  In this paper, we present a tightly-coupled visual-inertial object-level
multi-instance dynamic SLAM system. Even in extremely dynamic scenes, it can
robustly optimise for the camera pose, velocity, IMU biases and build a dense
3D reconstruction object-level map of the environment. Our system can robustly
track and reconstruct the geometries of arbitrary objects, their semantics and
motion by incrementally fusing associated colour, depth, semantic, and
foreground object probabilities into each object model thanks to its robust
sensor and object tracking. In addition, when an object is lost or moved
outside the camera field of view, our system can reliably recover its pose upon
re-observation. We demonstrate the robustness and accuracy of our method by
quantitatively and qualitatively testing it in real-world data sequences.
