Localization and Mapping for Mobile Robots using Correlation
Kurt Konolige
Artificial Intelligence Center
SRI International
[Joint work with Ken Chou (SRI) and Steffen Gutmann (U. of Freiburg)]
Abstract
In this talk I will give a general overview of recent work related to
probabilistic methods for localization (Markov Localization and Scan
Matching) and mapping (Expectation Maximization and the method of Lu
and Milios) in the domain of mobile robots. While these basic
theoretical approaches are well-founded and yield good results, in
practice the computational issues of representing and updating large
distributions often dominate the problem. In joint work with Ken Chou
(SRI), I have developed correlation methods that are orders of
magnitude faster than standard update methods. We have used these
methods to perform localization at video rates from laser range
information. Another application of correlation is in the very
difficult problem of "closing the loop" in mapping. Here, in joint
work with Steffen Gutmann (U. of Freiburg), we have succeeded in
automatically and efficiently generating globally consistent maps with
very large loops.
Eyal Amir
Last modified: Fri Apr 2 11:50:05 PST 1999