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