This paper presents an outdoors laser-based pedestrian tracking system using a group of mobile robots located near each other. Each robot detects pedestrians from its own laser scan image using an occupancy-grid-based method, and the robot tracks the detected pedestrians via Kalman filtering and global-nearest-neighbor (GNN)-based data association. The tracking data is broadcast to multiple robots through intercommunication and is combined using the covariance intersection (CI) method. For pedestrian tracking, each robot identifies its own posture using real-time-kinematic GPS (RTK-GPS) and laser scan matching. Using our cooperative tracking method, all the robots share the tracking data with each other; hence, individual robots can always recognize pedestrians that are invisible to any other robot. The simulation and experimental results show that cooperating tracking provides the tracking performance better than conventional individual tracking does. Our tracking system functions in a decentralized manner without any central server, and therefore, this provides a degree of scalability and robustness that cannot be achieved by conventional centralized architectures.