Computer Science Department
School of Computer Science, Carnegie Mellon University


Robust Monte Carlo Localization for Mobile Robots

Sebastian Thrun, Dieter Fox, Wolfram Burgard, Frank Dellaert

April 2000

Keywords: Mobile robots, particle filters, localization, kernel density trees, sampling, Monte Carlo algorithms, randomized algorithms, Bayes filters

Mobile robot localization is the problem of determining a robot's pose from sensor data. Monte Carlo Localization is a family of algorithms for localization based on particle filters, which are approximate Bayes filters that use random samples for posterior estimation. Recently, they have been applied with great success for robot localization. Unfortunately, regular particle filters perform poorly in certain situations. Mixture-MCL, the algorithm described here, overcomes these problems by using a "dual" sampler, integrating two complimentary ways of generating samples in the estimation. To apply this algorithm for mobile robot localization, a kd-tree is learned from data that permits fast dual sampling. Systematic empirical results obtained using data collected in crowded public places illustrate superior performance, robustness, and efficiency, when compared to other state-of-the-art localization algorithms.

41 pages

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