The effect analysis of point cloud registration of soybean plant from multiple perspectives

Abstract. In order to reconstruct the three-dimensional model of the soybean rapidly and efficiently, this paper analyzes the registration effect of point clouds of soybean plants from multiple angles. First, the Kinect V2 camera is used to obtain the point cloud information of soybean plants at 24 different angles, which means that, the images are taken at intervals of 15 degree angles. Second, the obtained point cloud information is subjected to preprocessing operations such as straight through filtering and outlier filtering. Thirdly, two representative point cloud registration algorithms are investigated, which include local features based FPFH (Fast point feature histograms) and probability distribution based NDT (Normal distributions transform). Meanwhile, the FPFH-ICP and NDT-ICP are used to perform pairwise registration for point cloud data at different perspectives. Finally, time cost and accuracy of the above two registration strategies are compared and analyzed. The experimental results show that the NDT-ICP soybean plant point cloud registration algorithm is superior to that of FPFH-ICP in terms of time-consuming, registration accuracy, and adaptation at different angles. At the same time, it is found that when the image acquisition interval between two point clouds is less than or equal to 45 degrees, the adaptability to different point cloud registration algorithms is more robust, and the registration error is less.