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

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)

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

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

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

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)

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]

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

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)

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)

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)

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)

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)

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)

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

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

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)

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)

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)

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

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

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)

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

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)

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

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)

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]

Lab Meeting July 6th 2010 (Andi): Upsampling Range Data in Dynamic Environments (CVPR 2010 )

Authors

Jennifer Dolson, Jongmin Baek, Christian Plagemann and Sebastian Thrun (Stanford University)

Abstract

We present a flexible method for fusing information from optical and range sensors based on an accelerated high-dimensional filtering approach. Our system takes as input a sequence of monocular camera images as well as a stream of sparse range measurements as obtained from a laser or other sensor system. In contrast with existing approaches, we do not assume that the depth and color data streams have the same data rates or that the observed scene is fully static. Our method produces a dense, high-resolution depth map of the scene, automatically generating confidence values for every interpolated depth point. We describe how to integrate priors on object shape, motion and appearance and how to achieve an efficient implementation using parallel processing hardware such as GPUs.

paper

Lab Meeting June 29th, 2010 (KuoHuel): People Tracking with Human Motion Predictions from Social Forces (ICRA'10)

Title: People Tracking with Human Motion Predictions from Social Forces

Authors: Matthias Luber, Johannes A. Stork, Gian Diego Tipaldi, and Kai O. Arras

Abstract:

For many tasks in populated environments, robots need to keep track of present and future motion states of people. Most approaches to people tracking make weak assumptions on human motion such as constant velocity and direction. But even over a short period, human motion behavior is more complex and influenced by factors such as an intended goal, other people, objects in the environment, or social rules. Therefore, more sophisticated motion models are highly desirable especially since people frequently undergo lengthy occlusion events.

For the study of crowd behavior or evacuation dynamics, computational models that describe individual and collective pedestrian dynamics have been developed in e.g. the social psychology community. In this paper, we make use of such a model for the purpose of people tracking. Concretely, we integrate a pedestrian dynamics model based on social forces into a multi-hypothesis target tracker. We show how the re ned motion predictions translate into more informed probability distributions over hypotheses and nally into a more robust tracking behavior and better occlusion handling. In experiments in indoor and outdoor environments with data from a laser range nder, the social force model leads to more accurate tracking with up to two times fewer data association errors.

[link]

Lab Meeting June 29th, 2010 (Jeff): Fully Autonomous Trajectory Estimation with Long-Range Passive RFID

Title: Fully Autonomous Trajectory Estimation with Long-Range Passive RFID

Authors: Philipp Vorst and Andreas Zell

Abstract:

We present a novel approach which enables a mobile robot to estimate its trajectory in an unknown environment with long-range passive radio-frequency identi cation
(RFID). The estimation is based only on odometry and RFID measurements. The technique requires no prior observation model and makes no assumptions on the RFID setup. In
particular, it is adaptive to the power level, the way the RFID antennas are mounted on the robot, and environmental characteristics, which have major impact on long-range RFID
measurements. Tag positions need not be known in advance, and only the arbitrary, given infrastructure of RFID tags in the environment is utilized. By a series of experiments with a
mobile robot, we show that trajectory estimation is achieved accurately and robustly.

Link:
IEEE International Conference on Robotics and Automation(ICRA), May 2010
http://www.ra.cs.uni-tuebingen.de/publikationen/2010/vorst2010icra.pdf

Lab Meeting June 8th, 2010 (Wang Li): Pictorial Structures for Object Recognition

Pictorial Structures for Object Recognition

Pedro F. Felzenszwalb
Daniel P. Huttenlocher
IJCV 61(1), 2005

Abstract
In this paper we present a computationally efficient framework for part-based modeling and recognition of objects, motivated by the pictorial structure models introduced by Fischler and Elschlager. We address the problem of using pictorial structure models to find instances of an object in an image as well as the problem of learning an object model from training examples, presenting efficient algorithms in both cases. We demonstrate the techniques
by learning models that represent faces and human bodies and using the resulting
models to locate the corresponding objects in novel images.

Paper Link

MSI Robot Vacuum

CMU PhD Thesis Defense: Search in the Physical World

Search in the Physical World

Geoffrey A. Hollinger
Carnegie Mellon University

June 01, 2010, 10:00 a.m., NSH 3305
Abstract
This thesis examines search in the physical world, which differs significantly from the searches in the digital world that we perform every day on our computers. When searching the internet, for instance, success is a matter of informed indexing that allows the information to be retrieved quickly. In these cases, there is no consideration of the physical nature of the world, and the search is not cognizant of space, time, or traversal distance. In contrast, search in the physical world must consider a target that could be continuously moving, possibly even trying to evade being found. The environment may be partially known, and the search proceeds with information gathered during the search itself. In many cases, such as guaranteeing capture of an adversarial target, the problem cannot be solved with a single searcher, and all group members must coordinate their actions with others on the team. Prior work has explored limited instances of such problems, but existing techniques either scale poorly or do not have performance guarantees.

