Active monte carlo localization in outdoor terrains using multi-level surface maps

In this paper we consider the problem of active mobile robot localization with range sensors in outdoor environments. In contrast to passive approaches our approach actively selects the orientation of the laser range finder to improve the localization results. It applies a particle filter to estimate the full six-dimensional state of the robot. To represent the environment we utilize multi-level surface maps which allow the robot to represent vertical structures and multiple levels. To efficiently calculate the optimal orientation for the range scanner, we apply a clustering operation on the particles and only evaluate potential orientations based on these clusters. Experimental results obtained with a mobile robot in an outdoor environment indicate that the active control of the range sensor leads to more efficient localization results.