针对点云配准中匹配精度低和算法收敛速度慢等问题,提出一种基于二维图像特征和奇异值分解(SVD)的点云配准算法。先将三维点云转换成二维方位角(BA)图像,利用基于内部距离的形状上下文(IDSC)算法对BA图像进行配准;再根据三维点和二维像素的一对一映像关系计算三维点云的刚体变换,从而实现两个点云的初始粗配准;最后采用基于SVD的迭代最近点(ICP)算法对点云进行进一步精配准,从而实现点云的最终精确配准。实验采用公共点云、颅骨点云和文物点云数据验证所提配准算法的配准性能,结果表明所提算法是一种快速和高精度的点云配准算法。

To solve the problems of low matching accuracy and slow convergence speed in point cloud registration, a point cloud registration algorithm based on two-dimensional (2D) image features and singular value decomposition (SVD) is proposed. First, a three-dimensional (3D) point cloud was transformed into a 2D bearing angle (BA) image and the BA image was registered using the internal-distance shape context (IDSC) algorithm. Then, using the one-to-one mapping relationship between the 3D point cloud and the 2D pixel, the rigid body transformation of the 3D point cloud was calculated to achieve the rough registration of the two point clouds. Finally, the iterative closest point (ICP) algorithm based on SVD was used to accurately register the two point clouds. In the experiment, the proposed registration algorithm was validated using public point cloud, skull point cloud, and cultural relics point cloud data. Results show that the proposed algorithm is a fast and high-precision point cloud registration algorithm.