The author introduces a Bayes-maximum entropy formalism for multisensor data fusion, and presents an application of this methodology to the fusion of ultrasound and visual sensor data as acquired by a mobile robot. In the approach the principle of maximum entropy was applied to the construction of priors and likelihoods from data. Distances between ultrasound and visual points of interest in a dual representation were used to define Gibbs likelihood distributions. Both one- and two-dimensional likelihoods are presented and cast into a form which makes explicit their dependence on the mean. The Bayesian posterior distributions were used to test a null hypothesis, and maximum entropy maps used for navigation were updated using the resulting information from the dual representation.>

HERMIES-III is a large mobile robot designed for human-scale experiments. The initial experiment with the robot (DEMO 89) was the cleanup of a simulated chemical spill. To perform the experiment, the robot was required to plan a path through an already known world, navigate along the path (avoiding unexpected obstacles), and locate and remove debris from a target area. A description is given of the software system that was developed to perform the experiment. The software system consisted of 19 processes that operated on a distributed set of heterogeneous computers.>

A methodology has been developed for the treatment of systematic errors that arise in the processing of sparse sensor data. A detailed application of this methodology to the construction, from wide-angle sonar sensor data, of navigation maps for use in autonomous robotic navigation is presented. In the methodology, a four-valued labeling scheme and a simple logic for label combination are introduced. The four labels Conflict, Occupied, Empty, and Unknown are used to mark the cells of the navigation maps. The logic allows for the rapid updating of these maps as new information is acquired. Systematic errors are treated by relabeling conflicting pixel assignments. Most of the new labels are obtained from analyses of the characteristic patterns of conflict that arise during the information processing. The remaining labels are determined by imposing an elementary consistent-labeling condition.>