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Image-based fiducial markers are useful in problems such as object trackingin cluttered or textureless environments, camera (and multi-sensor) calibrationtasks, and vision-based simultaneous localization and mapping (SLAM). Thestate-of-the-art fiducial marker detection algorithms rely on the consistencyof the ambient lighting. This paper introduces LiDARTag, a novel fiducial tagdesign and detection algorithm suitable for light detection and ranging (LiDAR)point clouds. The proposed method runs in real-time and can process data at 100Hz, which is faster than the currently available LiDAR sensor frequencies.Because of the LiDAR sensors' nature, rapidly changing ambient lighting willnot affect the detection of a LiDARTag; hence, the proposed fiducial marker canoperate in a completely dark environment. In addition, the LiDARTag nicelycomplements and is compatible with existing visual fiducial markers, such asAprilTags, allowing for efficient multi-sensor fusion and calibration tasks. Wefurther propose a concept of minimizing a fitting error between a point cloudand the marker's template to estimate the marker's pose. The proposed methodachieves millimeter error in translation and a few degrees in rotation. Due toLiDAR returns' sparsity, the point cloud is lifted to a continuous function ina reproducing kernel Hilbert space where the inner product can be used todetermine a marker's ID. The experimental results, verified by a motion capturesystem, confirm that the proposed method can reliably provide a tag's pose andunique ID code. The rejection of false positives is validated on the GoogleCartographer indoor dataset and the Honda H3D outdoor dataset. Allimplementations are coded in C++ and are available at:https://github.com/UMich-BipedLab/LiDARTag.

LiDARTag: A Real-Time Fiducial Tag System for Point Clouds