The main contribution of this paper is a new SLAM algorithm for the mapping of large scale environments by combining local maps. The local maps can be generated by traditional extended Kalman filter (EKF) based SLAM. Relationships between the locations of the landmarks in the local map are then extracted and used in an extended information filter (EIF) to build a global map. An important feature is that the information matrix for the global map is exactly sparse, leading to significant computational advantages. This paper thus presents an algorithm that combines the advantages of both the existing local map joining SLAM algorithms, which reduces the linearization error in EKF SLAM and allows computationally demanding global map fusion to be scheduled off-line, and the decoupled SLAM (D-SLAM) algorithm, which provides an efficient strategy for building large maps using relative location information. The effectiveness of the new algorithm is illustrated through computer simulations.
['Shoudong Huang', 'Zhan Wang', 'Gamini Dissanayake']
Mapping large scale environments using relative position information among landmarks