Fiducial Tag Localization on a 3D LiDAR Prior Map

• URL: http://arxiv.org/abs/2209.01072v3
• Date: Wed, 5 Jun 2024 17:12:32 GMT
• Title: Fiducial Tag Localization on a 3D LiDAR Prior Map
• Authors: Yibo Liu, Jinjun Shan, Hunter Schofield,
• Abstract summary: The existing LiDAR fiducial tag localization methods do not apply to 3D LiDAR maps. We develop a novel approach to directly localize fiducial tags on a 3D LiDAR prior map. 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.
• Score: 0.6554326244334868
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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