ISPRS Journal of Photogrammetry and Remote Sensing ( IF 12.7 ) Pub Date : 2022-05-05 , DOI: 10.1016/j.isprsjprs.2022.04.017 Jianwei Li , Jiawang Zhan , Ting Zhou , Virgílio A. Bento , Qianfeng Wang
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.
中文翻译:
基于体素平面特征的点云配准与定位
3D点云可以直接提供准确的距离信息,方便自动驾驶、环境建模等诸多应用。为了为这些应用构建更完整的环境信息,需要注册从不同姿势获得的点云。粗略定位后的配准是了解设备在其环境上下文中的姿态的有效方法。在这里,提出了一种基于体素提取平面特征的方法,并将其用于粗略配准和定位。将点云通过八叉树划分为体素,将具有相同平面特征的体素合并得到平面特征。使用对应的平面集计算候选变换矩阵,采用快速验证和精细验证两级变换矩阵验证的RANSAC过程,从聚类后的候选中找到最优变换矩阵。然后,可以实现粗配准。利用提取的平面特征,构建点云框架的组合平面特征描述,以实现快速的框架级全局定位。结合本文提出的配准方法,也可以实现姿态定位。实验结果表明,所提出的方法可以在很短的时间消耗下达到85%以上的成功注册率。这意味着所提出的方法比基准方法更有效。即使地图很大,帧级定位仍然很快,本地化成功率达90%以上。对应代码在 https://github.com/zhanjiawang/VPFBR-L。