论文标题
3D激光雷达上的基准标签定位先验地图
Fiducial Tag Localization on a 3D LiDAR Prior Map
论文作者
论文摘要
LIDAR基金会标签类似于相机应用中使用的著名Apriltag，它是向LIDAR传感器赋予人造功能的方便资源，从而促进了机器人的应用程序。不幸的是，现有的LIDAR基准标签定位方法不适用于3D LiDAR地图，而解决此问题则对基于LIDAR的重新定位和导航有益。在本文中，我们开发了一种新颖的方法，可以将基准标签直接定位在3D激光雷达的先前地图上，返回标签姿势（由ID号标记）和顶点位置（由索引标记）W.R.T.地图的全球坐标系。特别是，考虑到基准标签是与附着的平面无法区分的薄板对象，我们设计了一条新管道，从强度和几何学角度逐渐分析地图的3D点云，从而提取了潜在的含标记点簇。然后，我们引入了一种基于中间平面的方法，以进一步检查每个潜在群集是否具有标签并计算顶点位置，并在找到标签位置。我们同时进行定性和定量实验，以证明我们的方法是第一种适用于在3D激光雷德图上定位标签的方法，同时与以前的方法相比，可以实现更好的准确性。这项工作的开源实现可在以下网址提供：https：//github.com/york-sdcnlab/marker-detection-general。
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.