Two of the main variations of search in the physical world are considered: efficient search and guaranteed search. During efficient search, robots move to optimize the average-case performance of the search given a model of the target’s motion. During guaranteed search, robots coordinate to provide worst-case guarantees on search time if the target is adversarial. This thesis unifies these search problems and shows them to be NP-hard, which suggests that a scalable and optimal algorithm is unlikely. Despite these hardness results, algorithms using implicit coordination can provide scalable and high-performing approximate solutions to many real-world search problems. Implicit coordination arises when robots share their locations, measurements, and/or actions to improve the plans of their teammates. In accord with this design strategy, a linearly scalable efficient search algorithm is presented that utilizes implicit coordination to achieve bounded performance. In addition, this thesis contributes a novel approach that augments the coordination with a pre-search spanning tree generation step, which leads to an anytime algorithm for guaranteed search.

With a focus on decentralized and online operation, the proposed search algorithms are extended to take into account team constraints, limited communication, and partially known environments. The techniques are illustrated using a scenario in the literature that incorporates both efficient and guaranteed search, and they are validated both in simulation and on human-robot search teams operating in the physical world. The developed framework enables teams of autonomous agents to search environments outside the scope of previous techniques, and the analysis provides insight into the complexity of multi-robot coordination problems.

[Thesis PDF]

Thesis Committee
Sanjiv Singh, Chair
Geoff Gordon
Reid Simmons
Athanasios Kehagias, Aristotle University of Thessaloniki

Department machine learning talks: Interactively Building Mashups by Demonstration

Title: Interactively Building Mashups by Demonstration
Speaker: Dr. Craig A. Knoblock, University of Southern California
Time: 10:30am, May 25 (Tue), 2010
Place: Room 210, CSIE Building

Abstract:

There are a number of tools and services available now for building mashups on the Web. However, many of the tools for constructing mashups reply on a widget paradigm, where users must select, customize, and connect widgets to build the desired application. While this approach does not require programming, the users must still understand programming concepts to successfully create a mashup. In this talk I describe our programming-by-demonstration approach to building mashups by example. Instead of requiring a user to select and customize a set of widgets, the user simply demonstrates the integration task by example. I will describe how this approach addresses the problems of extracting data from various sources, cleaning and modeling the extracted data, integrating the data across sources, and visualizing the integrated results in a geospatial context. We implemented these ideas in a system called Karma and evaluated Karma on a set of 20 users and showed that compared to other mashup construction tools, Karma allowed more of the users to successfully build mashups and made it possible to build these mashups significantly faster compared to using a widget-based approach.

This research is joint work with Shubham Gupta, Pedro Szekely, and Rattapoom Tuchinda.

Short Biography:

Dr. Craig Knoblock is a Research Professor in Computer Science and a Senior Project Leader in the Information Sciences Institute at the University of Southern California (USC). He received both his M.S. and Ph.D. in Computer Science from Carnegie Mellon and his B.S. from Syracuse University. His current research interests include information integration, information extraction, machine learning, users interfaces, constraint reasoning, geospatial data fusion, and bioinformatics. He has published one book and over 200 articles, book chapters, and conference papers on his research. He has served on the Senior Program Committees of the National Artificial Intelligence Conference, the International Joint Conference on AI, the International Semantic Web Conference, and the International Conference on Intelligent User Interfaces. He was program co-chair for the 2008 AAAI track on AI and the Web and he is conference chair for the 2011 International Joint Conference on AI (IJCAI). He is on the editorial board of Artificial Intelligence, AAAI Press, Computational Intelligence, and the Journal on Foundations and Trends in Web Science. He is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), a Distinguished Scientist of the Association of Computing Machinery (ACM), a Trustee of the International Joint Conference on Artificial Intelligence (IJCAI), and past President of the International Conference on Automated Planning and Scheduling (ICAPS). He has started two companies, Fetch Technologies and Geosemble Technologies, based on his research at USC.

Department machine learning talks: Transfer Learning with Applications

Title: Transfer Learning with Applications
Speaker: Prof. Qiang Yang, Hong Kong University of Science and Technology
Time: 11:15am, May 25 (Tue), 2010
Place: Room 210, CSIE Building

Abstract:

Transfer learning is a new machine learning and data mining framework that allows the training and test data to come from different distributions or feature spaces. We can find many novel applications of machine learning and data mining where transfer learning is necessary. In this talk, I will give an introduction to transfer learning and then highlight some important applications such as text and image classification, sensor network data mining and activity recognition, collaborative filtering and bioinformatics. I will also discuss some potential future directions of transfer learning.

