Abstract
A fundamental issue for modern service robots is human–robot interaction. In order to perform such a task, these robots need to detect and track people in the surroundings. Especially, to track targets robustly is a indispensable capability of autonomous mobile robots. Thus, a robust human detection and tracking system is an important research area in robotics. In this thesis, we present a system which is able to detect and track people efficiently by integrating laser measurements and monocular camera images information on mobile platform. A laser-based leg detector is used to detect human legs, which is trained by cascaded Adaboost with a set of geometrical features of scan segments. A visual human detector Range C4 is also proposed, which is modified from C4 human detector by adding laser range information. It achieves lower false positive rate than original C4 detector. The detected legs or persons are fused and tracked by a global nearest neighbor (GNN) data association and a sequential Kalman filtering with constant velocity model strategies. Measurements are assigned to tracks by GNN which assigns measurement by maximum similarity sum, and track states are updated by using corresponded measurements sequen- tially. Several experiments are done and to demonstrate the robustness and efficiency of our system.