  This paper reports on a dynamic semantic mapping framework that incorporates
3D scene flow measurements into a closed-form Bayesian inference model.
Existence of dynamic objects in the environment can cause artifacts and traces
in current mapping algorithms, leading to an inconsistent map posterior. We
leverage state-of-the-art semantic segmentation and 3D flow estimation using
deep learning to provide measurements for map inference. We develop a Bayesian
model that propagates the scene with flow and infers a 3D continuous (i.e., can
be queried at arbitrary resolution) semantic occupancy map outperforming its
static counterpart. Extensive experiments using publicly available data sets
show that the proposed framework improves over its predecessors and input
measurements from deep neural networks consistently.
