基于3D LiDAR先验地图的基准标签定位
Fiducial Tag Localization on a 3D LiDAR Prior Map
AI总结：
本文提出首个可直接在3D LiDAR先验地图上定位基准标签的方法，通过强度与几何联合分析提取候选点簇，并利用中间平面法计算标签位姿和顶点位置，实验证明其精度优于已有方法。
摘要：
LiDAR基准标签类似于相机应用中广为人知的AprilTag，作为一种便捷资源，可为LiDAR传感器赋予人工特征，从而促进机器人应用。然而，现有的LiDAR基准标签定位方法不适用于3D LiDAR地图，而解决这一问题对基于LiDAR的重定位和导航具有重要意义。在本文中，我们提出了一种新方法，可直接在3D LiDAR先验地图上定位基准标签，返回标签位姿（以ID编号标注）和顶点位置（以索引标注），且均相对于地图的全局坐标系表示。特别地，考虑到基准标签是薄片状物体，难以与所附着平面区分，我们设计了一套新的处理流程，从强度和几何两个角度逐步分析地图的3D点云，提取可能包含标签的点簇。随后，我们引入一种基于中间平面的方法，进一步检查每个候选点簇是否包含标签，并在确认存在时计算顶点位置和标签位姿。我们进行了定性和定量实验，证明本文方法是首个适用于在3D LiDAR地图上定位标签的方法，同时与先前方法相比取得了更高的精度。本工作的开源实现可在以下地址获取：https://github.com/York-SDCNLab/Marker-Detection-General。
英文摘要：
The LiDAR fiducial tag, akin to the well-known AprilTag used in camera applications, serves as a convenient resource to impart artificial features to the LiDAR sensor, facilitating robotics applications. Unfortunately, the existing LiDAR fiducial tag localization methods do not apply to 3D LiDAR maps while resolving this problem is beneficial to LiDAR-based relocalization and navigation. In this paper, we develop a novel approach to directly localize fiducial tags on a 3D LiDAR prior map, returning the tag poses (labeled by ID number) and vertex locations (labeled by index) w.r.t. the global coordinate system of the map. In particular, considering that fiducial tags are thin sheet objects indistinguishable from the attached planes, we design a new pipeline that gradually analyzes the 3D point cloud of the map from the intensity and geometry perspectives, extracting potential tag-containing point clusters. Then, we introduce an intermediate-plane-based method to further check if each potential cluster has a tag and compute the vertex locations and tag pose if found. We conduct both qualitative and quantitative experiments to demonstrate that our approach is the first method applicable to localize tags on a 3D LiDAR map while achieving better accuracy compared to previous methods. The open-source implementation of this work is available at: https://github.com/York-SDCNLab/Marker-Detection-General.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
