  Dynamic environments that include unstructured moving objects pose a hard
problem for Simultaneous Localization and Mapping (SLAM) performance. The
motion of rigid objects can be typically tracked by exploiting their texture
and geometric features. However, humans moving in the scene are often one of
the most important, interactive targets - they are very hard to track and
reconstruct robustly due to non-rigid shapes. In this work, we present a fast,
learning-based human object detector to isolate the dynamic human objects and
realise a real-time dense background reconstruction framework. We go further by
estimating and reconstructing the human pose and shape. The final output
environment maps not only provide the dense static backgrounds but also contain
the dynamic human meshes and their trajectories. Our Dynamic SLAM system runs
at around 26 frames per second (fps) on GPUs, while additionally turning on
accurate human pose estimation can be executed at up to 10 fps.
