基于Kinect的点云配准方法

Kinect采集的点云存在点云数量大、点云位置有误差,直接使用迭代最近点(ICP)算法对点云进行配准时效率低.针对该问题,提出一种基于特征点法向量夹角的改进点云配准算法.首先使用体素栅格对Kinect采集的原始点云进行下采样,精简点云数量,并使用滤波器移除离群点.然后使用SIFT算法提取目标点云与待配准点云公共部分的特征点,通过计算特征点法向量之间的夹角调整点云位姿,完成点云的初始配准.最后使用ICP算法完成点云的精细配准.实验结果表明,该算法与传统ICP算法相比,在保证点云配准精度的同时,能够提高点云的配准效率,具有较高的适用性和鲁棒性.

Point Cloud Registration Method Based on Kinect

The point clouds collected by Kinect have a large quantity and position errors, and it is inefficient to directly apply the Iterative Closest Point (ICP) algorithm to point cloud registration. To solve this problem, we propose an improved point cloud registration algorithm based on the angle between the normal vectors of feature points. First, the voxel grids are used to down sample the original point clouds collected by Kinect and reduce the number of point clouds and a filter is applied to remove the outliers. Then, the Scale Invariant Feature Transform (SIFT) algorithm is employed to extract the common feature points between the target point clouds and the point clouds to be registered, and the angle between the normal vectors of feature points is calculated to adjust the point cloud pose. Thus, the initial registration of the point clouds is completed. Finally, the ICP algorithm is applied to complete the fine registration of the point clouds. The experimental results show that compared with the traditional ICP algorithm, the proposed algorithm, while ensuring the registration accuracy, can improve the registration efficiency of point clouds and has high applicability and robustness.