A computationally efficient solution to the simultaneous localisation and map building (SLAM) problem

The theoretical basis of the solution to the simultaneous localisation and map building (SLAM) problem where an autonomous vehicle starts in an unknown location in an unknown environment and then incrementally build a map of landmarks present in this environment while simultaneously using this map to compute absolute vehicle location is well understood. Although a number of SLAM implementations have appeared in the literature, the need to maintain the knowledge of the relative relationships between all the landmark location estimates contained in the map makes SLAM computationally intractable in implementations containing more than few tens of landmarks. In this paper, the theoretical basis and a practical implementation of a computationally efficient solution to SLAM is presented. The paper shows that it is indeed possible to remove a large percentage of the landmarks from the map without making the map-building process statistically inconsistent. Furthermore, it is shown that the efficiency of the SLAM can be maintained by judicious selection of landmarks, to be preserved in the map, based on their information content