001+08:00

Sorry for the previous blank post.
Here's the content:

Title:
What if the Irresponsible Teachers Are Dominating? A Method of Training on Samples and Clustering on Teachers

Authors:
Shuo Chen, Jianwen Zhang, Guangyun Chen, Changshui Zhang
State Key Laboratory on Intelligent Technology and Systems
Tsinghua National Laboratory for Information Science and Technology (TNList)
Department of Automation, Tsinghua University, Beijing 100084, China

Abstract:
Learning from multiple teachers or sources
has received more attention of the researchers in the machine
learning area. In this setting, the learning system is dealing
with samples and labels provided by multiple teachers, who
in common cases, are non-expert. Their labeling styles and
behaviors are usually diverse, some of which are even detrimental
to the learning system. Thus, simply putting them
together and utilizing the algorithms designed for singleteacher
scenario would be not only improper, but also damaging.
Our work focuses on a case where the teachers are composed of good
ones and irresponsible ones. By irresponsible, we mean the
teacher who takes the labeling task not seriously and label
the sample at random without inspecting the sample itself.
If we do not take out their effects, our learning system would be ruined with no
doubt. In this paper, we propose a method for picking out the
good teachers with promising experimental results. It works
even when the irresponsible teachers are dominating in numbers.

Link

江山代有才人出攻讀博士—不輕言放棄

2010-07-21T09:12:00.000+08:00

Author: 王榮騰臺大客座教授

即使興趣不完全符合指導教授之研究領域，應考慮與其適時溝通、更換原指定之研究項目，甚至要求同時加入另一教授研究團隊，仍繼續跟定指導教授，不輕言放棄，或才是良策。

A生曾榮獲美國極頂尖大學某一指導教授〈Advisor〉給予的全額研究助理獎學金〈RA，Research Assistantship〉，一年半後，A生放棄學業，正在覓職中！
B生曾榮獲美國另一極頂尖大學給予的一年期全額研究生獎學金〈Graduate Fellowship〉，一年後及時拿到RA，卻一直認為研究與現實脫節，擔心未來就業機會而深感困擾！

這兩位高材生皆因成績優異，才能獲美國極頂尖大學給予的全額獎學金，卻皆因興趣不全在指導教授研究領域，而心蒙去念或煩惱不已！

其實，人生不如意十有八九，人生本就不完美，古今中外皆然。即使未來就職、自己創業或擔任教授，又怎能保證事事如意。已是天之驕子，若不能克服眼前困難，輕言放棄，今後何以立足於社會？因此，提供一些建議，學習如何解決問題，創造師生雙贏局面。

續跟指導教授
------------更換研究項目

指導教授常會同時進行數個研究項目，可當面請教並說明原因，是否能更換原指定之研究題目。若非不合理，教授多半都會接受。須知，博士論文〈PhD Dissertation〉大多由幾個研究專題組合而成。因此，最好是在文章被期刊或會議接受後提出；一來，可對目前該專題有所交代〈不至於浪費教授研究經費〉，二來也有助於自己博士論文的進展。再者，亦可利用這段時間對新研究項目有所了解。

假如研究題目太難以致研究上難已突破並發表文章時，更須謹言並舉例佐證，以免教授懷疑你的能力，對你失去信心。如純粹是興趣不足，最好是在老師指定題目數週內及時告知，以免留下不好印象。無論如何，必須同時向教授提出你的興趣所在，並告知你的背景及觀點，以便讓他對你能在此新研究專題上有所突破更具信心。

另加指導教授
-------------研究跨領域項目

對跨領域的新研究專題，可請求老師，是否能同時加入另一共同指導教授〈Co-Advisor〉的研究團隊。這項權宜之計，由於兩位教授可平均分擔研究經費，卻同時受惠於你未來的研究成果，因此指導教授通常會樂於接受。

換言之，不管個人興趣是否與指導教授研究領域相近，繼續跟定指導教授，不輕言放棄；且莫在博士資格考試〈PhD Qualifying Examination〉未通過前提出，以免造成輟學的嚴重後果。

經過長期溝通，最後A生接受指導教授建議，先休學、工作一段時間，再考慮是否繼續完成博士學位。B生則同時加入另一教授之研究團隊，不排除於畢業後往學術界發展；其後續已不再為研究課題而煩惱，並已在新覓研究領域之尖端會議中發表論文。由於處理得宜，目前這兩位高材生仍與原來指導教授保持良好關係。畢竟，恩師難覓，必須知福惜福；師生情難建，值得一生珍惜！〈王榮騰臺大電機系與電子工程研究所客座教授;2010年6月6日〉

Lab Meeting July 20, 2010 (Gary): Robust Unified Stereo-Based 3D Head Tracking and Its Application to Face Recognition (ICRA2010)

2010-07-18T21:08:00.002+08:00

Title:
Robust Unified Stereo-Based 3D Head Tracking and Its Application
to Face Recognition

Authors: Kwang Ho An and Myung Jin Chung

Abstract:
This paper investigates the estimation of 3D head poses and its identity authentication with a partial ellipsoid model. To cope with large out-of-plane rotations and translation in-depth, we extend conventional head tracking with a single camera to a stereo-based framework. To achieve more robust motion estimation even under time-varying lighting conditions, we incorporate illumination correction into the aforementioned framework. We approximate the face image variations due to illumination changes as a linear combination of illumination bases. Also,��by computing the illumination bases online from the registered face images, after estimating the 3D head poses, user-specific illumination bases can be obtained, and therefore illumination-robust tracking without a prior learning process can be possible. Furthermore, our unified stereo-based tracking is approximated as a linear least-squares problem; a closed-form solution is then provided. After recovering the full-motions of the head, we can register face images with pose variations into stabilized-view images, which are suitable for pose-robust face recognition. To verify the feasibility and applicability of our approach, we performed extensive experiments with three sets of challenging image sequences.

