  The static world assumption is standard in most simultaneous localisation and
mapping (SLAM) algorithms. Increased deployment of autonomous systems to
unstructured dynamic environments is driving a need to identify moving objects
and estimate their velocity in real-time. Most existing SLAM based approaches
rely on a database of 3D models of objects or impose significant motion
constraints. In this paper, we propose a new feature-based, model-free,
object-aware dynamic SLAM algorithm that exploits semantic segmentation to
allow estimation of motion of rigid objects in a scene without the need to
estimate the object poses or have any prior knowledge of their 3D models. The
algorithm generates a map of dynamic and static structure and has the ability
to extract velocities of rigid moving objects in the scene. Its performance is
demonstrated on simulated, synthetic and real-world datasets.
