A Comparison of G2o Graph SLAM and EKF Pose Based SLAM with Bathymetry Grids

This paper address the Simultaneous Localization and Mapping (SLAM) problem of an AUV using bathymetric maps. The algorithm compounds swath profiles of the seafloor with DVL navigation(dead-reckoning) to build surface patches (3D point clouds). An initial guess of the location of these point clouds is known a priori by means of the dead-reckoning solution. Whenever there is a significant overlap of two or more point clouds, the corresponding surface patches are registered among themselves using a probabilistic ICP algorithm. The outcome of the registration procedure is a set of constrains defining the relative position of the overlapping surface patches. Next, these constrains are used to optimize a pose graph using the G2o optimizer. The results are compared against our prior EKF-pose-based SLAM solution. Our results suggest that a better performance is achieved using EKF global optimization with respect to the G2o graph-SLAM solution.