Moving Object Tracking with Mobile Robot Using Camera and Laser

This paper presents a simultaneous localization, mapping (SLAM) and object tracking(OT) method based on laser and camera data fusion to achieve simultaneous estimation of robot state, environment features states and object's trajectory in unknown environments. The proposed algorithm is using Full-Correlation Extended Kalman Filter (FCEKF) frameworks, and the system state is combination of robot state, feature states and object state. Object observation is come from two sensors, one is camera and the other is laser. Camshift method is used to get object measurements from camera image, at same time, consistency grid map method is used to get the same object measurements from laser ranger finder, those same object measurements come from different sensor is inputted to FCEKF, and improving robustness and accuracy of system state estimation. The experimental results show that the proposed algorithm is effective to object tracking in outdoor unknown environments.