Short Biography:

Qiang Yang is a professor of the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology. He is also an adjunct professor at Peking University, Beijing, and at Zhongshan University in Guangzhou, China. He received his PhD degree from the University of Maryland, College Park. His research interests include AI planning and sensor-based activity recognition, machine learning and case-based reasoning, and data mining. He is a senior member of the IEEE, the AAAI, and the ACM, and an associate editor for the IEEE Transactions on Knowledge and Data Engineering and IEEE Intelligent Systems, as well as the International Journal of Knowledge and Information Systems. More information about him can be found at http://www.cse.ust.hk/~qyang/

Lab Meeting June 8th, 2010 (fish60): Learning to Navigate Through Crowded Environments

Peter Henry, Christian Vollmer, Brian Ferris, and Dieter Fox,
Learning to Navigate Through Crowded Environments,
in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA2010), Anchorage, Alaska, May 2010

Abstract—The goal of this research is to enable mobile robots to navigate through crowded environments such as indoor shopping malls, airports, or downtown side walks. The key research question addressed in this paper is how to learn planners that generate human-like motion behavior. Our approach uses inverse reinforcement learning (IRL) to learn human-like navigation behavior based on example paths. Since robots have only limited sensing, we extend existing IRL methods to the case of partially observable environments. We demonstrate the capabilities of our approach using a realistic crowd flow simulator in which we modeled multiple scenarios in crowded environments. We show that our planner learned to guide the robot along the flow of people when the environment is crowded, and along the shortest path if no people are around.

Link

News: Innovation: Teaching robots some manners

13:07 17 May 2010 by Colin Barras

Where PCs are concerned, faster is invariably better. But things aren't so clear-cut in human society. The next generation of social robots will be better loved if they adopt more human-like behaviour – even if that means losing some of their raw efficiency.

Norihiro Hagita and colleagues at the ATR laboratories in Kyoto, Japan, asked 38 volunteers to click on a PC mouse to enlarge an image. The response was programmed to be delayed by 1 to 3 seconds. As expected, an immediate response was most favoured, and participants expressed more and more dissatisfaction as the delay lengthened.

But a version of the experiment that involved a humanoid robot threw up a surprising result. The volunteers were asked to tell the robot to take out the rubbish, and the robot verbally acknowledged the request. This time an immediate response – beginning the moment the volunteer finished talking – was considered less welcome than one that was delayed by a second.

...

See the full article here.

News: Software that Learns by Watching

KarDo learns how to perform common IT support tests by observing what the experts do.
By Duncan Graham-Rowe
WEDNESDAY, MAY 19, 2010

Overworked and much in demand, IT support staff can't be in two places at once. But software designed to watch and learn as they carry out common tasks could soon help--by automatically performing the same jobs across different computers.

The new software system, called KarDo, was developed by researchers at MIT. It can automatically configure an e-mail account, install a virus scanner, or set up access to a virtual private network, says MIT's Dina Katabi, an associate professor at MIT.

Crucially, the software just needs to watch an administrator perform this task once before being able to carry out the same job on computers running different software. Businesses spend billions of dollars each year on simple and repetitive IT tasks, according to reports from the analyst groups Forrester and Gartner. KarDo could reduce these costs by as much as 20 percent, Katabi says.

See the full article here.

Lab Meeting June 1st, 2010 (Kuen-Han): Multiframe Motion Segmentation with Missing Data Using PowerFactorization and GPCA(IJCV 2008)

Title: Multiframe Motion Segmentation with Missing Data Using
PowerFactorization and GPCA
Authors: René Vidal · Roberto Tron · Richard Hartley

Abstract: We consider the problem of segmenting multiple
rigid-body motions from point correspondences in multiple
affine views. We cast this problem as a subspace clustering
problem in which point trajectories associated with each
motion live in a linear subspace of dimension two, three or
four. Our algorithm involves projecting all point trajectories
onto a 5-dimensional subspace using the SVD, the Power-
Factorization method, or RANSAC, and fitting multiple linear
subspaces representing different rigid-body motions to
the points in R5 using GPCA. Unlike previous work, our
approach does not restrict the motion subspaces to be
fourdimensional and independent. Instead, it deals gracefully
with all the spectrum of possible affine motions: from twodimensional
and partially dependent to four-dimensional and fully independent.

