CI-Graph: An efficient approach for large scale SLAM

When solving the Simultaneous Localization and Mapping (SLAM) problem, submapping and graphical methods have shown to be valuable approaches that provide significant advantages over the standard EKF solution: they are faster and can produce more consistent estimates when using local coordinates. In this paper we present CI-Graph, a submapping method for SLAM that uses a graph structure to efficiently solve complex trajectories reducing the computational cost. Unlike other submapping SLAM approaches, we are able to transmit and share information through maps in the graph in a consistent manner by using conditionally independent submaps. In addition, the current submap always summarizes, without further computations, all information available making CI-Graph be an intrinsically “up to date” algorithm. Moreover, the technique is also efficient in memory requirements since it does not need to recover the full covariance matrix. To evaluate CI-Graph performance, the method has been tested using a synthetic Manhattan world and Victoria Park data set.