Visual place recognition for autonomous robots

The problem of place recognition is central to robot map learning. A robot needs to be able to recognize when it has returned to a previously visited place, or at least to be able to estimate the likelihood that it has been at a place before. Our approach is to compare images taken at two places, using a stochastic model of changes due to shift, zoom, and occlusion to predict the probability that one of them could be a perturbation of the other. We have performed experiments to gather the valve of a χ² statistic applied to image matching from a variety of indoor locations. Image pairs gathered from nearby locations generate low χ² values, and images gathered from different locations generate high values. The rate of false positive and false negative matches is low