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.