A segmentation method for LiDAR point clouds of aerial slender targets
LiDAR (Light Detection and Ranging) is an essential device for capturing the depth information of objects. Unmanned aerial vehicles (UAV) can sense the surrounding environment through LiDAR and image sensors to make autonomous flight decisions. In this process, aerial slender targets, such as overhe...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Physics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2025.1548786/full |
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| Summary: | LiDAR (Light Detection and Ranging) is an essential device for capturing the depth information of objects. Unmanned aerial vehicles (UAV) can sense the surrounding environment through LiDAR and image sensors to make autonomous flight decisions. In this process, aerial slender targets, such as overhead power lines, pose a threat to the flight safety of UAVs. These targets have complex backgrounds, elongated shapes, and small reflection cross-sections, making them difficult to detect directly from LiDAR point clouds. To address this issue, this paper takes overhead power line as a representative example of aerial slender targets and proposes a method that utilizes visible light images to guide the segmentation of LiDAR point clouds under large depth of field conditions. The method introduces an image segmentation algorithm based on a voting mechanism for overhead power lines and designs a calibration algorithm for LiDAR point clouds and images in the scenarios with large depth of field. Experimental results demonstrate that in various complex scenes, this method can segment the LiDAR point clouds of overhead power lines, thereby achieving accurate positions and exhibiting good adaptability across multiple scenes. Compared to traditional point cloud segmentation methods, the segmentation accuracy of the proposed method is significantly improved, promoting the practical application of LiDAR. |
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| ISSN: | 2296-424X |