Curb detection is essential for environmental awareness in Automated Driving (AD), as it typically limits drivable and non-drivable areas. Annotated data are necessary for developing and validating an AD function. However, the number of public datasets with annotated point cloud curbs is scarce. This paper presents a method for detecting 3D curbs in a sequence of point clouds captured from a LiDAR sensor, which consists of two main steps. First, our approach detects the curbs at each scan using a segmentation deep neural network. Then, a sequence-level processing step estimates the 3D curbs in the reconstructed point cloud using the odometry of the vehicle. From these 3D points of the curb, we obtain polylines structured following ASAM OpenLABEL standard. These detections can be used as pre-annotations in labelling pipelines to efficiently generate curb-related ground truth data. We validate our approach through an experiment in which different human annotators were required to annotate curbs in a group of LiDAR-based sequences with and without our automatically generated pre-annotations. The results show that the manual annotation time is reduced by 50.99% thanks to our detections, keeping the data quality level.

翻译：路缘检测对于自动驾驶中的环境感知至关重要，因为它通常界定了可行驶区域与不可行驶区域。为开发和验证自动驾驶功能，标注数据不可或缺。然而，带有标注点云路缘的公开数据集数量稀少。本文提出一种从激光雷达传感器捕获的点云序列中检测三维路缘的方法，包含两个主要步骤。首先，我们的方法采用分割深度神经网络在每个扫描帧中检测路缘。随后，通过序列级处理步骤，利用车辆里程计在重建点云中估计三维路缘。基于这些路缘的三维点，我们获得遵循ASAM OpenLABEL标准的结构化折线。这些检测结果可作为标注流水线中的预标注，高效生成路缘相关的真实数据。我们通过一项实验验证该方法：要求不同人工标注员在有无自动生成预标注的条件下，对一组基于激光雷达的序列进行路缘标注。结果表明，借助我们的检测结果，手动标注时间减少了50.99%，同时保持了数据质量水平。