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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,.