Our algorithm can handle the case of missing data, meaning
that point tracks do not have to be visible in all images, by
using the PowerFactorization method to project the data. In
addition, our method can handle outlying trajectories by using
RANSAC to perform the projection.

We compare our approach to other methods on a database of
167 motion sequences with full motions, independent motions,
degenerate motions, partially dependent motions, missing data,
outliers, etc. On motion sequences with complete data our
method achieves a misclassification error of less that 5% for
two motions and 29% for three motions.

paper link

Lab Meeting June 1, 2010 (Jimmy): Object Recognition in 3D Point Clouds Using Web Data and Domain Adaptation

Title: Object Recognition in 3D Point Clouds Using Web Data and Domain Adaptation
In: IJRR2010
Authors: Kevin Lai and Dieter Fox

Abstract
In recent years, object detection has become an increasingly active field of research in robotics. An important problem in object detection is the availability of a sufficient amount of labeled training data to learn good classifiers. In this paper we show how to significantly reduce the need for manually labeled training data by leveraging data sets available on the World Wide Web. Specifically, we show how to use objects from Google’s 3D Warehouse to train an object detection system for 3D point clouds collected by robots navigating through both urban and indoor environments. In order to deal with the different characteristics of the web data and the real robot data, we additionally use a small set of labeled point clouds and perform domain adaptation. Our experiments demonstrate that additional data taken from the 3D Warehouse along with our domain adaptation greatly improves the classification accuracy on real-world environments.

[pdf]

Lab Meeting May, 18 (Gary) : "2D vs. 3D Deformable Face Models: Representational Power, Construction, and Real-Time Fitting "( IJCV 2007)

Title:
2D vs. 3D Deformable Face Models: Representational Power, Construction, and Real-Time Fitting

Authors: Iain Matthews, Jing Xiao, Simon Baker


Abstract:

Model-based face analysis is a general paradigm with applications that include face recognition, expression recognition, lip-reading, head pose estimation, and gaze estimation. A face model is first constructed from a collection of training data, either 2D images or 3D range scans. The face model is then fit to the input image(s) and the model parameters used in whatever the application is. Most existing face models can be classified as either 2D (e.g. Active Appearance Models) or 3D (e.g. Morphable Models). In this paper we compare 2D and 3D face models along three axes: (1) representational power, (2) construction, and (3) real-time fitting. For each axis in turn, we outline the differences that result from using a 2D or a 3D face model.


link

ICRA 2010 Awards - Best Cognitive Robotics Paper

• Gesture-Based Human-Robot Jazz Improvisation: Guy Hoffman and Gil Weinberg (Video)
  
• Categorizing Object-Action Relations from Semantic Scene Graphs: Eren Erdal Aksoy, Alexey Abramov, Florentin Woergoetter and Babette Dellen
• Mapping Indoor Environments Based on Human Activity: Slawomir Grzonka, Frederic Dijoux, Andreas Karwath and Wolfram Burgard
• Biomimetic Motor Behavior for Simultaneous Adaptation of Force, Impedance and Trajectory in Interaction Tasks: Gowrishankar Ganesh, Alin Albu-Schäffer, Masahiko Haruno, Mitsuo Kawato and Etienne Burdet
  

ICRA 2010 Awards - KUKA Service Robotics Best Paper

• Visual Path Following on a Manifold in Unstructured Three-Dimensional Terrain: Paul Timothy Furgale and Timothy Barfoot (Video)
  
• Efficiency Based Modulation for Wheelchair Driving Collaborative Control: Christina Urdiales, Manuel Fernandez-Carmona, Jose Manuel Peula Palacios, Roberta Annicchiarico, Francisco Sandoval and Carlo Caltagirone
• Anytime Online Novelty Detection for Vehicle Safeguarding: Boris Sofman, James Bagnell and Anthony Stentz
• Pulling Open Doors and Drawers: Coordinating an Omni-Directional Base and a Compliant Arm with Equilibrium Point Control: Advait Jain and Charlie Kemp (Video)
  

ICRA 2010 Awards - Best Conference Paper

• Integrated Silicon-PDMS Process for Microbot Mechanisms: Aaron P. Gerratt, Ivan Penskiy and Sarah Bergbreiter
• The Distributed Flight Array: Raymond Oung, Frederic Bourgault, Matthew Donovan and Rafaello D'Andrea
• MoonWalker, a Lower Limb Exoskeleton able to Sustain Bodyweight using a Passive Force Balancer: Sebastien Krut, Michel Benoit, Etienne Dombre and François Pierrot
• Fast, Robust Quadruped Locomotion Over Challenging Terrain: Mrinal Kalakriskhan, Jonas Buchli, Peter Pastor, Michael Mistry and Stefan Schaal

