  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.
