Mobile robots that navigate in unknown environments need to be constantly aware of the dynamic objects in their surroundings for mapping, localization, and planning. It is key to reason about moving objects in the current observation and at the same time to also update the internal model of the static world to ensure safety. In this paper, we address the problem of jointly estimating moving objects in the current 3D LiDAR scan and a local map of the environment. We use sparse 4D convolutions to extract spatio-temporal features from scan and local map and segment all 3D points into moving and non-moving ones. Additionally, we propose to fuse these predictions in a probabilistic representation of the dynamic environment using a Bayes filter. This volumetric belief models, which parts of the environment can be occupied by moving objects. Our experiments show that our approach outperforms existing moving object segmentation baselines and even generalizes to different types of LiDAR sensors. We demonstrate that our volumetric belief fusion can increase the precision and recall of moving object segmentation and even retrieve previously missed moving objects in an online mapping scenario.

翻译：在未知环境中导航的移动机器人需要持续感知周围动态物体，以完成建图、定位和规划任务。关键在于推理当前观测中的移动物体，同时更新静态世界的内部模型以确保安全性。本文研究了联合估计当前3D激光雷达扫描中的移动物体与局部环境地图的问题。我们采用稀疏4D卷积从扫描与局部地图中提取时空特征，将所有3D点分割为移动与非移动类别。此外，我们提出通过贝叶斯滤波器将这些预测结果融合到动态环境的概率表示中，构建体积置信度模型以表征环境中可能被移动物体占据的区域。实验表明，我们的方法优于现有移动物体分割基准，并能泛化至不同类型的激光雷达传感器。我们证明体积置信度融合可提升移动物体分割的精确率与召回率，甚至能在线建图场景中检索到之前遗漏的移动物体。