## Monte Carlo Localization

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. Probabilistic approaches provide a comprehensive and real-time solution to the robot localization problem. However, the type of representation used to represent probability densities over the robot’s state space is crucially important. In 1999 we introduced the **Monte Carlo Localization** algorithm, where we represent the probability density involved by maintaining a set of samples that are randomly drawn from it, i.e., a particle filter. By using a sampling-based representation we obtain a localization method that can represent arbitrary distributions. The resulting method is able to efficiently localize a mobile robot without knowledge of its starting location, and it is faster, more accurate and less memory-intensive than earlier grid-based methods.