Point cloud registration and localization based on voxel plane features

点云 计算机科学 兰萨克 体素 人工智能 八叉树 计算机视觉 转化(遗传学) 帧(网络) 聚类分析 背景(考古学) 变换矩阵 姿势 平面(几何) 水准点(测量) 数学 图像(数学) 运动学 几何学 经典力学 地理 大地测量学 化学 考古 物理 生物化学 基因 电信
作者
Jianwei Li,Jiawang Zhan,Ting Zhou,Virgílio A. Bento,Qianfeng Wang
出处
期刊:Isprs Journal of Photogrammetry and Remote Sensing 卷期号:188: 363-379 被引量:28
标识
DOI:10.1016/j.isprsjprs.2022.04.017
摘要

The 3D point cloud can directly provide accurate distance information, which facilitates many applications such as autonomous driving and environmental modeling. In order to construct more complete environmental information for these applications, it is necessary to register point clouds that are obtained from different poses. Registration after coarse localization is an effective method for understanding the pose of the device among the context of its environment. Here, a method for extracting plane features based on voxels is proposed and used for coarse registration and localization. The point cloud is divided into voxels by an octree, and voxels with the same plane characteristics are merged to obtain plane features. The candidate transformation matrix is calculated by using the corresponding plane set, and the RANSAC process with two-level transformation matrix verification, including quick verification and fine verification, is used to find the optimum transformation matrix from the candidates after clustering. Then, the coarse registration can be achieved. With the extracted plane features, a combined plane feature description of the point cloud frame is constructed to fulfill fast frame-level global localization. With the integration of the registration method presented in the current paper, pose localization can also be achieved. Experimental results show that the proposed method can achieve more than 85% successful registration rate with short time consumptions. This implies that the proposed method is more efficient than the benchmark method. Even when the map is large, frame-level localization is still fast, and has a successful localization rate of over 90%. The corresponding code are available at https://github.com/zhanjiawang/VPFBR-L .
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