  This work presents a novel RGB-D-inertial dynamic SLAM method that can enable
accurate localisation when the majority of the camera view is occluded by
multiple dynamic objects over a long period of time. Most dynamic SLAM
approaches either remove dynamic objects as outliers when they account for a
minor proportion of the visual input, or detect dynamic objects using semantic
segmentation before camera tracking. Therefore, dynamic objects that cause
large occlusions are difficult to detect without prior information. The
remaining visual information from the static background is also not enough to
support localisation when large occlusion lasts for a long period. To overcome
these problems, our framework presents a robust visual-inertial bundle
adjustment that simultaneously tracks camera, estimates cluster-wise dense
segmentation of dynamic objects and maintains a static sparse map by combining
dense and sparse features. The experiment results demonstrate that our method
achieves promising localisation and object segmentation performance compared to
other state-of-the-art methods in the scenario of long-term large occlusion.
