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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Bibliographic Details
Published in2021 International Conference on Security, Pattern Analysis, and Cybernetics（SPAC pp. 489 - 494
Main Authors Zhou, Zhiguo, Li, Yiyao, Cao, Jiangwei, Di, Shunfan, Zhao, Wang, Ailaterini, Melliou
Format Conference Proceeding
LanguageEnglish
Published IEEE 18.06.2021
Subjects
3D lidar
Data mining
Feature extraction
Laser radar
Navigation
Object detection
Target detection
Three-dimensional displays
USV
Visualization
VoxelNet
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DOI10.1109/SPAC53836.2021.9539991

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Summary: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.
DOI:10.1109/SPAC53836.2021.9539991