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

This paper presents new algorithms to implement simultaneous localization and mapping in environments with very large numbers of features. The algorithms present an efficient solution to the full update required by the compressed extended Kalman filter algorithm. It makes use of the relative landmark representation to develop very close to optimal decorrelation solutions. With this approach, the memory and computational requirements are reduced from ∼O(N²) to ∼O(N^*N_a), N and N_a 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.