Fast Monte Carlo Localization using spatial density information

Estimating the robot localization is a fundamental requirement for applications in robotics. For many years, Monte Carlo Localization (MCL) has been one of the most popular approaches to solve the global localization when using range finders, like sonars or lasers. It generally weights the estimates about the robot state by comparing raw sensor readings with simulated readings computed for each estimate. In this paper, we propose an observation model for localization that associates a kernel density estimate (KDE) to each point in the space. This single-valued density measure is independent of orientation, what allows an efficient pre-caching step, substantially boosting the computation time of the process. Using the gradient of the densities field, our strategy is able to estimate orientation information that helps to restrict the localization search space. Additionally, we can combine densities obtained by kernels of different sizes and profiles to improve the quality of the acquired information. We show through experiments in comparison with traditional approaches that our method is efficient, even working with large sets of particles, and effective.