Combining Simultaneous Localisation and Mapping (SLAM) estimation and dynamic
scene modelling can highly benefit robot autonomy in dynamic environments.
Robot path planning and obstacle avoidance tasks rely on accurate estimations
of the motion of dynamic objects in the scene. This paper presents VDO-SLAM, a
robust visual dynamic object-aware SLAM system that exploits semantic
information to enable accurate motion estimation and tracking of dynamic rigid
objects in the scene without any prior knowledge of the objects' shape or
geometric models. The proposed approach identifies and tracks the dynamic
objects and the static structure in the environment and integrates this
information into a unified SLAM framework. This results in highly accurate
estimates of the robot's trajectory and the full SE(3) motion of the objects as
well as a spatiotemporal map of the environment. The system is able to extract
linear velocity estimates from objects' SE(3) motion providing an important
functionality for navigation in complex dynamic environments. We demonstrate
the performance of the proposed system on a number of real indoor and outdoor
datasets and the results show consistent and substantial improvements over the
state-of-the-art algorithms. An open-source version of the source code is
available.

VDO-SLAM 是一种稳健的视觉动态物体感知 SLAM 系统，利用语义信息实现对本体运动的准确估计和跟踪，能够精确估计机器人轨迹、物体的 SE（3）运动和空间时序地图。