基于DNSS与点到平面的ICP结合的点云配准算法

Point Cloud Registration Algorithm Based on the Combination of DNSS and ICP

针对现有点云配准算法众多、配准速度和配准精度不尽相同的问题,本文提出了一种将DNSS与点到平面的ICP相结合的配准算法,利用DNSS提取源点云数据的关键点,利用关键点约束查找对应匹配点对,结合点到平面的误差度量方法计算最优刚体变换矩阵,从而完成点云配准。对配准后的结果进行误差分析,实验结果证明,基于DNSS与点到平面ICP结合的点云配准算法配准精度高于点到点的ICP算法和点到平面的ICP算法,且该方法处理几何特征复杂、特征明显的点云数据优势显著。

Aiming at the problems of many existing point cloud registration algorithms,such as different registration speed and registration accuracy,this paper proposes a registration algorithm combining DNSS with Point-to-Plane ICP.It uses DNSS to extract the key points of the source point cloud data,uses the key point constraints to find the corresponding matching point pairs,and calculates the optimal rigid body transformation matrix combined with the point to plane error measurement method,so as to complete the point cloud registration.The experimental results show that the registration accuracy of point cloud registration algorithm based on the combination of DNSS and Point-to-Plane ICP is higher than that of point-to-point ICP algorithm and Point-to-Plane ICP algorithm,and this method has significant advantages in processing point cloud data with complex geometric features and obvious features.