Monte Carlo localization for mobile robots

To navigate reliably in indoor environments, a mobile robot must know where it is. Thus, reliable position estimation is a key problem in mobile robotics. We believe that probabilistic approaches are among the most promising candidates to providing a comprehensive and real-time solution to the robot localization problem. However, current methods still face considerable hurdles. In particular the problems encountered are closely related to the type of representation used to represent probability densities over the robot's state space. Earlier work on Bayesian filtering with particle-based density representations opened up a new approach for mobile robot localization based on these principles. We introduce the Monte Carlo localization method, where we represent the probability density involved by maintaining a set of samples that are randomly drawn from it. By using a sampling-based representation we obtain a localization method that can represent arbitrary distributions. We show experimentally that the resulting method is able to efficiently localize a mobile robot without knowledge of its starting location. It is faster, more accurate and less memory-intensive than earlier grid-based methods,.

[1]  R. S. Bucy,et al.  Bayes Theorem and Digital Realizations for Non-Linear Filters , 1969 .

[2]  J. E. Handschin.  Monte Carlo techniques for prediction and filtering of non-linear stochastic processes , 1970 .

[3]  John J. Leonard,et al.  Directed Sonar Sensing for Mobile Robot Navigation , 1992 .

[4]  Alan E. Gelfand,et al.  Bayesian statistics without tears: A sampling-resampling perspective , 1992 .

[5]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[6]  Bernt Schiele,et al.  A comparison of position estimation techniques using occupancy grids , 1994, Robotics Auton. Syst..

[7]  Bernt Schiele,et al.  A comparison of position estimation techniques using occupancy grids , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[8]  Reid G. Simmons,et al.  Probabilistic Robot Navigation in Partially Observable Environments , 1995, IJCAI.

[9]  Illah R. Nourbakhsh,et al.  DERVISH - An Office-Navigating Robot , 1995, AI Mag..

[10]  G. Kitagawa.  Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models , 1996 .

[11]  Wolfram Burgard,et al.  Estimating the Absolute Position of a Mobile Robot Using Position Probability Grids , 1996, AAAI/IAAI, Vol. 2.

[12]  Michael Isard,et al.  Contour Tracking by Stochastic Propagation of Conditional Density , 1996, ECCV.

[13]  Leslie Pack Kaelbling,et al.  Acting under uncertainty: discrete Bayesian models for mobile-robot navigation , 1996, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS '96.

[14]  J.-S. Gutmann,et al.  AMOS: comparison of scan matching approaches for self-localization in indoor environments , 1996, Proceedings of the First Euromicro Workshop on Advanced Mobile Robots (EUROBOT '96).

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

[16]  Michael Isard,et al.  A mixed-state condensation tracker with automatic model-switching , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[17]  Wolfram Burgard,et al.  An experimental comparison of localization methods , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

[18]  Wolfram Burgard,et al.  Integrating global position estimation and position tracking for mobile robots: the dynamic Markov localization approach , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

[19]  M. Pitt,et al.  Filtering via Simulation: Auxiliary Particle Filters , 1999 .

[20]  P. Fearnhead,et al.  An improved particle filter for non-linear problems , 1999 .