A parallel maximum likelihood algorithm for robot mapping

Authors:
Dario Lodi Rizzini;Stefano Caselli
Affiliations:
Robotics and Intelligent Machines Laboratory, Dipartimento di Ingegneria dell'Informazione, University of Parma, Italy;Robotics and Intelligent Machines Laboratory, Dipartimento di Ingegneria dell'Informazione, University of Parma, Italy
Venue:
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Year:
2009

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Abstract

Several recent algorithms address simultaneous localization and mapping as a maximum likelihood problem. While many proposed methods focus on efficiency or on online computation, less interest has been devoted to investigate a parallel or distributed organization of such algorithms in the perspective of multi-robot exploration. In this paper, we propose a parallel algorithm for map estimation based on Gauss-Seidel relaxation. The map is given in the form of a constraints network and is partioned into clusters of nodes by applying a node-tearing technique. The identified clusters of nodes can be processed independently as tasks assigned to different processors. The graph decomposition induces also a hierarchical organization of nodes that could be exploited for more sophisticated relaxation techniques. Results illustrate the potential and flexibility of the new approach.