A Point Cloud Registration Pipeline using Gaussian Process Regression for Bathymetric SLAM

Abstract

Point cloud registration is a means of achieving loop closure correction within a simultaneous localization and mapping (SLAM) algorithm. Data association is a critical component in point cloud registration, and can be very challenging in feature-depleted environments such as seabed. This paper presents a point cloud registration pipeline for performing loop closure correction in feature-depleted subsea environments using data collected from an optical scanner. The pipeline uses Gaussian process regression to extract keypoint sets, and a weighted network alignment algorithm to propose point correspondences. A variant of the iterative closest point (ICP) registration algorithm is used to perform fine alignment, with point correspondences informed by the mappings determined following the network alignment step. The developed registration pipeline is deployed with success on a challenging section of field data containing topography that cannot be resolved using conventional imaging sonar.