Monday, May 27, 2013

Lab meeting May 29th 2013 (Jim): Reciprocal collision avoidance

I'm going to present the idea of "reciprocal collision avoidance": each moving agent
should take responsibilities for collision avoidance with each other during the navigation. Based on the model of velocity obstacles, the "reciprocal velocity obstacles" and its variations are developed for multi-agent navigation. The main references / materials are the following papers:

Reciprocal Velocity Obstacles for Real-time Multi-agent Navigation
Jur van den Berg, Ming C. Lin, Dinesh Manocha
IEEE International Conference on Robotics and Automation (ICRA), 2008


TheHybrid Reciprocal Velocity Obstacle
Jamie Snape, Jur van den Berg, Stephen J. Guy, Dinesh Manocha
IEEE Transactions on Robotics (T-RO), vol. 27, pp. 696-706, 2011

Reciprocaln-body Collision Avoidance
Jur van den Berg, Stephen J. Guy, Ming C. Lin, Dinesh Manocha
Robotics Research: The 14th International Symposium (ISRR), Springer Tracts in Advanced Robotics (STAR), vol. 70, pp. 3-19, 2011

Tuesday, May 21, 2013

Lab meeting May 22th 2013 (Tom Hsu): Incorporating User Interaction and Topological Constraints within Contour Completion via Discrete Calculus

Presented by: Tom Hsu

From: Proc. of the Computer Vision and Pattern Recognition (CVPR'13),  Portland, Oregon 2013.

Authors: Jia Xu Maxwell D. Collins Vikas Singh (University of Wisconsin-Madison)

Link: Paper

We study the problem of interactive segmentation and contour completion for multiple objects. The form of constraints our model incorporates are those coming from user scribbles (interior or exterior constraints) as well as information regarding the topology of the 2-D space after partitioning (number of closed contours desired). We discuss how concepts from discrete calculus and a simple identity using the Euler characteristic of a planar graph can be utilized to derive a practical algorithm for this problem. We also present specialized branch and bound methods for the case of single contour completion under such constraints. On an extensive dataset of ~1000 images, our experiments suggest that a small amount of side knowledge can give strong improvements over fully unsupervised contour completion methods. We show that by interpreting user indications topologically, user effort is substantially reduced.

Monday, May 06, 2013

Lab meeting Mar 8th 2013 (Gene): Lost! Leveraging the Crowd for Probabilistic Visual Self-Localization

Presented by: Gene

From: CVPR2013

Authors: Marcus A. Brubaker, Andreas Geiger, Raquel Urtasun


In this paper we propose an affordable solution to selflocalization, which utilizes visual odometry and road maps as the only inputs. To this end, we present a probabilistic model as well as an efficient approximate inference algorithm, which is able to utilize distributed computation to meet the real-time requirements of autonomous systems. Because of the probabilistic nature of the model we are able to cope with uncertainty due to noisy visual odometry and inherent ambiguities in the map (e.g., in a Manhattan world). By exploiting freely available, community developed maps and visual odometry measurements, we are able to localize a vehicle up to 3m after only a few seconds of driving on maps which contain more than 2,150km of drivable roads.