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| Research on underground obstacle dynamic detection based on LiDAR |
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Received:April 26, 2022
Revised:May 11, 2022
Accepted:May 12, 2022
Published Online:February 23, 2023
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| DOI： |
| KeyWord:Obstacle detection; Filtering; Segmentation; Clustering; Bounding box; Intelligent mine |
| Author | Institution |
| WANG Zilin |
Beijing General Research Institute of Mining and Metallurgy |
| ZHANG Da |
BGRIMM Technology Group |
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| Abstract: |
| Aiming at the problem that environmental obstacles such as underground pedestrian vehicles, support facilities, pipe network and cables seriously affect the flight safety of underground UAV, in order to prevent flight faults and equipment damage, this paper proposes a dynamic identification method of underground obstacle detection, which uses airborne LiDAR to scan the underground environment online and obtain high-resolution point cloud data. The method jointly realize the effective real-time identification of underground environmental obstacles through the preprocessing method based on point cloud filtering, the point cloud segmentation method based on random sampling consistency (RANSAC), the point cloud clustering method based on Euclidean clustering and the bounding box method based on principal component analysis (PCA). The obstacle recognition rate is up to 85%, which effectively ensures the safety of underground UAV flight detection operation and can better serve the construction of Intelligent Mine and mine safety rescue. |
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