The LiDAR fiducial marker, akin to the well-known AprilTag used in camera applications, serves as a convenient resource to impart artificial features to the LiDAR sensor, facilitating robotics applications. Unfortunately, current LiDAR fiducial marker detection methods are limited to occlusion-free point clouds. In this work, we present a novel approach for occlusion-resistant LiDAR fiducial marker detection. We first extract 3D points potentially corresponding to the markers, leveraging the 3D intensity gradients. Afterward, we analyze the 3D spatial distribution of the extracted points through clustering. Subsequently, we determine the potential marker locations by examining the geometric characteristics of these clusters. We then successively transfer the 3D points that fall within the candidate locations from the raw point cloud onto a designed intermediate plane. Finally, using the intermediate plane, we validate each location for the presence of a fiducial marker and compute the marker's pose if found. We conduct both qualitative and quantitative experiments to demonstrate that our approach is the first LiDAR fiducial marker detection method applicable to point clouds with occlusion while achieving better accuracy.

翻译：激光雷达基准标记类似于相机应用中广为人知的AprilTag，为激光雷达传感器赋予人工特征提供了便捷手段，从而促进机器人应用。然而，现有激光雷达基准标记检测方法仅适用于无遮挡的点云数据。本文提出了一种创新的抗遮挡激光雷达基准标记检测方法。我们首先利用三维强度梯度提取可能对应标记的三维点，随后通过聚类分析提取点的三维空间分布特征。接着，通过检测这些聚类的几何特性确定候选标记位置。然后，将候选位置内的原始点云三维点依次投影至预设的中间平面。最后，基于该中间平面验证各位置是否存在基准标记，并在确认存在时计算标记位姿。通过定性与定量实验表明，本方法是首个能够处理遮挡点云且实现更高精度的激光雷达基准标记检测方法。