Moving-obstacle segmentation is an essential capability for autonomous driving. For example, it can serve as a fundamental component for motion planning in dynamic traffic environments. Most of the current 3-D Lidar-based methods use road segmentation to find obstacles, and then employ ego-motion compensation to distinguish the static or moving states of the obstacles. However, when there is a slope on a road, the widely-used flat-road assumption for road segmentation may be violated. Moreover, due to the signal attenuation, GPS-based ego-motion compensation is often unreliable in urban environments. To provide a solution to these issues, this letter proposes an end-to-end sparse tensor-based deep neural network for moving-obstacle segmentation without using GPS or the planar-road assumption. The input to our network are merely two consecutive (previous and current) point clouds, and the output is directly the point-wise mask for moving obstacles on the current frame. We train and evaluate our network on the public nuScenes dataset. The experimental results confirm the effectiveness of our network and the superiority over the baselines.

中文翻译：

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PointMoSeg：用于自动驾驶的 3D 激光雷达点云中基于稀疏张量的端到端移动障碍物分割

移动障碍物分割是自动驾驶的基本能力。例如，它可以作为动态交通环境中运动规划的基本组件。当前大多数基于 3-D 激光雷达的方法使用道路分割来寻找障碍物，然后使用自我运动补偿来区分障碍物的静态或移动状态。然而，当道路上有斜坡时，可能会违反广泛使用的道路分割的平坦道路假设。此外，由于信号衰减，基于 GPS 的自我运动补偿在城市环境中通常不可靠。为了解决这些问题，这封信提出了一种端到端的基于稀疏张量的深度神经网络，用于在不使用 GPS 或平面道路假设的情况下进行移动障碍物分割。我们网络的输入仅仅是两个连续的（前一个和当前）点云，输出直接是当前帧上移动障碍物的逐点掩码。我们在公共 nuScenes 数据集上训练和评估我们的网络。实验结果证实了我们网络的有效性和对基线的优越性。