  This work presents a novel RGB-D SLAM approach to simultaneously segment,
track and reconstruct the static background and large dynamic rigid objects
that can occlude major portions of the camera view. Previous approaches treat
dynamic parts of a scene as outliers and are thus limited to a small amount of
changes in the scene, or rely on prior information for all objects in the scene
to enable robust camera tracking. Here, we propose to treat all dynamic parts
as one rigid body and simultaneously segment and track both static and dynamic
components. We, therefore, enable simultaneous localisation and reconstruction
of both the static background and rigid dynamic components in environments
where dynamic objects cause large occlusion. We evaluate our approach on
multiple challenging scenes with large dynamic occlusion. The evaluation
demonstrates that our approach achieves better motion segmentation,
localisation and mapping without requiring prior knowledge of the dynamic
object's shape and appearance.