link

Lab Meeting July 20, 2010 (Jimmy): Group-Sensitive Multiple Kernel Learning for Object Categorization

2010-07-15T16:30:00.002+08:00

Title: Group-Sensitive Multiple Kernel Learning for Object Categorization
Authors: Jingjing Yang, Yuanning Li, Yonghong Tian, Lingyu Duan, Wen Gao
In: ICCV 2009

Abstract
In this paper, we propose a group-sensitive multiple kernel learning (GS-MKL) method to accommodate the intra-class diversity and the inter-class correlation for object categorization. By introducing an intermediate representation “group” between images and object categories, GS-MKL attempts to find appropriate kernel combination for each group to get a finer depiction of object categories. For each category, images within a group share a set of kernel weights while images from different groups may employ distinct sets of kernel weights. In GS-MKL, such group-sensitive kernel combinations together with the multi-kernels based classifier are optimized in a joint manner to seek a trade-off between capturing the diversity and keeping the invariance for each category. Extensive experiments show that our proposed GS-MKL method has achieved encouraging performance over three challenging datasets.

[pdf]

Lab Meeting July 13, 2010(ShaoChen):Rao-Blackwellized Particle Filters Multi Robot SLAM with Unknown Initial Correspondences and Limited Communication(ICRA 2010)

2010-07-12T18:22:00.000+08:00

Title: Rao-Blackwellized Particle Filters Multi Robot SLAM with Unknown Initial Correspondences and Limited Communication

Authors: Luca Carlone, Miguel Kaouk Ng, Jingjing Du, Basilio Bona, and Marina Indri

Abstract:

Multi robot systems are envisioned to play an important role in many robotic applications. A main prerequisite for a team deployed in a wide unknown area is the capability of autonomously navigate, exploiting the information acquired through the on-line estimation of both robot poses
and surrounding environment model, according to Simultaneous Localization And Mapping (SLAM) framework. As team coordination is improved, distributed techniques for filtering
are required in order to enhance autonomous exploration and large scale SLAM increasing both efficiency and robustness of operation. Although Rao-Blackwellized Particle Filters (RBPF) have been demonstrated to be an effective solution to the problem of single robot SLAM, few extensions to teams of robots exist, and these approaches are characterized by strict assumptions on both communication bandwidth and prior knowledge on relative poses of the teammates. In the present paper we address the problem of multi robot SLAM in the case of limited communication and unknown relative initial poses. Starting from the well established single robot RBPFSLAM, we propose a simple technique which jointly estimates SLAM posterior of the robots by fusing the prioceptive and the eteroceptive information acquired by each teammate. The approach intrinsically reduces the amount of data to be exchanged among the robots, while taking into account the uncertainty in relative pose measurements. Moreover it can be naturally extended to different communication technologies (bluetooth, RFId, wifi, etc.) regardless their sensing range. The proposed approach is validated through experimental test.

[link]

Lab Meeting July 13,2010(Nicole):Mutual Localization in a Team of Autonomous Robots using Acoustic Robot Detection

2010-07-12T09:32:00.004+08:00

Title: Mutual Localization in a Team of Autonomous Robots using Acoustic Robot Detection

Authors: David Becker and Max Risler

In RoboCup 2008: Robot Soccer World Cup XII ,Volume 5399/2009

Abstract
In order to improve self-localization accuracy we are exploring ways of mutual localization in a team of autonomous robots. Detecting team mates visually usually leads to inaccurate bearings and only rough distance estimates. Also, visually identifying teammates is not possible. Therefore we are investigating methods of gaining relative position information acoustically in a team of robots.
The technique introduced in this paper is a variant of code-multiplexed communication (CDMA, code division multiple access). In a CDMA system, several receivers and senders can communicate at the same time, using the same carrier frequency. Well-known examples of CDMA systems include wireless computer networks and the Global Positioning System, GPS. While these systems use electro-magnetic waves, we will try to adopt the CDMA principle towards using acoustic pattern recognition, enabling robots to calculate distances and bearings to each other.
First, we explain the general idea of cross-correlation functions and appropriate signal pattern generation. We will further explain the importance of synchronized clocks and discuss the problems arising from clock drifts.
Finally, we describe an implementation using the Aibo ERS-7 as platform and briefly state basic results, including measurement accuracy and a runtime estimate. We will briefly discuss acoustic localization in the specific scenario of a RoboCup soccer game.

[link]

Lab Meeting July 6th (Casey): Live Dense Reconstruction with a Single Moving Camera (CVPR 2010)

2010-07-06T09:04:00.003+08:00

Authors: Richard A. Newcombe and Andrew J. Davison

Abstract:

We present a method which enables rapid and dense reconstruction of scenes browsed by a single live camera. We take point-based real-time structure from motion (SFM) as our starting point, generating accurate 3D camera pose estimates and a sparse point cloud. Our main novel contribution is to use an approximate but smooth base mesh generated from the SFM to predict the view at a bundle of poses around automatically selected reference frames spanning the scene, and then warp the base mesh into highly accurate depth maps based on view-predictive optical flow and a constrained scene flow update. The quality of the resulting depth maps means that a convincing global scene model can be obtained simply by placing them side by side and removing overlapping regions. We show that a cluttered indoor environment can be reconstructed from a live hand-held camera in a few seconds, with all processing performed by current desktop hardware. Real-time monocular dense reconstruction opens up many application areas, and we demonstrate both real-time novel view synthesis and advanced augmented reality where augmentations interact physically with the 3D scene and are correctly clipped by occlusions.

[Paper]