Research on LiDAR point cloud filtering algorithm for unmanned aerial vehicles
To address the issue of limited point cloud filtering accuracy due to the randomness in selecting ground seed points by the classical progressive triangulated irregular network(TIN)densification(PTD)filtering algorithm,this paper proposed an improved PTD(IPTD)filtering algorithm.In the implementation of airborne point cloud filtering,the key processes of this algorithm were as follows:Firstly,the extended local minimum method was used to conduct a deep search within the grid and precisely identify the minimum points as potential ground seed points,thus overcoming the limitation of traditional algorithms that directly take the lowest point within the grid as the ground seed point.Secondly,the local thin plate spline(TPS)function interpolation method was adopted to achieve high-precision fitting and calculation of the elevation data within the grid.Finally,based on the screened high-quality seed points,a TIN was constructed to achieve an accurate classification of the point clouds.To verify the effectiveness of the proposed improved filtering algorithm,a comparative experiment was conducted to compare the experimental results of the improved filtering algorithm with those of the traditional filtering algorithm.The results show that the proposed improved filtering algorithm exhibits significant advantages in improving the accuracy of point cloud filtering.
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