Solving computational and memory requirements of feature-based simultaneous localization and mapping algorithms

This paper presents new algorithms to implement simultaneous localisation and map building (SLAM) in environments with very large number of features. The algorithms present an efficient solution to the full update required by the Compressed Extended Kalman Filter algorithm (CEKF). It makes uses of the Relative Landmark Representation (RLR) to develop very close to optimal de-correlation solutions. With this approach the memory and computational requirements are reduced from ~O(N) to ~O(N*Nb), being N and Nb proportional to the number of features in the map and features close to the vehicle respectively. Experimental results are presented to verify the operation of the system when working in large outdoor environments.