A sparse weight Kalman filter approach to simultaneous localisation and map building

This paper describes a sparse weight Kalman filter algorithm for simultaneous localisation and map building (SLAM). This algorithm trades optimality for a form of the weight equation which confers computational advantages. For a map of n beacons, the storage is O(n/sup 2/) and the computational costs are O(n). We show that, in a simulation, the method yields results which are similar to the optimal Kalman filter and the suboptimal update method proposed by Guivant et al. (2000).