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

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.

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

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.

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

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)

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)

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)

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

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)

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

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.

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

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

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)

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

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

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

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

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)

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)

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)

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

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