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 article 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 .

中文翻译：

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LiDARTag：用于点云的实时基准标签系统

基于图像的基准标记在诸如杂乱或无纹理环境中的对象跟踪，相机（和多传感器）校准任务以及基于视觉的同时定位和制图（SLAM）等问题中很有用。最新的基准标记检测算法依赖于环境照明的一致性。本文介绍LiDARTag，这是一种适用于光检测和测距（LiDAR）点云的新颖基准标签设计和检测算法。所提出的方法可以实时运行，并且可以以100 Hz的频率处理数据，这比当前可用的LiDAR传感器频率要快。由于LiDAR传感器的特性，迅速变化的环境照明不会影响LiDARTag的检测；因此，建议的基准标记可以在完全黑暗的环境中工作。此外，LiDARTag很好地补充了现有的视觉基准标记（例如AprilTags）并与之兼容，从而可以实现高效的多传感器融合和校准任务。我们进一步提出了一个最小化点云和标记模板之间的拟合误差以估计标记姿势的概念。所提出的方法实现了平移毫米误差和旋转几度。由于LiDAR返回的稀疏性，点云在可复制内核Hilbert空间中提升为连续函数，在该函数中内部乘积可用于确定标记的ID。经运动捕捉系统验证的实验结果证实，所提出的方法可以可靠地提供标签的姿势和唯一的ID码。已在Google Cartographer室内数据集和Honda H3D室外数据集上验证了对误报的拒绝。所有实现均以C ++编码，可在以下位置获得：https://github.com/UMich-BipedLab/LiDARTag 。