Combinatorial Filters: Handling Sensing Uncertainty by Avoiding Big Models

Tuesday, March 24, 2009 - 9:35am

11:00 – 12:00
Computer Science Conference Room, Harold Frank Hall Rm. 1132

HOST: Subhash Suri

SPEAKER: Steven M. LaValle
Professor Department of Computer Science
University of Illinois

Title: Combinatorial Filters: Handling Sensing Uncertainty by Avoiding
Big Models


Over the past several years, Bayesian filtering techniques have become
mainstream tools in robotics research that handles uncertainty.
Variations include the classical Kalman filter and recent particle
filters, all of which are routinely used for robot localization,
navigation, and map building. In this talk, I will introduce a new class
of filters, called combinatorial filters, that are distinctive in
several ways: 1) they simplify modeling burdens by avoiding
probabilities, 2) they are designed for processing information from the
weakest sensors possible, and 3) they avoid unnecessary state
estimation. In many ways, they are the direct analog to Bayesian
filters, but handle substantial amounts of uncertainty by refusing to
model it. The emphasis is on detecting and maintaining tiny pieces of
information that are critical to solving robotic tasks, such as
navigation, map building, target tracking, and pursuit-evasion.


Steven is on the program committees of SoCG 2009, WAFR 2008, and DCOSS 2009. He is co-organizer of the RSS Workshop, Topology and Minimalism in Robotics and Sensor Networks, June 2008. He is also a co-chair of the IEEE RAS Technical Committee on Algorithms for Planning and Control of Robot Motion. In 2002, he organized and hosted the 4th Midwest Mechanical Motion Meeting.