Radar Point Cloud Clustering Method Based on Optimization

Autonomous navigation has become a trend in the development of ships, and the realization of automatic obstacle avoidance plays an important role. Radar is an indispensable sensor for ships, and it provide information on the location and distance of water surface obstacles. Based on the cluster analysis of radar point cloud data, the number of water surface obstacles is obtained. Use the information to adjust the output threshold of the obstacle detection network. Realize the detection and classification of obstacle targets. Considering the reduction of time complexity, the DBSCAN algorithm based on KD tree acceleration can be used, where the KD tree parameter selection determines the clustering effect. This paper proposes to use flower pollination optimization algorithm to determine the two parameters to ensure higher clustering accuracy. This method can find the appropriate optimal parameters in the parameter space, which can make the performance of the DBSCAN clustering algorithm based on KD tree acceleration the best.