Sonar-Based Mapping With Mobile Robots Using EM
This paper presents an algorithms for learning occupancy grid maps with mobile robots equipped with range finders, such as sonar sensors. Our approach employs the EM algorithm to solve the concurrent mapping and localization problem. To accommodate the spatial nature of range data, it relies on a two-layered representation of maps, where global maps are composed from a collection of small, local maps. To avoid local minima during likelihood maximization, a softmax version of the M step is proposed that is gradually annealed to the exact maximum. Experimental results demonstrate that our approach is well suited for constructing large maps of typical indoor environments using sensors as inaccurate as sonars.