ICRA 2010 Awards - Best Student Paper

• Non-Ideal Swimming of Artificial Bacterial Flagella Near a Surface: Kathrin Eva Peyer, Li Zhang, Bradley Kratochvil and Bradley J. Nelson
• Feature Detection and Matching in Images with Radial Distortion: Miguel Lourenço, Joăo P. Barreto and Abed Malti
• Mapping Indoor Environments Based on Human Activity: Slawomir Grzonka, Frederic Dijoux, Andreas Karwath and Wolfram Burgard
• Toward the Set of Frictional Velocity Fields Generable by 6-Degree-Of-Freedom Oscillatory Motion of a Rigid Plate: Thomas Vose, Paul Umbanhowar and Kevin Lynch
  

ICRA 2010 Awards - Best Medical Robotics Paper

• Superhuman Performance of Surgical Tasks by Robots Using Iterative Learning from Human-Guided Demonstrations: Jur van den Berg, Daniel Duckworth, Stephen Miller, Humphrey Hu, Andrew Wan, Xiao-Yu Fu, Ken Goldberg and Pieter Abbeel
• Wireless Control of Magnetic Helical Microbots Using a Rotating-Permanent-Magnet Manipulator: Thomas Fountain, Prem Kailat and Jake Abbott
• Design and Control of Motion Compensation Cardiac Catheters: Samuel Benjamin Kesner and Robert D. Howe
• Robotically Generated Force Fields for Stroke Patient Pelvic Obliquity Gait Rehabilitation: Maciej Pietrusinski, Iahn Cajigas, Yuhannes Mizikacioglu, Paolo Bonato and Constantinos Mavroidis
  

ICRA 2010 Awards - Best Automation Paper

• Continuous Path Tracing by a Cable-Suspended, Under-Actuated Robot: The Winch-Bot: Daniel Cunningham and Harry Asada
• Hybrid Dual Actuator Unit: A Design of a Variable Stiffness Actuator Based on an Adjustable Moment Arm Mechanism: Byeong-Sang Kim and Jae-Bok Song
• New Magnetic Rotational Drive by Use of Magnetic Particles with Specific Gravity Smaller Than a Liquid: Makoto Nokata, Hiromi Masuka and Satoshi Kaitamura
• Rao-Blackwellized Particle Filtering for Probing-Based 6-DOF Localization in Robotic Assembly: Yuichi Taguchi and Tim K. Marks
  

ICRA 2010 Awards - Best Vision Paper

• Real-time Monocular SLAM: Why Filter?: Hauke Strasdat, J.M.M. Montiel and Andrew J. Davison
• Projected Texture Stereo: Kurt Konolige
• Using Stereo for Object Recognition: Scott Helmer and David Lowe
• FAB-MAP 3D: Topological Mapping with Spatial and Visual Appearance: Rohan Paul and Paul Newman
  

ICRA 2010 Awards - Best Manipulation Paper

• Octo-Mag: An Electromagnetic System for 5-DOF Wireless Micromanipulation: Michael Kummer, Jake Abbott, Bradley Kratochvil, Ruedi Borer, Ali Sengul and Bradley J. Nelson
• Planning Pre-Grasp Manipulation for Transport Tasks: Lillian Chang, Siddhartha Srinivasa and Nancy S. Pollard
• Representation and Constrained Planning of Manipulation Strategies in the Context of Programming by Demonstration: Rainer Jäkel, Sven R. Schmidt-Rohr, Martin Lösch and Rüdiger Dillmann
• A Micromanipulation System for Single Cell Deposition: Zhe Lu, Chris Moraes, Yan Zhao, LiDan You, Craig A. Simmons and Yu Sun
  

ICRA 2010 Awards - Best Video

This post, along with the following posts, will provide links to the best paper awards and finalists for each category in ICRA 2010, with the best papers listed first.

