针对重叠率低的两片点云配准难度大、精度低等问题,提出了一种将聚类区域分块和凸优化问题相结合的点云配准方法。首先,利用点云的曲率特征进行多尺度描述符的建立,确保点云数据完整并且使冗余数据最小;其次,利用多尺度描述符的角度差异进行对应关系聚类分块,获取源点云与目标点云的重叠区域;最后,将重叠区域的点云以及它们的对应关系代入凸优化问题,进行离群值的去除和对应关系的优化,实现粗配准并利用迭代最近点算法进行细化。实验结果表明,所提算法能够缩小点云配准的有用搜索范围,减少配准计算量,为初始重叠程度较低的点云数据提供更具优势的配准精度和时间效率。

Aiming at the problem of high difficulty and low precision of two point cloud registration with low overlap rate, a point cloud registration method combining clustering region partitioning with convex optimization problem is proposed. First, the curvature feature of point cloud is used to establish multi-scale descriptor to ensure the integrity of point cloud data and minimize redundant data. Second, the angle difference of multi-scale descriptor is used to cluster and block the corresponding relationship to obtain the overlap area of the source point cloud and the target point cloud. Finally, the point clouds in the overlap area and their corresponding relations are substituted into the convex optimization problem to remove outliers and optimize the corresponding relations to achieve the coarse registration, and then the iterative closest point algorithm is used to refine. Experimental results show that the proposed algorithm can narrow the useful search range of point cloud registration, reduce the amount of registration computation, and provide more advantageous registration accuracy and time efficiency for point cloud data with low initial overlap.