Robust Monte-Carlo Localization Using Adaptive Likelihood Models

Abstract

In probabilistic mobile robot localization, the development of the sensor model plays a crucial role as it directly influences the efficiency and the robustness of the localization process. Sensor models developed for particle filters compute the likelihood of a sensor measurement by assuming that one of the particles accurately represents the true location of the robot. In practice, however, this assumption is often strongly violated, especially when using small sample sets or during global localization. In this paper we introduce a novel, adaptive sensor model that explicitly takes the limited representational power of particle filters into account. As a result, our approach uses smooth likelihood functions during global localization and more peaked functions during position tracking. Experiments show that our technique significantly outperforms existing, static sensor models.