Monte Carlo Localization: Efficient Position Estimation for Mobile Robots

Dieter Fox, Carnegie Mellon University; Wolfram Burgard, University of Bonn; Frank Dellaert and Sebastian Thrun, Carnegie Mellon University

This paper presents a new, highly efficient algorithm for mobile robot localization, called Monte Carlo Localization. Mobile robot localization has been recognized as one of the most important problems in mobile robotics. Our algorithm is a version of Markov localization, a family of probabilistic approaches that have recently been applied with great practical success. However, previous approaches were either computationally extremely cumbersome (such as grid-based approaches that represent the state space by high-resolution 3D grids), or had to resort to extremely coarse-grained resolution. Our approach is computationally efficient while retaining the ability to represent (almost) arbitrary distributions. It applies highly efficient sampling-based methods for approximating probability distributions. This approach places computation exactly where needed. The number of samples is adapted on-line, thereby invoking large sample sets only when needed. Empirical results illustrate that Monte Carlo Localization is an extremely efficient on-line algorithm, characterized by better accuracy and an order of magnitude lower computation and memory requirement when compared to previous approaches. It is also much easier to implement.


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