兰萨克
点云
计算机科学
算法
刚性变换
人工智能
计算机视觉
变换矩阵
职位(财务)
点集注册
钥匙(锁)
特征(语言学)
点(几何)
云计算
图像配准
数学
图像(数学)
操作系统
经济
哲学
物理
几何学
经典力学
语言学
运动学
计算机安全
财务
作者
Zhongda Zhang,Jialin Tang,Lihong Niu,Binghua Su,Yi Feng,Yicheng Sheng,Shounan Lin,Xipeng Cheng
标识
DOI:10.1109/iscer55570.2022.00034
摘要
Point cloud registration is a key technology in the fields of SLAM and 3D reconstruction. Aiming at the problem of the high initial position of the point cloud by the traditional ICP algorithm, and the problems of inconspicuous feature and low matching rate caused by the use of random sampling strategy in the classic 3D Match point cloud registration algorithm, the improved ISS key point extraction algorithm is used to obtain key points. And input the 3D Match network to obtain the 512-dimensional feature vector, RANSAC was used to eliminate mismatches, and then rigid body transformation matrix was calculated by SVD to complete coarse registration of point cloud. Then use the ICP algorithm to perform the fine registration of the point cloud. The final experimental results show that the algorithm has excellent performance and can complete the point cloud registration quickly and accurately.
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