Abstract:

Multi-object Tracking （MOT） combined with Simultaneous Localization and Mapping （SLAM） ， making full use of dynamic and static information in the scene， can improve positioning accuracy and robustness， which has received considerable attention. In this paper a 3D object tracking SLAM algorithm based on the scene flow uncertainty model is proposed. With stereo or RGB-D images as input， combining instance masks and IMU information， it can accurately detect dynamic features and jointly estimate the pose transformation of itself and the objects. For the problem that dynamic， static and temporary static features cannot be accurately identified， instance information and scene flow uncertainty modeling are combined for modeling to eliminate error interference and achieve accurate dynamic feature detection. For the problem that feature points of moving objects are scarce and difficult to track， KLT optical flow and instance information are combined to perform robust multi-level data association. By constructing a factor graph and introducing vehicle kinematic constraints， tightly coupled optimization of the vehicle's own and moving object poses and map point coordinates is achieved. Finally， comparative experiments are conducted on public datasets. The results show that the proposed algorithm can accurately track the pose transformation of itself and the moving objects.

Key words: visual-inertial SLAM, 3D object tracking, scene flow uncertainty model, instance segmentation