  Image-based fiducial markers are useful in problems such as object tracking
in cluttered or textureless environments, camera (and multi-sensor) calibration
tasks, and vision-based simultaneous localization and mapping (SLAM). The
state-of-the-art fiducial marker detection algorithms rely on the consistency
of the ambient lighting. This paper introduces LiDARTag, a novel fiducial tag
design and detection algorithm suitable for light detection and ranging (LiDAR)
point clouds. The proposed method runs in real-time and can process data at 100
Hz, which is faster than the currently available LiDAR sensor frequencies.
Because of the LiDAR sensors' nature, rapidly changing ambient lighting will
not affect the detection of a LiDARTag; hence, the proposed fiducial marker can
operate in a completely dark environment. In addition, the LiDARTag nicely
complements and is compatible with existing visual fiducial markers, such as
AprilTags, allowing for efficient multi-sensor fusion and calibration tasks. We
further propose a concept of minimizing a fitting error between a point cloud
and the marker's template to estimate the marker's pose. The proposed method
achieves millimeter error in translation and a few degrees in rotation. Due to
LiDAR returns' sparsity, the point cloud is lifted to a continuous function in
a reproducing kernel Hilbert space where the inner product can be used to
determine a marker's ID. The experimental results, verified by a motion capture
system, confirm that the proposed method can reliably provide a tag's pose and
unique ID code. The rejection of false positives is validated on the Google
Cartographer indoor dataset and the Honda H3D outdoor dataset. All
implementations are coded in C++ and are available at:
https://github.com/UMich-BipedLab/LiDARTag.
