为了解决三维点云在无序、数据被遮挡以及噪声干扰情况下的配准问题,提出了一种高维正交子空间映射的尺度点云配准算法。根据能量-功率的比值,对待配准点云进行等比例放大,完成仿射配准。在点云无序、数据被遮挡、尺寸放缩以及噪声干扰的情况下,所提算法与经典ICP(Iterative Closest Point)算法的配准精度相当。与经典ICP算法相比,所提算法对Bunny点云数据的配准效率提高了98%,对Dragon点云数据的配准速度至少提高了20倍,且在对大尺度Dragon点云数据的配准中,所提算法的配准时间比经典ICP算法短6210.4 s,配准精度也高于其他算法。所提算法不会陷入局部最小值,在快速精确配准和稳定性方面有明显的优势。

To solve the registration problem of a three-dimensional (3D) point cloud under disorder, data occlusion and noise disturbance, a scale point cloud registration algorithm in high-dimensional orthogonal subspace mapping is proposed. The point cloud to be registered is scaled up to complete the affine registration according to the energy-power ratio. The registration accuracy of the proposed algorithm is comparable to that of the classical iterative closest point (ICP)algorithm when the point cloud is out of order with data occluded, size scaled and noise disturbance. Compared with the classical ICP algorithm, the proposed algorithm improves the registration efficiency of the Bunny point cloud data by 98% and the registration speed of the Dragon point cloud data by at least 20 times. Moreover, in the registration of the large-scale Dragon point cloud data, the registration time of the proposed algorithm is 6210.4 s less than that of the classical ICP algorithm, and the registration accuracy is higher than those of other algorithms. The proposed algorithm does not fall into the local minimum and possesses obvious advantages in terms of fast and accurate registration and stability.