• Minimalistic, Dynamic, Tube Climbing Robot: Amir Degani, Siyuan Feng, Howie Choset and Matthew T. Mason (Video)
  
• Octo-Mag: An Electromagnetic System for 5-DOF Wireless Manipulation: Bradley Kratochvil, Michael Kummer, Jake Abbott, Ruedi Borer, Olgaç Ergeneman and Bradley J. Nelson (Video)
  
• From Compliant Balancing to Dynamic Walking on Humanoid Robot: Integration of CNS and CPG: Sang-Ho Hyon (Video)
  
• RANGE - Robust Autonomous Navigation in GPS-Denied Environments: Abraham Bachrach, Anton de Winter, Ruijie He, Garrett Hemann, Samuel Prentice and Nicholas Roy (Video)
  

Lab Meeting May 11 (Nicole): Active Audition Using the Parameter-less Self-organising Map (Auton Robot 2008)

Title: Active Audition Using the Parameter-less Self-organising Map
Authors: Erik Berglund · Joaquin Sitte · Gordon Wyeth

Autonomous Robots Volume 24, Number 4, 2008/5

Abstract:
This paper presents a novel method for enabling a robot to determine the position of a sound source in three dimensions using just two microphones and interaction with its environment. The method uses the Parameter-Less Self-Organising Map (PLSOM) algorithm and Reinforcement Learning (RL) to achieve rapid, accurate response. We also introduce a method for directional filtering using the PLSOM. The presented system is compared to a similar system to evaluate its performance.

[Link]

Lab Meeting May 11 (Shao-Chen): Decentralized Localization of Sparsely-Communicating Robot Networks: A Centralized-Equivalent Approach(IEEE T-RO 2010)

Title: Decentralized Localization of Sparsely-Communicating Robot Networks: A Centralized-Equivalent Approach
Authors: Leung, K.Y.K.;   Barfoot, T.D.;   Liu, H.;   Inst. for Aerosp. Studies, Univ. of Toronto, Toronto, ON, Canada

IEEE TRANSACTIONS ON ROBOTICS, VOL. 26, NO. 1, FEBRUARY 2010

Abstract:
Finite-range sensing and communication are factors in the connectivity of a dynamic mobile-robot network. State estimation becomes a difficult problem when communication connections allowing information exchange between all robots are not guaranteed. This paper presents a decentralized state-estimation algorithm guaranteed to work in dynamic robot networks without connectivity requirements. We prove that a robot only needs to consider its own knowledge of network topology in order to produce an estimate equivalent to the centralized state estimate whenever possible while ensuring that the same can be performed by all other robots in the network. We prove certain properties of our technique and then it is validated through simulations. We present a comprehensive set of results, indicating the performance benefit in different network connectivity settings, as well as the scalability of our approach.

Link

ICRA 2010 Day 1 Report:

I will attend the Best Practice in 3D Perception and Modeling for Mobile Manipulation workshop on May 3rd. The web page of this workshop is here. The papers of this workshop are available at the lab server.

-Bob

Lab Meeting April 27th (Andi): Error Modeling and Calibration of Exteroceptive Sensors for Accurate Mapping Applications

Authors: James P. Underwood, Andrew Hill, Thierry Peynot, and Steven J. Scheding
ARC Centre of Excellence for Autonomous Systems, Australian Centre for Field Robotics

Abstract: Reliable robotic perception and planning are critical to performing autonomous actions in uncertain, unstructured environments. In field robotic systems, automation is achieved by interpreting exteroceptive sensor information to infer something about the world. This is then mapped to provide a consistent spatial context, so that actions can be planned around the predicted future interaction of the robot and the world. The whole system is as reliable as the weakest link in this chain. In this paper, the term mapping is used broadly to describe the transformation of range-based exteroceptive sensor data (such as LIDAR or stereo vision) to a fixed navigation frame, so that it can be used to form an internal representation of the environment. The coordinate transformation from the sensor frame to the navigation frame is analyzed to produce a spatial error model that
captures the dominant geometric and temporal sources of mapping error. This allows the mapping accuracy to be calculated at run time. A generic extrinsic calibration method for exteroceptive range-based sensors is then presented to determine the sensor location and orientation. This allows systematic errors in individual sensors to be minimized, and when multiple sensors are used, it minimizes the systematic contradiction between them to enable reliable multisensor data fusion. The mathematical derivations at the core of this model are not particularly novel or complicated, but the rigorous analysis and application to field robotics seems to be largely absent from the literature to date. The techniques in this paper are simple to implement, and they offer a significant improvement to the accuracy, precision, and integrity of mapped information. Consequently, they should
be employed whenever maps are formed from range-based exteroceptive sensor data.

full paper

Lab Meeting April 20th, 2010 (KuoHuei): Directing Crowd Simulations Using Navigation Fields (TVCG 2010)

Title: Directing Crowd Simulations Using Navigation Fields

(to appear in IEEE Transactions on Visualization and Computer Graphics)

Authors: Sachin Patil,Jur van den Berg,Sean Curtis,Ming Lin,Dinesh Manocha

Abstract: We present a novel approach to direct and control virtual crowds using navigation fields. Our method guides one or more agents towards desired goals based on guidance fields. The system allows the user to specify these fields by either sketching paths directly in the scene via an intuitive authoring interface or by importing motion flow fields extracted from crowd video footage. We propose a novel formulation to blend input guidance fields to create singularity-free, goal-directed navigation fields. Our method can be easily combined with most current local collision-avoidance methods and we use two such methods as examples to highlight the potential of our approach. We illustrate its performance on several simulation scenarios.

