The revisiting problem in mobile robot map building: A hierarchical Bayesian approach

The revisiting problem in mobile robot map building: A hierarchical Bayesian approach (2003)

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by Benjamin Stewart , Jonathan Ko , Dieter Fox , Kurt Konolige

Venue:	In Proc. of the Conference on Uncertainty in Artificial Intelligence (UAI
Citations:	24 - 4 self

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Datum	Value	Source
TITLE	The revisiting problem in mobile robot map building: A hierarchical Bayesian approach	INFERENCE
AUTHOR NAME	Benjamin Stewart	SVM HeaderParse 0.2
AUTHOR AFFIL	Dept. of Computer Science & Engineering † Artificial Intelligence Center ‡; University of Washington; SRI International	SVM HeaderParse 0.2
AUTHOR ADDR	Seattle, WA; Menlo Park, CA	SVM HeaderParse 0.2
AUTHOR NAME	Jonathan Ko	SVM HeaderParse 0.2
AUTHOR AFFIL	Dept. of Computer Science & Engineering † Artificial Intelligence Center ‡; University of Washington; SRI International	SVM HeaderParse 0.2
AUTHOR ADDR	Seattle, WA; Menlo Park, CA	SVM HeaderParse 0.2
AUTHOR NAME	Dieter Fox	SVM HeaderParse 0.2
AUTHOR AFFIL	Dept. of Computer Science & Engineering † Artificial Intelligence Center ‡; University of Washington; SRI International	SVM HeaderParse 0.2
AUTHOR ADDR	Seattle, WA; Menlo Park, CA	SVM HeaderParse 0.2
AUTHOR NAME	Kurt Konolige	SVM HeaderParse 0.2
AUTHOR AFFIL	Dept. of Computer Science & Engineering † Artificial Intelligence Center ‡; University of Washington; SRI International	SVM HeaderParse 0.2
AUTHOR ADDR	Seattle, WA; Menlo Park, CA	SVM HeaderParse 0.2
ABSTRACT	We present an application of hierarchical Bayesian estimation to robot map building. The revisiting problem occurs when a robot has to decide whether it is seeing a previously-built portion of a map, or is exploring new territory. This is a difficult decision problem, requiring the probability of being outside of the current known map. To estimate this probability, we model the structure of a ”typical ” environment as a hidden Markov model that generates sequences of views observed by a robot navigating through the environment. A Dirichlet prior over structural models is learned from previously explored environments. Whenever a robot explores a new environment, the posterior over the model is estimated by Dirichlet hyperparameters. Our approach is implemented and tested in the context of multi-robot map merging, a particularly difficult instance of the revisiting problem. Experiments with robot data show that the technique yields strong improvements over alternative methods. 1	SVM HeaderParse 0.2
YEAR	2003	INFERENCE
VENUE	In Proc. of the Conference on Uncertainty in Artificial Intelligence (UAI	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	551--558	INFERENCE
CITATIONS	16 found	ParsCit 1.0