Understanding the scene is key for autonomously navigating vehicles and the ability to segment the surroundings online into moving and non-moving objects is a central ingredient for this task. Often, deep learning-based methods are used to perform moving object segmentation (MOS). The performance of these networks, however, strongly depends on the diversity and amount of labeled training data, information that may be costly to obtain. In this paper, we propose an automatic data labeling pipeline for 3D LiDAR data to save the extensive manual labeling effort and to improve the performance of existing learning-based MOS systems by automatically generating labeled training data. Our proposed approach achieves this by processing the data offline in batches. It first exploits an occupancy-based dynamic object removal to detect possible dynamic objects coarsely. Second, it extracts segments among the proposals and tracks them using a Kalman filter. Based on the tracked trajectories, it labels the actually moving objects such as driving cars and pedestrians as moving. In contrast, the non-moving objects, e.g., parked cars, lamps, roads, or buildings, are labeled as static. We show that this approach allows us to label LiDAR data highly effectively and compare our results to those of other label generation methods. We also train a deep neural network with our auto-generated labels and achieve similar performance compared to the one trained with manual labels on the same data, and an even better performance when using additional datasets with labels generated by our approach. Furthermore, we evaluate our method on multiple datasets using different sensors and our experiments indicate that our method can generate labels in diverse environments.
翻译:了解现场是自动导航车辆的关键, 将周围环境分解成在线移动和不移动物体的能力是这项任务的核心要素。 通常, 使用深层次的学习方法来进行移动物体分割( MOS) 。 然而, 这些网络的性能在很大程度上取决于标签培训数据的多样性和数量, 这些信息可能非常昂贵。 在本文中, 我们提议为 3D LiDAR 数据建立自动数据标签管道, 以保存广泛的手工标签工作, 并通过自动生成标签化培训数据来改善现有学习型MOS 系统的性能。 我们提议的多样化方法是通过分批处理离线数据来实现这一点的。 我们首先利用基于占用的动态物体清除方法, 以粗略的方式探测可能的动态物体。 其次, 它从建议中提取部分内容, 并使用 Kalman 过滤器跟踪这些数据。 根据跟踪轨迹, 它将实际移动的物体标为驾驶汽车和行人行作为移动工具。 相比之下, 我们不移动的物体, 例如, 汽车、 灯泡、 道路或建筑物等不移动的用途, 通过分行方式处理数据, 我们的车辆、 将这些数据与高层次的标签进行高效的标签 。 我们的标签上, 我们的标签可以有效地将这些数据与高标签进行对比。