Mapping and Localisation with Sparse Range Data

  • We present an approach for indoor mapping and localization 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 given metric map into distinct regions is presented, thus creating a topological map in a metric framework. Spatial information extracted from this map is then used for self-localization. The robot computes local confidence maps for two simple localization 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.

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Author:Jochen SchmidtORCiD, C.K. Wong, W.K. Yeap
Parent Title (English):International Conference on Autonomous Robots and Agents (ICARA), pages 497-502, Palmerston North, New Zealand
Document Type:Conference Proceeding
Publication Year:2006
Tag:Localization; Mapping; Mobile Robots
faculties / departments:Fakultät für Informatik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke