Difference between revisions of "Prabir Barooah, May 2012"

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(Agenda)
 
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* 2:00: Steven Low, 219 Annenberg
 
* 2:00: Steven Low, 219 Annenberg
 
* 2:45: Scott Livingston, 331 Annenberg
 
* 2:45: Scott Livingston, 331 Annenberg
* 3:30: Open
+
* 3:30: Stephanie Tsuei
 
* 4:15: Open
 
* 4:15: Open
 
* 5:00: Done for the day
 
* 5:00: Done for the day

Latest revision as of 02:12, 10 May 2012

Prabir Barooah will be visiting Caltech on 10 May (Thu).

Agenda

  • 10:30: Richard Murray, 109 Steele Lab
  • 11:00: Seminar, Steele Lab library
  • 12:00: Lunch with faculty: Richard, Doug MacMynowski, Joel Burdick
  • 1:15: Necmiye Ozay, 130 Steele Lab
  • 2:00: Steven Low, 219 Annenberg
  • 2:45: Scott Livingston, 331 Annenberg
  • 3:30: Stephanie Tsuei
  • 4:15: Open
  • 5:00: Done for the day

Abstract

Improving robot localization by using robot-to-robot relative measurements in cooperative multi-robot teams

Thursday, 10 May 2012 - 11:00am, 114 Steele Lab

Localization of robots without GPS (or intermittentGPS) is a capability that is required of autonomous robots in several application domains. These include under water, in urban canyons, with poor line of sight to satellites, with adversarial GPS jamming, etc. Localization by integrating self-motion measurements from on-board sensors such as IMUs/cameras lead to large growth of estimation error. This limits how long a robot can operate effectively without GPS. In a team of collaborative robots, however, there is an opportunity to do better. A robot can occasionally measure another robot's relative pose/position/bearing, usually by using vision-based sensors. This information, when fused with self-motion measurements, can lead to location estimates that are substantially more accurate than what can be obtained with self-motion measurements alone.

In this talk we will describe a class of algorithms for fusing relative measurements to estimate the pose (position and orientation) of robots in a collaborative team. Due to the unknown orientations that are to be estimated, the problem becomes an optimization problem over a Riemannian manifold. A least-squares and (approximate) maximum likelihood estimator are developed. The least-squares version can be made distributed: it requires limited communication between nearby robots, but no centralized data exchange is needed. In each case, a gradient descent on the manifold is used to compute the estimates. Much of the earlier work in collaborative pose estimation was limited to 2-D case, the formulation we adopt allows to handle 3-D scenario with no additional difficulty. As is often the case, a number of unanswered questions remain, some of which will be discussed.

Brief Bio

Prabir Barooah was born in Jorhat, Assam (India). He received the Ph.D. degree in Electrical and Computer Engineering in 2007 from the University of California, Santa Barbara. From 1999 to 2002 he was a research engineer at United Technologies Research Center, East Hartford, CT. He received the M. S. degree in Mechanical Engineering from the University of Delaware in 1999 and the B.Tech degree in Mechanical Engineering from the Indian Institute of Technology, Kanpur, in 1996. Dr. Barooah is the winner of the NSF CAREER award (2010), General Chairs' Recognition Award for Interactive papers at the 48th IEEE Conference on Decision and Control (2009), best paper award at the 2nd Int. Conf. on Intelligent Sensing and Information Processing (2005), and NASA group achievement award (2003).