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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.

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This paper presents a method for pedestrian tracking in urban environments using in-vehicle multilayer laser lidar (MLLR). The MLLR that we developed irradiates the laser in six scanning planes by a polygon mirror mechanism, and thus objects with height are observed with the plural scanning planes. MLLR outputs are modified by GPS data and are mapped onto a grid map. Pedestrians are found based on the occupancy grid method, and they are tracked via Kalman filter in conjunction with global nearest neighboring (GNN) based data association. A track management method improves tracking accuracy in real worlds. Our tracking algorithm works well in a low-performance computer environment. The experimental results in different scenarios such as intersection and on the community road validate the proposed method. I IEEE Intelligent Vehicles Symposium

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