针对现有点云配准算法不能很好地同时解决点云模型变尺度和配准精度等问题,提出一种变尺度的两阶段点云模型配准算法。第一阶段加入动态的尺度因子,粗略估计并调整目标点云模型的尺度;然后将空间旋转变换三个角度进行格点划分,以30°为格点间距,这提高了算法的收敛速度并避免陷入局部最优,为第二阶段配准提供良好的初始位置。在尺度迭代最近点(SICP)算法基础上对第二阶段进行优化,以此对点云模型进行更加精准的匹配。对不同配准算法进行了综合对比实验,结果表明,在两个点云模型间存在较大刚体变换且尺度显著不同的情况下,所提算法的配准误差数量级为10 -30~10 -4。

Existing point cloud registration algorithms cannot solve problems of variable scale and registration accuracy of point cloud models simultaneously. Hence, this paper proposes a two-stage variable scale point cloud model registration algorithm. In the first stage of the algorithm, a dynamic scale factor is added to approximately estimate and adjust the scale of the target point cloud model. Spatial rotation transformation is then performed at three angles to divide the grid points, and the grid point spacing is set to 30°. This improves the convergence speed of the algorithm and prevents a local optimum, thus providing a good initial position for the second stage of registration. The second stage is optimized based on a scale iterative closest point (SICP) algorithm to match the point cloud model more precisely. A comprehensive comparison experiment is performed on different registration algorithms, and the experimental results show that in the case where there is a large rigid body transformation between two point cloud models and the scales are significantly different, the proposed algorithm has an order of magnitude of registration error of 10 ^-30--10 ^-4.