Link: [web] [pdf]

Lab Meeting April 20th, 2010 (Jeff): iSAM: Incremental Smoothing and Mapping

Title: iSAM: Incremental Smoothing and Mapping

Authors: Michael Kaess, Ananth Ranganathan, and Frank Dellaert

Abstract:

In this paper, we present incremental smoothing and mapping (iSAM), which is a novel approach to the simultaneous localization and mapping problem that is based on fast incremental matrix factorization. iSAM provides an efficient and exact solution by updating a QR factorization of the naturally sparse smoothing information matrix, thereby recalculating only those matrix entries
that actually change. iSAM is efficient even for robot trajectories with many loops as it avoids unnecessary fill-in in the factor matrix by periodic variable reordering. Also, to enable data association in real time, we provide efficient algorithms to access the estimation uncertainties of interest based on the factored information matrix. We systematically evaluate the different components of iSAM as well as the overall algorithm using various simulated and realworld
datasets for both landmark and pose-only settings.

Link:
IEEE Transactions on Robotics, Vol. 24, No. 6, December 2008:1365-1378
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4682731

News: Grand Challenges of Science: Robotics

In January, DISCOVER and the National Science Foundation continued their Grand Challenges event series with a panel discussion at Carnegie Mellon University exploring the dynamic world of robotics.

The panel included four eminent roboticists—Javier Movellan from University of California San Diego, Rodney Brooks from the Massachussetts Institute of Technology, William "Red" Whittaker from Carnegie Mellon University, and Robyn Murphy from Texas A&M University—who discussed some the big questions on the future of their field: How will robots transform industry, health care, and warfare? Will they ever be our equals? The conversation was moderated by DISCOVER editor-in-chief Corey Powell.

See the video

Lab Meeting April 13, 2010 (Alan) - Information-Based Compact Pose SLAM (T-RO 2010)

Title: Information-Based Compact Pose SLAM

Authors: Viorela Ila, Josep M. Porta, and Juan Andrade-Cetto, Member, IEEE

IEEE TRANSACTIONS ON ROBOTICS, VOL. 26, NO. 1, FEBRUARY 2010

Abstract—Pose SLAMis the variant of simultaneous localization and map building (SLAM) is the variant of SLAM, in which only the robot trajectory is estimated and where landmarks are only used to produce relative constraints between robot poses. To reduce the computational cost of the information filter form of PoseSLAM and, at the same time, to delay inconsistency as much as possible, we introduce an approach that takes into account only highly informative loop-closure links and nonredundant poses. This approach includes constant time procedures to compute the distance between poses, the expected information gain for each potential link, and the exact marginal covariances while moving in open loop, as well as a procedure to recover the state after a loop closure that, in practical situations, scales linearly in terms of both time and memory. Using these procedures, the robot operates most of the time in open loop, and the cost of the loop closure is amortized over long trajectories. This way, the computational bottleneck shifts to data association, which is the search over the set of previously visited poses to determine good candidates for sensor registration. To speed up data association, we introduce a method to search for neighboring poses whose complexity ranges from logarithmic in the usual case to linear in degenerate situations. The method is based on organizing the pose information in a balanced tree whose internal levels are defined using interval arithmetic. The proposed Pose-SLAM approach is validated through simulations, real mapping sessions, and experiments using standard SLAM data sets.

Link

Researchers develop a robot that folds towels

More than a household convenience, the project is a breakthrough in the robotic manipulation of non-rigid objects

Check out their ICRA 2010 paper.

Lab Meeting 3/29, 2010 (swem): MonoSLAM: Real-Time Single Camera SLAM

MonoSLAM: Real-Time Single Camera SLAM

Andrew J. Davison, Ian D. Reid, Member, IEEE, Nicholas D. Molton, and Olivier Stasse, Member, IEEE

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 29, NO. 6, JUNE 2007

[link]

Abstract—We present a real-time algorithm which can recover the 3D trajectory of a monocular camera, moving rapidly through a

previously unknown scene. Our system, which we dub MonoSLAM, is the first successful application of the SLAM methodology from

mobile robotics to the “pure vision” domain of a single uncontrolled camera, achieving real time but drift-free performance inaccessible

to Structure from Motion approaches. The core of the approach is the online creation of a sparse but persistent map of natural

landmarks within a probabilistic framework. Our key novel contributions include an active approach to mapping and measurement, the

use of a general motion model for smooth camera movement, and solutions for monocular feature initialization and feature orientation

estimation. Together, these add up to an extremely efficient and robust algorithm which runs at 30 Hz with standard PC and camera

hardware. This work extends the range of robotic systems in which SLAM can be usefully applied, but also opens up new areas. We

present applications of MonoSLAM to real-time 3D localization and mapping for a high-performance full-size humanoid robot and live

augmented reality with a hand-held camera.

