  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.
