RSE-lab Robot Localization

Animations related to robot localization can be found on our MCL in action web site.

Project Contributors

Dieter Fox, Cody Kwok, Marina Meila

Main publications

• Map-based Multiple Model Tracking of a Moving Object.
  C. Kwok and D. Fox. Robocup Symposium 2004. RoboCup Scientific Challenge Award.
• Adapting the sample size in particle filters through KLD-sampling.
  D. Fox. International Journal of Robotics Research IJRR, 2003.
• The Revisiting Problem in Mobile Robot Map Building: A Hierarchical Bayesian Approach.
  B. Stewart, J. Ko, D. Fox, and K. Konolige. UAI-03.
• Real-time particle filters.
  C. Kwok, D. Fox, and M. Meila. Proceedings of the IEEE, 2004.
• An experimental comparison of localization methods continued.
  J.-S. Gutmann and D. Fox. IROS-02.
• A Probabilistic Approach to Collaborative Multi-Robot Localization.
  D. Fox, W. Burgard, H. Kruppa, and S. Thrun. Auonomous Robots, 8 (3), 2000. Also appeared in Robot Teams: From Diversity to Polymorphism, AK Peters, 2002.
• Particle filters for mobile robot localization.
  D. Fox, S. Thrun, F. Dellaert, and W. Burgard. Sequential Monte Carlo Methods in Practice. Springer Verlag, New York, 2000.
• Markov Localization for Mobile Robots in Dynamic Environments.
  D. Fox, W. Burgard, and S. Thrun. Journal of Artificial Intelligence Research (JAIR), 11, 1999.
• Active markov localization for mobile robots.
  D. Fox, W. Burgard, and S. Thrun. Robotics and Autonomous Systems (RAS), 25:195-207, 1998.
• Monte carlo localization: Efficient position estimation for mobile robots.
  D. Fox, W. Burgard, F. Dellaert, and S. Thrun. AAAI-99.
• Using the condensation algorithm for robust, vision-based mobile robot localization.
  F. Dellaert, W. Burgard, D. Fox, and S. Thrun. CVPR-99.
• Coastal navigation: Mobile robot navigation with uncertainty in dynamic environments.
  N. Roy, W. Burgard, D. Fox, and S. Thrun. ICRA-99.
• An experimental comparison of localization methods.
  J.-S. Gutmann, W. Burgard, D. Fox, and K. Konolige. IROS-98. Best paper award.
• Integrating global position estimation and position tracking for mobile robots: the Dynamic Markov Localization approach.
  W. Burgard, A. Derr, D. Fox, and A.B. Cremers. IROS-98.
• Estimating the Absolute Position of a Mobile Robot Using Position Probability Grids.
  W. Burgard, D. Fox, D. Hennig, and T. Schmidt. AAAI-96.