Simultaneous localization, mapping and moving object tracking

LOCALIZATION, mapping and moving object tracking serve as the basis for scene understanding, which is a key prerequisite for making a robot truly autonomous. Simultaneous localization, mapping and moving object tracking (SLAMMOT) involves not only simultaneous localization and mapping (SLAM) in dynamic environments but also detecting and tracking these dynamic objects. It is believed by many that a solution to the SLAM problem would open up a vast range of potential applications for autonomous robots. Accordingly, a solution to the SLAMMOT problem would expand robotic applications in proximity to human beings where robots work not only for people but also with people. This thesis establishes a new discipline at the intersection of SLAM and moving object tracking. Its contributions are two-fold: theoretical and practical. From a theoretical perspective, we establish a mathematical framework to integrate SLAM and moving object tracking, which provides a solid basis for understanding an