NTU talk: Image-Based Mobile Robot Navigation Design

Title: Image-Based Mobile Robot Navigation Design
Speaker: Prof. Kai-Tai Song, National Chiao Tung University
Time: 2:20pm, April 2 (Fri), 2010
Place: Room 103, CSIE Building


Abstract: Autonomous navigation is a basic function of every mobile robot. It is important for a mobile robot to travel and arrive at a desired location autonomously. In order to accomplish this function, the robot needs to observe its surroundings and locate itself through acquired environmental information. Various sensors have been utilized for acquiring information from environment. Among these perception sensors, image-based systems are most promising for practical application in daily-life scenarios. In this talk, I will first give a general perspective of domestic and service robots today. Then the design issues of mobile robot navigation will be discussed. Two approaches to visual navigation design will be presented. One employs an omni-directional camera, the other uses a normal webcam. In this presentation, the design and implementation of the navigation system will be discussed. Some interesting video clips of autonomous mobile robots will be presented.

Short Biography: Kai-Tai Song received his Ph.D. degree in mechanical engineering from Katholieke Universiteit Leuven, Belgium in 1989. Since 1989 he has been a faculty member and is currently a Professor with National Chiao Tung University (NCTU). From 2007 to 2009, he served as the Associate Dean of the R & D Office of NCTU. He is currently the Director of Institute of Electrical and Control Engineering of NCTU. He has served as the Chairman of IEEE Robotics & Automation Chapter, Taipei Section in the term of 1999. His is a directorate of Robotics Association Taiwan, Taiwan Robotics Society, Taiwan Association of System Science and Engineering, and Chinese Automatic Control Society.
His areas of research interest include mobile robotics, image processing, visual tracking, human-robot interaction, and mechatronics.

Lab Meeting 3/23, 2010 (fish60): Learning to Search: Functional Gradient Techniques for Imitation Learning

I will try to present this one:

Learning to Search: Functional Gradient Techniques for Imitation Learning
Nathan Ratliff, David Silver, J. Andrew Bagnell

Submitted to Autonomous Robotics Special Issue on Robot Learning, 2009

[download draft]

Abstract:
While planning algorithms have shown success in many real-world applications ranging from legged locomotion to outdoor unstructured navigation, such algorithms rely on fully specified cost functions that map sensor readings and environment models to quantifiable costs. Such cost functions are usually manually designed and programmed. Recently, a set of techniques has been developed that explore learning these functions from expert human demonstration. These algorithms apply an inverse optimal control approach to find a cost function for which planned behavior mimics an expert's demonstration.
The work we present extends the Maximum Margin Planning (MMP) frame- work to admit learning of more powerful, non-linear cost functions. These algorithms, known collectively as LEARCH (LEArning to seaRCH ), are simpler to implement than most existing methods, more efficient than previous attempts at non-linearization, more naturally satisfy common constraints on the cost function, and better represent our prior beliefs about the function's form.

NTU talk: 3D Reconstruction from Images

Title: 3D Reconstruction from Images

Shang-Hong Lai
Department of Computer Science
National Tsing Hua University

Time: 2:20pm, March 26 (Fri), 2010
Place: Room 103, CSIE Building

Abstract: The goal of 3D computer vision is to recover real-world three-dimensional information of the scene or objects from 2D images. There have been many different approaches with various image acquisition set-ups to achieve the 3D reconstruction in computer vision. In this talk, I will discuss some 3D reconstruction systems and some researches on 3D reconstruction in my lab. I will present our researches on 3D reconstruction from multi-view images, two-view (stereo) images, and a single image. Some 3D reconstruction results will be shown to demonstrate the performance of our systems.

Bio: Shang-Hong Lai received the BS and MS degrees in electrical engineering from National Tsing Hua University in 1986 and 1988, respectively. He also received the PhD degree from University of Florida, Gainesville, USA, in 1995. He worked for Siemens Corporate Research in Princeton, USA, as a research scientist from 1995 to 2000. Then, Dr. Lai returned to Taiwan to join department of computer science, National Tsing Hua University, as a faculty member. He is currently a professor and associate chair in the same department.