Accurate detection of movable and moving objects in LiDAR is of vital importance for navigation. Most existing works focus on extracting and removing moving objects during navigation. Movable objects like pedestrians, parked vehicles, etc. although static may move in the future. This leads to erroneous navigation and accidents. In such cases, it becomes necessary to detect potentially movable objects. To this end, we present a learning-based approach that segments movable and moving objects by generating static parts of scenes that are otherwise occluded. Our model performs superior to existing baselines on static LiDAR reconstructions using 3 datasets including a challenging sparse industrial dataset. We achieve this without the assistance of any segmentation labels because such labels might not always be available for less popular yet important settings like industrial environments. The non-movable static parts of the scene generated by our model are of vital importance for downstream navigation for SLAM. The movable objects detected by our model can be fed to a downstream 3D detector for aiding navigation. Though we do not use segmentation, we evaluate our method against navigation baselines that use it to remove dynamic objects for SLAM. Through extensive experiments on several datasets, we showcase that our model surpasses these baselines on navigation.

翻译：基于LiDAR的可移动物体与运动物体的精确检测对于导航至关重要。现有方法大多聚焦于导航过程中提取并移除运动物体。行人、停驻车辆等可移动物体虽为静态，但未来可能运动，这将导致导航错误与事故。在此类场景中，检测潜在可移动物体变得尤为必要。为此，我们提出一种基于学习的方法，通过生成原本被遮挡的场景静态部分，实现对可移动与运动物体的分割。在包含具有挑战性的稀疏工业数据集在内的三个数据集的静态LiDAR重建实验中，本模型表现优于现有基线方法。由于此类分割标签在工业环境等非主流但重要的场景中可能缺失，我们实现了无需任何分割标签辅助的模型训练。模型生成的场景不可动静态部分对后续SLAM导航至关重要，而模型检测的可移动物体可输入下游3D检测器辅助导航。尽管未采用分割技术，我们仍评估了本方法在与使用分割技术移除动态物体的导航基线的对比效果。通过在多个数据集上的大量实验，我们证明了本模型在导航性能上全面超越这些基线方法。