Maximum-likelihood sample-based maps for mobile robots

The problem of representing the environment of a mobile robot has been studied intensively in the past. The predominant approaches for geometric representations are grid-based or line-based maps. In this paper, we consider sample-based maps which use the data points obtained by range scanners to represent the environment. The main advantage of this representation over the other techniques is that it is able to represent arbitrary structures and at the same time provide an arbitrary accuracy. However, range measurements come in large amounts and not every measurement necessarily contributes to the representation in the same way. We present a novel approach for calculating maximum-likelihood subsets of the data points by sub-sampling laser range data. In particular, our method applies a variant of the fuzzy k-means algorithm to find a map that maximizes the likelihood of the original data. Experimental results with real data show that the resulting maps are better suited for robot localization than maps obtained with other sub-sampling techniques.