Error correction in mobile robot map learning

Abstract

An issue that must be addressed in map-learning systems is that of error accumulation. The primary emphasis in the literature has been on reducing errors entering the map. The authors suggest that this methodology must reach a point of diminishing returns, and hence focus on explicit error detection and correction. By identifying the possible types of mapping errors, structural constraints can be exploited to detect and diagnose mapping errors. Such robust mapping requires little overhead beyond that needed for nonrobust mapping. A mapping system was implemented based on those ideas. Extensive testing in simulation demonstrated the effectiveness of the proposed error-correction strategies.