Mapping and Localisation with Sparse Range Data

We present an approach for indoor mapping and localisation with a mobile robot using sparse range data, without the need for solving the SLAM problem. The paper consists of two main parts. First, a split and merge based method for dividing a givenmetric map into distinct regions is presented, thus creating a topologicalmap in ametric framework. Spatial information extracted from this map is then used for self-localisation. The robot computes local confidence maps for two simple localisation strategies based on distance and relative orientation of regions. The local confidence maps are then fused using an approach adapted from computer vision to produce overall confidence maps. Experiments on data acquired by mobile robots equipped with sonar sensors are presented.