LiDAR K-means clustering algorithm based on threshold

For this question of low clustering accuracy problem in LiDAR full-waveform echo data with different targets at the same distance, a threshold-based K-means clustering algorithm was proposed based on the analysis of K-means clustering algorithm. Firstly, The distance information was calibrated using the intensity information, and the intensity information was used as a feature to distinguish different targets at the same distance by clustering. Secondly, the threshold was used to define the minimum distance between clustering centers to improve the clustering accuracy. Finally, the scanning verification platform was built for translation and rotation imaging to verify the effectiveness of the algorithm. The clustering experiments of different color targets and simulated road echo data show that the clustering accuracy rate of threshold-based K-means clustering algorithm is above 90% under different thresholds, and increases more than 10% compared with the threshold-free K-means clustering algorithm, which can effectively perform target clustering and simulation road extraction.