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With the maturity of LiDAR technology, LiDAR point cloud segmentation has been widely applied in automatic inspection of power lines. However, in weather scenarios such as rain, snow, and dust, lidar data noise and dynamically changing power line data can be generated, resulting in a decrease in power line extraction efficiency. The commonly used airborne LiDAR can only work in sunny weather conditions, and in order to improve the accuracy of point cloud power line extraction, the data collected on board needs to be preprocessed to obtain complete scene point cloud data, which cannot meet the requirement of automatic inspection of power lines.In order to solve the problems of real-time monitoring of airborne LiDAR data and low accuracy in extracting power line point clouds under different weather scenarios, this paper proposes a point cloud power line extraction method based on the improved DBSCAN algorithm, starting from the data features of fixed LiDAR real-time scanning point clouds. Firstly, the cloth filtering method is used to filter out ground points and obtain non ground point clouds; On this basis, based on the spatial relative density characteristics of non-ground point clouds, target data such as traverse points and tower points are roughly extracted from non-ground points; Then, the distribution characteristics of the elevation point cloud are used to identify the tower, and the maximum width of the tower is used to segment the power lines within the range of the tower. Then, based on the data characteristics of the point cloud, the density clustering parameters are continuously modified to further improve the accuracy of power line point cloud segmentation. In order to verify the effectiveness of the algorithm, point cloud power line point cloud segmentation experiments were conducted in different meteorological environments, and compared with European clustering segmentation and regional growth algorithms. The experimental results show that the improved DBSCAN algorithm proposed in this paper has the best segmentation performance for power line point clouds in complex weather scenarios, which is basically consistent with sunny conditions and can meet the actual power inspection needs.
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