中文摘要：

针对基于飞行时IN（Time—of-flight，TOF）原理的三维测距相机对物体完整表面进行三维点云建模中点云配准速度慢、精度低的问题，提出一种快速、易实现的散乱点云配准方法，该方法通过提取目标物体距离图像的特征点，采用非迭代的求解过程获取初始变换参数，实现点云初始位置配准。在此基础上，利用TOF相机强度图像的梯度值与基于局部3D空间分解的Knn算法寻找点云之间最邻近点作为匹配点对，根据原始迭代最近点算法的迭代过程对这些匹配点对进行迭代求解，获取点云之间最优的变换参数，同时结合错误匹配点对去除法则提高迭代点云匹配的精度，实现点云的精确位置配准。结合实际空间物体对该方法进行验证，试验结果表明，该点云配准方法与传统的配准方法相比，显著地提高配准速度和配准精度，将直接有助于提高后期物体曲面重建的精度，具有较高的实际应用价值。

英文摘要：

Against the problem of low accuracy, slow speed of point cloud registration in 3D point cloud modeling on the surface of the object based on the principle of time of flight（TOF） camera, a fast, easy method is proposed to realize the scattered points clouds registration, this method realize the point cloud initial position registration through extracting feature point from distance image of target objects, obtaining the initial transform parameter using an iterative solving process. Based on this, using gradient value of intensity image of TOF camera and K-nearest neighbor algorithm based on the local decomposition of 3D space to find the closest point between point cloud as the matching point pair. According to the iterative process of primitive iterative closest point（ICP） algorithm to iterate the matching point pair and obtain the best transform parameter in point cloud, at the same time, improving the precision of iterative point clouds registration combined with the error matching point removing rule, to realize the point cloud precise location registration. To verify the algorithm combined with the actual space objects, the experimental results show that this point cloud registration improved speed and the precision of registration compared with the traditional registration method, this will help to improve the precision of object surface reconstruction, have a high practical value.