XU Pengfei, MENG Hao, LI Tongfei, ZHENG Jinhai, LI Zhigang. A 3D Information Measuring Method of Underwater Targets Based on Line-Structured Light[J]. ROBOT, 2022, 44(5): 564-573. DOI: 10.13973/j.cnki.robot.210442
Citation: XU Pengfei, MENG Hao, LI Tongfei, ZHENG Jinhai, LI Zhigang. A 3D Information Measuring Method of Underwater Targets Based on Line-Structured Light[J]. ROBOT, 2022, 44(5): 564-573. DOI: 10.13973/j.cnki.robot.210442

A 3D Information Measuring Method of Underwater Targets Based on Line-Structured Light

  • For shortcomings of existing underwater detection methods such as poor accuracy, large calibration errors and low efficiency, a 3D information measuring method of underwater targets based on line-structured light is designed. Firstly, the principle of cross ratio invariance is utilized to solve the relative position of feature points in the world coordinate system, and then their real coordinate values in camera coordinates are solved based on both the direction vector of feature points on image plane and their position information. The structured light plane are calibrated using multiple feature points. The feasibility and effectiveness of the proposed method are verified by target scanning experiments. For calibrating the structured light plane, only one scan of the specially designed simple-structured stereo target is required, without the need for moving the target several times, which avoids the influence of human-induced errors. While calibrating the structured light plane, the displacement velocity of the displacement stage in each dimension can be calibrated simultaneously, which effectively reduces the system error of the experimental platform, improves the system accuracy and also greatly enhances the efficiency of the whole system calibration process. It is very suitable for the underwater field calibration of large-scale line-structured light system. It is proved that the error of the 3D point cloud coordinates obtained from scanning the underwater target is less than 2% along the X and Y axes, and the error along the Z axis is slightly larger, with an average error of only 2.77%.
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