A Bayes-maximum entropy method for multi-sensor data fusion

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.<
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