关键词: 激光雷达, 树冠检测, K-d tree模型, 自适应聚类

Abstract: In order to solve the problem of under-segmentation and over-segmentation such as missed detection and false detection of target objects under multi-size and multi-distance conditions in the process of Lidar detection under hilly and mountainous orchard conditions, a target object detection method based on adaptive target clustering radius of Lidar is proposed. Firstly, by using Lidar to sense the three-dimensional point cloud of the surrounding environment, the ground point cloud is removed and the preprocessing of down sample is performed by voxel filter. The amount of data is reduced and the noise points in the point cloud is removed. Secondly, the K-d tree model is established and the nearest neighbor search is carried out to accelerate the process of Euclidean clustering. By adaptively determining the clustering radius of each crown, the Euclidean clustering can get better clustering results. Finally, in order to verify the accuracy and practicability of the algorithm, based on the orchard tracked vehicle platform, a 32-line Lidar is used to test the algorithm. The results show that the algorithm can accurately cluster the canopy point cloud of fruit trees in hilly and mountainous orchards, and the field target detection rate is 94.41%.

Key words: Lidar, crown detection, K-dimensional tree model, adaptive clustering