Mobile robot localization using feature based fuzzy map matching

This paper presents a novel scan matching approach entitled Fuzzy Map Matching for mobile robot localization that extracts low level features in the form of line segment from perceptual channels that are then matched to a map given a priori. Multiple candidate matches are supported through the use of fuzzy logic, which are iteratively refined. This probabilistic based fuzzy model of scan matching is used to filter the alignment combination that have low probability in order to reduce computational complexity. In addition, the initial pose of the robot does not have to be known as a result of support for multiple hypotheses with respect to potential correspondences. Incomplete line segments that result from incomplete scans, noisy sensors, or occlusion do not present a problem as features in observation space are grown during the correspondence phase of the algorithm. This approach does not impose a heavy demand on computational resources, and is significantly less resource hungry than the probabilistic approaches. Initial results demonstrated that the algorithm performs well in real world environments.