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A robust, multi-hypothesis approach to matching occupancy grid maps

Published online by Cambridge University Press:  11 January 2013

Jose-Luis Blanco*
Department of Engineering, University of Almería, Almería, Spain
Javier González-Jiménez
Department of System Engineering and Automation, University of Málaga, Malaga, Spain
Juan-Antonio Fernández-Madrigal
Department of System Engineering and Automation, University of Málaga, Malaga, Spain
*Corresponding author. E-mail:


This paper presents a new approach to matching occupancy grid maps by means of finding correspondences between a set of sparse features detected in the maps. The problem is stated here as a special instance of generic image registration. To cope with the uncertainty and ambiguity that arise from matching grid maps, we introduce a modified RANSAC algorithm which searches for a dynamic number of internally consistent subsets of feature pairings from which to compute hypotheses about the translation and rotation between the maps. By providing a (possibly multi-modal) probability distribution of the relative pose of the maps, our method can be seamlessly integrated into large-scale mapping frameworks for mobile robots. This paper provides a benchmarking of different detectors and descriptors, along extensive experimental results that illustrate the robustness of the algorithm with a 97% success ratio in loop-closure detection for ~1700 matchings between local maps obtained from four publicly available datasets.

Copyright © Cambridge University Press 2013 

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