  Visual SLAM systems targeting static scenes have been developed with
satisfactory accuracy and robustness. Dynamic 3D object tracking has then
become a significant capability in visual SLAM with the requirement of
understanding dynamic surroundings in various scenarios including autonomous
driving, augmented and virtual reality. However, performing dynamic SLAM solely
with monocular images remains a challenging problem due to the difficulty of
associating dynamic features and estimating their positions. In this paper, we
present MOTSLAM, a dynamic visual SLAM system with the monocular configuration
that tracks both poses and bounding boxes of dynamic objects. MOTSLAM first
performs multiple object tracking (MOT) with associated both 2D and 3D bounding
box detection to create initial 3D objects. Then, neural-network-based
monocular depth estimation is applied to fetch the depth of dynamic features.
Finally, camera poses, object poses, and both static, as well as dynamic map
points, are jointly optimized using a novel bundle adjustment. Our experiments
on the KITTI dataset demonstrate that our system has reached best performance
on both camera ego-motion and object tracking on monocular dynamic SLAM.
