Improved likelihood models for probabilistic localization based on range scans

Range sensors are popular for localization since they directly measure the geometry of the local environment. Another distinct benefit is their typically high accuracy and spatial resolution. It is a well-known problem, however, that the high precision of these sensors leads to practical problems in probabilistic localization approaches such as Monte Carlo localization (MCL), because the likelihood function becomes extremely peaked if no means of regularization are applied. In practice, one therefore artificially smoothes the likelihood function or only integrates a small fraction of the measurements. In this paper we present a more fundamental and robust approach, that provides a smooth likelihood model for entire range scans. Additionally, it is location-dependent. In practical experiments we compare our approach to previous methods and demonstrate that it leads to a more robust localization.