Fast and accurate SLAM with Rao-Blackwellized particle filters

Rao-Blackwellized particle filters have become a popular tool to solve the simultaneous localization and mapping problem. This technique applies a particle filter in which each particle carries an individual map of the environment. Accordingly, a key issue is to reduce the number of particles and/or to make use of compact map representations. This paper presents an approximative but highly efficient approach to mapping with Rao-Blackwellized particle filters. Moreover, it provides a compact map model. A key advantage is that the individual particles can share large parts of the model of the environment. Furthermore, they are able to reuse an already computed proposal distribution. Both techniques substantially speed up the overall filtering process and reduce the memory requirements. Experimental results obtained with mobile robots in large-scale indoor environments and based on published standard datasets illustrate the advantages of our methods over previous mapping approaches using Rao-Blackwellized particle filters.

[1]  Ingemar J. Cox,et al.  Autonomous Robot Vehicles , 1990, Springer New York.

[2]  Favio R. Masson,et al.  Navigation and Mapping in Large Unstructured Environments , 2004, Int. J. Robotics Res..

[3]  Peter C. Cheeseman,et al.  Estimating uncertain spatial relationships in robotics , 1986, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[4]  Wolfram Burgard,et al.  On actively closing loops in grid-based FastSLAM , 2005, Adv. Robotics.

[5]  Matthew R. Walter,et al.  Sparse extended information filters: insights into sparsification , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  T. Michael Knasel,et al.  Robotics and autonomous systems , 1988, Robotics Auton. Syst..

[7]  Udo Frese,et al.  Simultaneous Localization and Mapping - A Discussion , 2001 .

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

[9]  Simon J. Godsill,et al.  On sequential simulation-based methods for Bayesian filtering , 1998 .

[10]  Sebastian Thrun,et al.  FastSLAM: a factored solution to the simultaneous localization and mapping problem , 2002, AAAI/IAAI.

[11]  Mark A. Paskin,et al.  Thin Junction Tree Filters for Simultaneous Localization and Mapping , 2002, IJCAI.

[12]  Kevin P. Murphy,et al.  Bayesian Map Learning in Dynamic Environments , 1999, NIPS.

[13]  Hugh F. Durrant-Whyte,et al.  Mobile robot localization by tracking geometric beacons , 1991, IEEE Trans. Robotics Autom..

[14]  Sebastian Thrun,et al.  FastSLAM 2.0: An Improved Particle Filtering Algorithm for Simultaneous Localization and Mapping that Provably Converges , 2003, IJCAI.

[15]  Hugh F. Durrant-Whyte,et al.  Simultaneous Localization and Mapping with Sparse Extended Information Filters , 2004, Int. J. Robotics Res..

[16]  Juan D. Tardós,et al.  Data association in stochastic mapping using the joint compatibility test , 2001, IEEE Trans. Robotics Autom..

[17]  Wolfram Burgard,et al.  Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[18]  Howie Choset,et al.  Hierarchical simultaneous localization and mapping , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[19]  Hugh F. Durrant-Whyte,et al.  Simultaneous Mapping and Localization with Sparse Extended Information Filters: Theory and Initial Results , 2004, WAFR.

[20]  Nando de Freitas,et al.  Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks , 2000, UAI.

[21]  Michael Bosse,et al.  An Atlas framework for scalable mapping , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[22]  Ronald Parr,et al.  DP-SLAM: fast, robust simultaneous localization and mapping without predetermined landmarks , 2003, IJCAI 2003.

[23]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[24]  Wolfram Burgard,et al.  An efficient fastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[25]  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).

[26]  Juan D. Tardós,et al.  Hierarchical SLAM: real-time accurate mapping of large environments , 2005, IEEE Transactions on Robotics.