基于WHI及ICP的点云配准算法

针对两步点云配准中精度差、计算效率低、易受噪声干扰的问题,提出一种基于WHI特征描述符结合改进的ICP点云配准算法。首先,对大数据量的点云通过ISS算法提取特征点集作为配准点云;然后,计算特征点云的WHI特征描述符,利用随机采样一致性算法完成粗配准;最后,基于安德森加速迭代ICP算法对粗配准点云进行精确配准。通过多组点云数据集对所提算法进行验证,实验表明,该算法配准精度高、速度快,在含有噪声数据集的优势更明显。在不同的点云模型下,所提算法的配准效率提高2倍以上,在噪声环境下具有一定的鲁棒性。

Point cloud registration algorithm based on WHI and ICP

In this paper,an improved ICP point cloud registration algorithm based on WHI feature descriptors is proposed to address the problems of poor accuracy,low computational efficiency and susceptibility to noise interference in two step point cloud registration.Firstly,the ISS algorithm is used to extract the feature point set from the point cloud with a large amount of data as the registration point cloud.Then,the WHI feature descriptor of the feature point cloud is calculated,and the random sampling consensus algorithm is used to complete the rough registration.Finally,based on Anderson accelerated iteration,the ICP algorithm performs precise registration on the coarse registration point cloud.The proposed algorithm is verified by multiple sets of point cloud datasets.The experimental results show that the algorithm is highly accurate and fast in alignment,and its advantages are more obvious in datasets containing noise.Under different point cloud models,the proposed algorithm improves the alignment efficiency by more than two times,and has certain robustness in the noise environment.