Abstract
In this paper, we present a new approach for continuous probabilistic mapping. The objective is to build metric maps of unknown environments through cooperation between multiple autonomous mobile robots. The approach is based on a Bayesian update rule that can be used to integrate the range sensing data coming from multiple sensors on multiple robots. In addition, the algorithm is fast and computationally inexpensive so that it can be implemented on small robots with limited computation resources. The paper describes the algorithm and illustrates it with experiments in simulation and on real robots.
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References
J. Borenstein, H. R. Everett, and L. Feng. Navigating Mobile Robots: Sensors and Techniques. A. K. Peters, Ltd., first edition, 1996.
J. Borenstein and Y. Koren. Histogramic in-motion mapping for mobile robot abstacle avoidance. IEEE Trans. on Robotics and Automation, 7(4):535–539, 1991.
Anthony R. Cassandra, L. Kaelbling, and J. Kurien. Acting under uncertainty: Discrete bayesian models for mobile-robot navigation. In IEEE/IROS Int. Conf. on Intelligent Robotics and Systems, 1996.
H. Choset. Sensor based motion planning: The hierarchical generalized Voronoi graph. PhD thesis, California Institute of Technology, USA, 1996.
Alberto Elfes. Occupancy grids: A probabilistic framework for mobile robot perception and navigation. PhD thesis, Electrical and Computer Engineering Dept., Carnegie Mellon Univ., 1989.
W. Burgard et al. The interactive museum tour-guide robot. In AAAI, Nat. Conf. on Artificial Intelligence, 1998.
Dieter Fox, W. Burgard, S. Thrun, and A. Cremers. Position estimation for mobile robots in dynamic environments. In AAAI, Nat. Conf. on Artificial Intelligence, 1998.
Wes Huang et al. CyberRAVE: A real and virtual environment for CyberScout, August 1998.
Martin C. Martin and Hans P. Moravec. Robot evidence grids. Technical Report CMU-RI-TR-96-06, Carnegie Mellon University, March 1996.
H.P. Moravec. Sensor fusion in certainty grids for mobile robots. AI Magazine, Summer:116–121, 1988.
Athanosios Papoulis. Probability, Random Variables, and Stochastic Processes. McGraw-Hill, third edition, 1991.
J. Salido, J.M. Dolan, J. Hampshire, and P. Khosla. A modified reactive control framework for cooperative mobile robots. In SPIE International Symposium on Sensor Fusion and Decentralized Control in Autonomous Robotic Systems, volume 3209, pages 90–100, October 1997.
Reid Simmons and S. Koenig. Probabilistic robot navigation in partially observable environments. In International Joint Conference on Artificial Intelligence, pages 1080–1087, August 1995.
S. Thrun, D. Fox, and W. Burgard. Probabilistic mapping of a environment by a mobile robot. In AAAI, Nat. Conf. on Artificial Intelligence, 1998.
Sebastian Thrun. Learning maps for indoor mobile robot navigation. AI Magazine, 1997.
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© 2000 Springer-Verlag London Limited
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Tercero, J.S., Paredis, C.J.J., Khosla, P.K. (2000). Continuous probabilistic mapping by autonomous robots. In: Experimental Robotics VI. Lecture Notes in Control and Information Sciences, vol 250. Springer, London. https://doi.org/10.1007/BFb0119406
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DOI: https://doi.org/10.1007/BFb0119406
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Publisher Name: Springer, London
Print ISBN: 978-1-85233-210-5
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