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JRM Vol.25 No.2 pp. 364-374
doi: 10.20965/jrm.2013.p0364
(2013)

Paper:

A Method for Obtaining High-Coverage 3D Images of Rough Seafloor Using AUV – Real-Time Quality Evaluation and Path-Planning –

Ayaka Kume, Toshihiro Maki, Takashi Sakamaki, and Tamaki Ura

Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan

Received:
October 12, 2012
Accepted:
February 15, 2013
Published:
April 20, 2013
Keywords:
AUV, real-time path-planning, 3D image mapping, next-best-view problem
Abstract

Autonomous Underwater Vehicles (AUVs) are often used for seafloor exploration, and some AUVs are now being deployed to obtain detailed photomosaics of the seafloor. However, it is difficult for the results to be evaluated on-site, so the image maps obtained often have unscanned areas caused by occlusions, disturbances, etc. In order to improve the coverage of a map, operators have to plan a new path and then redeploy the AUV. This process is quite timeconsuming and troublesome. The authors propose a new method for an AUV to obtain a full-coverage 3D image of a rough, unknown seafloor in a single deployment. First, the AUV observes the seafloor by following a pre-determined path. Second, the AUV calculates the following on-site and based on the data obtained: 3D bathymetry map, unscanned areas on the map, and the next path that can be taken to image the unscanned areas effectively. Then, the AUV follows the new path to obtain better results. The performance of this proposed method is verified in both tank experiments and by simulation. In the experiments, the AUV “Tri-TON” succeeds in generating a route for a second observation, and the coverage increases from 73% to 82%. The performance of the method on the actual seafloor is verified using the results of the tank experiments and the bathymetry data on a chimney in Kagoshima Bay, Japan.

Cite this article as:
A. Kume, T. Maki, T. Sakamaki, and T. Ura, “A Method for Obtaining High-Coverage 3D Images of Rough Seafloor Using AUV – Real-Time Quality Evaluation and Path-Planning –,” J. Robot. Mechatron., Vol.25, No.2, pp. 364-374, 2013.
Data files:
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Last updated on Jul. 23, 2019