Mobile laser scanning (MLS) is widely used in the mapping of forest environments. It has become important for extracting the parameters of forest trees using the generated environmental map. In this study, a three-dimensional point cloud map of a forest area was generated by using the Velodyne VLP-16 LiDAR system, so as to extract the diameter at breast height (DBH) of individual trees. The Velodyne VLP-16 LiDAR system and inertial measurement units (IMU) were used to construct a mobile measurement platform for generating 3D point cloud maps for forest areas. The 3D point cloud map in the forest area was processed offline, and the ground point cloud was removed by the random sample consensus (RANSAC) algorithm. The trees in the experimental area were segmented by the European clustering algorithm, and the DBH component of the tree point cloud was extracted and projected onto a 2D plane, fitting the DBH of the trees using the RANSAC algorithm in the plane. A three-dimensional point cloud map of 71 trees was generated in the experimental area, and estimated the DBH. The mean and variance of the absolute error were 0.43 cm and 0.50, respectively. The relative error of the whole was 2.27%, the corresponding variance was 15.09, and the root mean square error (RMSE) was 0.70 cm. The experimental results were good and met the requirements of forestry mapping, and the application value and significance were presented.

For the process of point cloud registration, and the problem of inaccurate registration due to errors in correspondence between keypoints. In this paper, a registration method based on calibration balls was proposed, the trunk, branch, and crown were selected as experimental objects, and three calibration balls were randomly placed around the experimental objects to ensure different distances between two ball centers. Using the Kinect V2 depth camera to collect the point cloud of the experimental scene from four different viewpoints, the PassThrough filter algorithm was used for point cloud filtering in each view of the experimental scenes. The Euclidean cluster extraction algorithm was employed for point cloud clustering and segmentation to extract the experimental object and the calibration ball. The random sample consensus (RANSAC) algorithm was applied to fit the point cloud of a ball and calculate the coordinates of the ball center so that the distance between two ball centers under different viewpoints can be obtained by using the coordinates of the ball center. Comparing the distance between the ball centers from different viewpoints to determine the corresponding relationship between the ball centers from different viewpoints, and then using the singular value decomposition (SVD) method, the initial registration matrix was obtained. Finally, Iterative Closest Point (ICP) and its improved algorithm were used for accurate registration. The experimental results showed that the method of point cloud registration based on calibration balls can solve the problem of corresponding error of keypoints, and can register point clouds from different viewpoints of the same object. The registration method was evaluated by using the registration running time and the fitness score. The final registration running time of different experimental objects was not more than 6.5 s. The minimum fitness score of the trunk was approximately 0.0001, the minimum fitness score of the branch was approximately 0.0001, and the minimum fitness score of the crown was approximately 0.0006. Keywords: point cloud registration, calibration balls, Kinect V2, ICP DOI: 10.25165/j.ijabe.20201301.5077 Citation: Zhou S Z, Kang F, Li W B, Kan J M, Zheng Y J. Point cloud registration for agriculture and forestry crops based on calibration balls using Kinect V2. Int J Agric & Biol Eng, 2020; 13(1): 198–205.