Localization of mobile robots using incremental local maps

Abstract

This paper presents an algorithm for the global localization of mobile robots. The general idea is to build a local map incrementally which is matched to a global map in each localization step. The matching algorithm is a very time and memory efficient enhancement of the common Random Sample Consensus (RANSAC). It is executed a fixed number of iterations computing a set of hypotheses of the current robot pose. This paper describes how to handle the resulting hypotheses, i.e. a method is introduced deciding when the robot is localized reliably enough. The algorithm has been implemented and its characteristics are evaluated and discussed in an experimental section.