Improving computational and memory requirements of simultaneous localization and map building algorithms

Addresses the problem of implementing simultaneous localisation and map building (SLAM) in very large outdoor environments. A method is presented to reduce the computational requirement from ~O(N²) to ~O(N), N being the states used to represent all the landmarks and vehicle pose. With this implementation the memory requirements are also reduced to ~O(N). This algorithm presents an efficient solution to the full update required by the compressed extended Kalman filter algorithm. Experimental results are also presented