Improving the Robustness of Weighted Scan Matching with Quad Trees

This paper presents the improvement the robustness and accuracy of the weighted scan matching algorithm matching against the union of earlier acquired scans. The approach allows to reduce the correspondence error, which is explicitly modeled in the weighted scan matching algorithm, by providing a more complete and denser frame of reference to match new scans. By making use of the efficient quad tree data structure, earlier acquired scans can be stored with millimeter accuracy for environments with dimensions larger than 100x100 meter. This can be realized with the preservation of real-time performance. In our experiments we illustrate the significant gains in robustness and accuracy that can be the result with this approach.

[1]  Nicholas Roy,et al.  Trajectory Optimization using Reinforcement Learning for Map Exploration , 2008, Int. J. Robotics Res..

[2]  Joachim Hertzberg,et al.  6D SLAM with Cached kd-tree Search , 2007, Robot Navigation.

[3]  Wesley H. Huang,et al.  SLAM with sparse sensing , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[4]  Stefano Carpin,et al.  USARSim: Providing a Framework for Multi-Robot Performance Evaluation | NIST , 2006 .

[5]  Wolfram Burgard,et al.  Probabilistic Robotics (Intelligent Robotics and Autonomous Agents) , 2005 .

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

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

[8]  Andrew Howard,et al.  Multi-robot mapping using manifold representations , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[9]  Stergios I. Roumeliotis,et al.  Weighted range sensor matching algorithms for mobile robot displacement estimation , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

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

[11]  Sanjiv Singh,et al.  An efficient on-line path planner for outdoor mobile robots , 2000, Robotics Auton. Syst..

[12]  Martin Rutishauser,et al.  Merging range images of arbitrarily shaped objects , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Gérard G. Medioni,et al.  Object modeling by registration of multiple range images , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[14]  Peter Cheeseman,et al.  On the Representation and Estimation of Spatial Uncertainty , 1986 .

[15]  Hanan Samet,et al.  The Quadtree and Related Hierarchical Data Structures , 1984, CSUR.

[16]  David Wettergreen,et al.  Real‐Time SLAM with Octree Evidence Grids for Exploration in Underwater Tunnels , 2007, J. Field Robotics.

[17]  Max Pfingsthorn,et al.  UvA-DARE ( Digital Academic Repository ) A scalable hybrid multi-robot SLAM method for highly detailed maps , 2007 .

[18]  Sebastian Thrun,et al.  Robotic mapping: a survey , 2003 .

[19]  Frank Wolter,et al.  Exploring Artificial Intelligence in the New Millenium , 2002 .