Research on Surface Target Detection Algorithm Based on 3D Lidar
3D Lidar is the key perception module of Unmanned Surface Vehicle (USV). Targets in the background of the water are affected by refracted light. The visual sensor is difficult to detect in special scenes, which affects the autonomous navigation and obstacle avoidance function. This paper proposes a...
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Published in | 2021 International Conference on Security, Pattern Analysis, and Cybernetics(SPAC pp. 489 - 494 |
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Main Authors | , , , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
18.06.2021
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Subjects | |
Online Access | Get full text |
DOI | 10.1109/SPAC53836.2021.9539991 |
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Abstract | 3D Lidar is the key perception module of Unmanned Surface Vehicle (USV). Targets in the background of the water are affected by refracted light. The visual sensor is difficult to detect in special scenes, which affects the autonomous navigation and obstacle avoidance function. This paper proposes a 3D lidar-based VoxelNet detection algorithm for water surface targets. The sparse point cloud data on the water surface is divided into voxel form, and the hash table is input for efficient query, and the feature tensor is extracted through the feature learning layer and input into the convolutional layer to obtain the global target Information to achieve high-precision target detection. Experimental results show that the surface target detection algorithm based on 3D lidar improves 13.6% compared with the visual solution, which provides a more effective technical means for the intelligent process of USV. |
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AbstractList | 3D Lidar is the key perception module of Unmanned Surface Vehicle (USV). Targets in the background of the water are affected by refracted light. The visual sensor is difficult to detect in special scenes, which affects the autonomous navigation and obstacle avoidance function. This paper proposes a 3D lidar-based VoxelNet detection algorithm for water surface targets. The sparse point cloud data on the water surface is divided into voxel form, and the hash table is input for efficient query, and the feature tensor is extracted through the feature learning layer and input into the convolutional layer to obtain the global target Information to achieve high-precision target detection. Experimental results show that the surface target detection algorithm based on 3D lidar improves 13.6% compared with the visual solution, which provides a more effective technical means for the intelligent process of USV. |
Author | Zhou, Zhiguo Di, Shunfan Zhao, Wang Cao, Jiangwei Li, Yiyao Ailaterini, Melliou |
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Snippet | 3D Lidar is the key perception module of Unmanned Surface Vehicle (USV). Targets in the background of the water are affected by refracted light. The visual... |
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SubjectTerms | 3D lidar Data mining Feature extraction Laser radar Navigation Object detection Target detection Three-dimensional displays USV Visualization VoxelNet |
Title | Research on Surface Target Detection Algorithm Based on 3D Lidar |
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