Improving Computational and Memory Requirements of Simultaneous Localization and Map Building Algorithms

This paper 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(N) to ~O(N), being N the states used to represent all the landmarks and vehicle pose. With this implementation the memory requirement are also reduced to ~O(N). This algorithm presents an efficient solution to the full update required by the Compressed Extended Kalman Filter algorithm (CEKF). Experimental results are also presented.