Hubei Key Laboratory of Modern Manufacturing Quantity Engineering, School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, China.
Sensors (Basel). 2022 Aug 5;22(15):5850. doi: 10.3390/s22155850.
Spherical targets are widely used in coordinate unification of large-scale combined measurements. Through its central coordinates, scanned point cloud data from different locations can be converted into a unified coordinate reference system. However, point cloud sphere detection has the disadvantages of errors and slow detection time. For this reason, a novel method of spherical object detection and parameter estimation based on an improved random sample consensus (RANSAC) algorithm is proposed. The method is based on the RANSAC algorithm. Firstly, the principal curvature of point cloud data is calculated. Combined with the k-d nearest neighbor search algorithm, the principal curvature constraint of random sampling points is implemented to improve the quality of sample points selected by RANSAC and increase the detection speed. Secondly, the RANSAC method is combined with the total least squares method. The total least squares method is used to estimate the inner point set of spherical objects obtained by the RANSAC algorithm. The experimental results demonstrate that the method outperforms the conventional RANSAC algorithm in terms of accuracy and detection speed in estimating sphere parameters.
球形目标在大型组合测量的坐标统一中得到了广泛应用。通过其中心坐标，可以将来自不同位置的扫描点云数据转换为统一的坐标参考系。然而，点云球检测存在误差和检测时间慢的缺点。为此，提出了一种基于改进的随机抽样一致性（RANSAC）算法的球形目标检测和参数估计的新方法。该方法基于 RANSAC 算法。首先，计算点云数据的主曲率。结合 k-d 最近邻搜索算法，对随机采样点的主曲率约束进行了实现，以提高 RANSAC 选择的样本点的质量，提高检测速度。其次，将 RANSAC 方法与总体最小二乘法相结合。总体最小二乘法用于估计 RANSAC 算法获得的球形物体的内点集。实验结果表明，该方法在估计球体参数方面的准确性和检测速度均优于传统的 RANSAC 算法。
