  At modern construction sites, utilizing GNSS (Global Navigation Satellite
System) to measure the real-time location and orientation (i.e. pose) of
construction machines and navigate them is very common. However, GNSS is not
always available. Replacing GNSS with on-board cameras and visual simultaneous
localization and mapping (visual SLAM) to navigate the machines is a
cost-effective solution. Nevertheless, at construction sites, multiple
construction machines will usually work together and side-by-side, causing
large dynamic occlusions in the cameras' view. Standard visual SLAM cannot
handle large dynamic occlusions well. In this work, we propose a motion
segmentation method to efficiently extract static parts from crowded dynamic
scenes to enable robust tracking of camera ego-motion. Our method utilizes
semantic information combined with object-level geometric constraints to
quickly detect the static parts of the scene. Then, we perform a two-step
coarse-to-fine ego-motion tracking with reference to the static parts. This
leads to a novel dynamic visual SLAM formation. We test our proposals through a
real implementation based on ORB-SLAM2, and datasets we collected from real
construction sites. The results show that when standard visual SLAM fails, our
method can still retain accurate camera ego-motion tracking in real-time.
Comparing to state-of-the-art dynamic visual SLAM methods, ours shows
outstanding efficiency and competitive result trajectory accuracy.
