Multibeam 3D Underwater SLAM with Probabilistic Registration

Multibeam 3D Underwater SLAM with Probabilistic Registration

Blog Article

This paper describes a pose-based underwater 3D Simultaneous Localization and Mapping (SLAM) using a multibeam echosounder to produce high consistency underwater maps.The proposed algorithm compounds swath profiles of the seafloor with dead reckoning localization to build surface patches (i.e.

, point clouds).An Iterative Closest Point (ICP) with a probabilistic implementation is then used to register the point Suncatcher Ornament clouds, taking into account their uncertainties.The registration process is divided in two steps: (1) point-to-point association for coarse registration and (2) point-to-plane association for fine registration.

The point clouds of the surfaces to be registered are sub-sampled in order to decrease both the computation time and also the potential of falling into local minima during the registration.In addition, a heuristic is used to decrease the complexity of the association step of the ICP from O ( Evaporator Cover Support n 2 ) to O ( n ).The performance of the SLAM framework is tested using two real world datasets: First, a 2.

5D bathymetric dataset obtained with the usual down-looking multibeam sonar configuration, and second, a full 3D underwater dataset acquired with a multibeam sonar mounted on a pan and tilt unit.