构建了基于彩色相机和光学混合探测(PMD)相机的多源视觉系统，旨在建立具有真彩色信息的果树冠层三维点云模型，为果树的剪枝、疏花疏果和采摘等果园管理提供技术支持。针对PMD相机获取的目标场景三维点云，结合PMD相机的幅度图像和密度聚类算法提取有效点，利用前期研究的图像配准方法得到多源图像之间的坐标转换关系，完成了果树冠层多源信息融合。通过主成分分析法得到较好的初始位置，再采用最近点迭代算法，实现两组三维点云之间的拼接。对自然场景下的开花期和坐果期的果树冠层三维点云拼接方法进行了实验验证，结果表明多视角三维点云拼接误差为2.62 cm，可以较好地弥补单个角度下拍摄造成的数据缺失，实现了果树冠层完整的三维显示。

In order to guide the pruning, flower thinning and harvesting of fruit trees in orchard, a novel vision system which combines a color-camera system with a photo mixing detector (PMD)-camera is constructed. For the three-dimensional coordinate information of target scene acquired by the PMD camera, effective point cloud combining PMD amplitude image with density-based spatial clustering of applications with noise (DBSCAN) algorithm is extracted. Multi-source information fusion is completed with the result of image registration in the previous studies. Primary component analysis algorithm (PCA) is used to get the initial state of the point cloud at different locations, which is called prealignment. Accurate splice between two point clouds is realized by the iterative closest point (ICP) algorithm based on the least square method to get the optimal matching. Coordinate transformations are obtained by singular value decomposition (SVD) after prealigment and accurate splice. Several groups of experiments are used for verification, which show the average error of multi-view point cloud splicing reaches 2.62 cm and can better make up full three dimensional display of apple tree canopy without missing data than a single angle shot.