A parallel maximum likelihood algorithm for robot mapping

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.

[1]  Frank Dellaert,et al.  A Multifrontal QR Factorization Approach to Distributed Inference Applied to Multirobot Localization and Mapping , 2005, AAAI.

[2]  Frank Dellaert,et al.  Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing , 2006, Int. J. Robotics Res..

[3]  Wolfram Burgard,et al.  Online constraint network optimization for efficient maximum likelihood map learning , 2008, 2008 IEEE International Conference on Robotics and Automation.

[4]  Evangelos E. Milios,et al.  Globally Consistent Range Scan Alignment for Environment Mapping , 1997, Auton. Robots.

[5]  Hanumant Singh,et al.  Exactly Sparse Delayed-State Filters , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[6]  Stephen R. Marsland,et al.  Fast, On-Line Learning of Globally Consistent Maps , 2002, Auton. Robots.

[7]  Geoffrey C. Fox,et al.  A parallel Gauss-Seidel algorithm for sparse power system matrices , 1994, Proceedings of Supercomputing '94.

[8]  Frank Dellaert,et al.  Tectonic SAM: Exact, Out-of-Core, Submap-Based SLAM , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[9]  Tom Duckett,et al.  A multilevel relaxation algorithm for simultaneous localization and mapping , 2005, IEEE Transactions on Robotics.

[10]  Udo Frese.  Treemap: An O(log n) algorithm for indoor simultaneous localization and mapping , 2006, Auton. Robots.

[11]  Edwin Olson,et al.  Fast iterative alignment of pose graphs with poor initial estimates , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[12]  Frank Dellaert,et al.  iSAM: Fast Incremental Smoothing and Mapping with Efficient Data Association , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[13]  Leon O. Chua,et al.  An efficient heuristic cluster algorithm for tearing large-scale networks , 1977 .

[14]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[15]  Kurt Konolige,et al.  Incremental mapping of large cyclic environments , 1999, Proceedings 1999 IEEE International Symposium on Computational Intelligence in Robotics and Automation. CIRA'99 (Cat. No.99EX375).