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. 2016 Apr 20;16(4):560.
doi: 10.3390/s16040560.

Multibeam 3D Underwater SLAM with Probabilistic Registration

Albert Palomer  1 , Pere Ridao  2 , David Ribas  3
Affiliations

Multibeam 3D Underwater SLAM with Probabilistic Registration

Albert Palomer et al. Sensors (Basel). .

Abstract

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 O(n2) 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.

Keywords: 3D; AUV; SLAM; bathymetry; multibeam.

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Figures

Figure 1
Figure 1
Two different submaps colored according to depth. In (a), the multibeam was mounted in a fixed downward-looking configuration, typically from bathymetric mapping; In (b), the sonar head was mounted on a pan and tilt unit and swept vertically to cover a portion of steep terrain.
Figure 2
Figure 2
Example of the dead reckoning (a) and the scan forming (b).
Figure 3
Figure 3
Point-to-point and point-to-plane comparison in the presence of small displacement (a) and large displacement (b). In blue, the points of the reference scan with the plane, in red, the points of the new scan. Green arrows correspond to point-to-point association while black ones represent point-to-plane.
Figure 4
Figure 4
A 3D visual concept of the idea behind octree use: two surfaces to be matched (a); the same surfaces resampled (b).
Figure 5
Figure 5
The contribution of octree in point resampling: octree construction, top view (a); points after resampling (b).
Figure 6
Figure 6
The figure shows how the support grid is used during the association step. First, the points in the reference scan (blue) are inserted into the cells using their uncertainty ellipses (a). Then, each point in the new scan (red) is also laid inside the grid (b). In this case, n1 overlaps with r2 and r3 while n2 overlaps only with r3. Moreover, r1 has no potential associations.
Figure 7
Figure 7
Patch number 13 overlaps with patches 1, 2, 6, 8, 9 and 10. For improving the matching process, patches that are consecutive (1 and 2 as well as 8 and 9) are merged. This results in four patches taking part in the matching process.
Figure 8
Figure 8
Bathymetric maps of the area. The color goes from deep (dark blue) to shallow (dark red). The bathymetry is gridded at 0.5 m. (a) Dead Reckoning; (b) SLAM.
Figure 9
Figure 9
Consistency-based error maps. The error is color plotted from low (dark blue) to high (dark red) with 0.5 m grid resolution. (a) Dead Reckoning; (b) SLAM.
Figure 10
Figure 10
The Girona 500 AUV in the water tank with the configuration used for the experiments. The multibeam sonar and the pan and tilt unit can be seen at the lower-right side of the vehicle facing in two different directions. In (a) the multibeam is tilted at a pitch of around 45° while in (b) it is in a downward-looking position.
Figure 11
Figure 11
Trajectory of the experiment over the Google Maps image of the St. Feliu de Guíxols harbor.
Figure 12
Figure 12
Top view of the 3D reconstruction of St. Feliu Harbor using dead reckoning navigation (a), usbl-aided navigation (b) and the proposed SLAM algorithm (c).The bottom part of the model is the vertical wall of the pier. Under each view, written inside parentheses, the number of cells occupied by each model’s point clouds can be observed. The meshes are reconstructed using [38] and colored according to the depth (deeper parts are in blue, shallower ones in red).
Figure 13
Figure 13
Zoom in the highlighted area of Figure 12. First row (a-c) shows the point clouds and the reconstructed meshes. Second (d–f) and third (g–i) rows show the mesh and point clouds respectively. The columns, from left to right are related to the results obtained with: (1) dead reckoning (a, d and g); (2) USBL-aided (b, e and h) and (3) proposed approach (c, f and i).
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