Point cloud simplification algorithm based on particle swarm optimization for online measurement of stored bulk grain

The simplification of 3D laser scanning point cloud is an important step of surface reconstruction and volume estimation of bulk grain in granary. This study presented an adaptive simplification algorithm based on particle swarm optimization (PSO). It introduced PSO into the average distance method, a conventional simplification method. The basic idea of this algorithm was to adaptively determine the optimal point reducing intervals of scanning lines according to original point cloud density by PSO. By using the 3D point cloud scanned from bulk grain surface in granary, the proposed algorithm was validated. Compared with the average distance method, the proposed algorithm obtained more evenly distributed point set, smaller reduction ratio (6.96%) and higher volume estimation accuracy (relative error was less than 3‰). The 3D laser scanner (GSLS003, Jilin University and SkyViTech Co., Ltd., Hangzhou, China) used in this study could scan the complete picture of the grain surface in a granary in one time, so the acquired point cloud data do not have to be jointed. For the good simplification performance and capability of updating the reducing interval at any moment, the proposed algorithm and the 3D laser scanner could be used to realize online real-time measurement of stored bulk grain volume in granary.