Ami Berler
Ben-Gurion University - Math. and Comp. Sci. Dept.
P. O. Box 653
84105 Be'er Sheva, Israel
e-mail: ami@cs.bgu.ac.il
FAX : +972-7-472909
Solomon Eyal Shimony
Ben-Gurion University - Math. and Comp. Sci. Dept.
P. O. Box 653
84105 Be'er Sheva, Israel
e-mail: shimony@cs.bgu.ac.il
Phone: +972-7-461653
FAX : +972-7-472909
Wide-angle sonar mapping of the environment by mobile robot is nontrivial due to several sources of uncertainty: dropouts due to ``specular'' reflections, obstacle location uncertainty due to the wide beam, and distance measurement error. Earlier papers address the latter problems, but dropouts remain a problem in many environments. We present an approach that lifts the overoptimistic independence assumption used in earlier work, and use Bayes nets to represent the dependencies between objects of the model. Objects of the model consist of readings, and of regions in which ``quasi location invariance'' of the (possible) obstacles exists, with respect to the readings. Simulation supports the method's feasibility. The model is readily extensible to allow for prior distributions, as well as other types of sensing operations.
Sensor Fusion, Sonar-Based Mapping, Probabilistic Reasoning, Bayes Nets.
This paper is also available in (compressed PostScript) format.
dsl@sis.pitt.edu /
Last update: 22 May 1997