Stereo-based simultaneous localization, mapping and moving object tracking

Vision based simultaneous localization and mapping (SLAM) has recently received much research interest. However, vision based SLAM could be corrupted with the inclusion of moving entities, which makes it hard to operate in dynamic environments. Simultaneous localization, mapping and moving object tracking (SLAMMOT) serves as a solution to deal with moving objects while performing SLAM. The existing work has shown the feasibility of monocular SLAMMOT in dynamic environments. However, monocular SLAMMOT inherits the observability issue of bearings-only tracking in which moving entities would be unobservable according to motions of the camera and moving objects. In this paper, stereo-based SLAMMOT is proposed to solve the observability issue as well as increase the accuracy of localization, mapping and tracking. Simulation and experimental results demonstrate that the proposed stereo SLAMMOT is superior than monocular SLAMMOT in dynamic environments.