Multibeam 3D Underwater SLAM with Probabilistic Registration
Vicorob Research Institute, Universitat de Girona, c/Pic de Peguera 13-Parc Científic i Tecnològic de la UdG-CIRS Building, Girona l17003, Spain
Author to whom correspondence should be addressed.
Academic Editor: Jaime Lloret Mauri
Received: 15 January 2016 / Revised: 13 April 2016 / Accepted: 14 April 2016 / Published: 20 April 2016
PDF [9494 KB, uploaded 20 April 2016]
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 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
. 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.
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MDPI and ACS Style
Palomer, A.; Ridao, P.; Ribas, D. Multibeam 3D Underwater SLAM with Probabilistic Registration. Sensors 2016, 16, 560.
Palomer A, Ridao P, Ribas D. Multibeam 3D Underwater SLAM with Probabilistic Registration. Sensors. 2016; 16(4):560.
Palomer, Albert; Ridao, Pere; Ribas, David. 2016. "Multibeam 3D Underwater SLAM with Probabilistic Registration." Sensors 16, no. 4: 560.
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