基于法向量权重改进的ICP算法

Improved ICP Algorithm Based on Normal Vector Weight

针对三维重建过程中点云配准的精度和速度不理想的问题,提出一种基于法向量权重改进的迭代最近点(ICP)算法。通过将点云的法向量投射到高斯球上,统计不同方向法向量的分布情况,结合物体的几何结构信息赋予相应的权重,利用法向量权重结合点到平面的误差度量方法计算最优刚体变换矩阵。实验结果证明:以球面点云数据为例,与改进前的迭代最近点(ICP)算法相比,在配准速度没有降低的情况下,配准误差减小为原来的30%左右,而且该算法适用于各种点云模型,效果显著。

Aiming at the problem that the accuracy and speed of point cloud registration in the process of 3D reconstruction are not ideal,an iterative nearest point(ICP)algorithm based on normal vector weight improvement is proposed.By projecting the normal vector of the point cloud onto the Gaussian sphere,the distribution of normal vectors in different directions is counted,the corresponding weight is assigned by combining the geometric structure information of the object,and the normal vector weight combined with the error measurement method from point to plane is used to calculate the optimal rigid body transformation matrix.Experimental results show that taking spherical point cloud data as an example,compared with the iterative closest point(ICP)algorithm before improvement,the registration error is reduced to about 30%without reducing the registration speed,and the algorithm is suitable for various point cloud models with significant effects.