LiDARTag: A Real-Time Fiducial Tag using Point Clouds

by   Jiunn-Kai Huang, et al.

Image-based fiducial markers are widely used in robotics and computer vision problems such as object tracking in cluttered or textureless environments, camera (and multi-sensor) calibration tasks, or vision-based simultaneous localization and mapping (SLAM). The state-of-the-art fiducial marker detection algorithms rely on 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 tag runs in real-time and can process data faster than the currently available LiDAR sensors frequencies. Due to the nature of the LiDAR's sensor, rapidly changing ambient lighting will not affect detection of a LiDARTag; hence, the proposed fiducial marker can operate in a completely dark environment. In addition, the LiDARTag nicely complements available visual fiducial markers as the tag design is compatible with available techniques, such as AprilTags, allowing for efficient multi-sensor fusion and calibration tasks. The experimental results, verified by a motion capture system, confirm the proposed technique can reliably provide a tag's pose and its unique ID code. All implementations are done in C++ and will be available soon at:



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Code Repositories


This is a package for extrinsic calibration between a 3D LiDAR and a camera, described in paper: Improvements to Target-Based 3D LiDAR to Camera Calibration. This package is used for Cassie Blue's 3D LiDAR semantic mapping and automation.

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This is a package for LiDARTag, described in paper: LiDARTag: A Real-Time Fiducial Tag System for Point Clouds

view repo



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