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

TL;DR

LiDARTag is a real-time LiDAR fiducial marker system that operates reliably in dark environments, enabling accurate pose estimation and multi-sensor calibration with high processing speed and robustness against lighting variations.

Contribution

This paper introduces LiDARTag, a novel LiDAR fiducial marker design and detection algorithm that works in real-time and is unaffected by lighting conditions, enhancing multi-sensor calibration.

Findings

Runs at 100 Hz, faster than LiDAR sensor frequencies

Operates reliably in complete darkness

Achieves millimeter-level translation and degree-level rotation accuracy

Abstract

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.