To solve the problem of unmanned ground vehicle leader-follower formation transportation in unstructured environment, we propose a novel target detection and tracking method based on multi-sensor fusion perception. Combined with 3D-Lidar, millimeter wave Radar and GPS/IMU, the proposed method can achieve stable target detection and continuous tracking of both static and dynamic vehicles. First, 3D-Lidar is used to detect the geometric model of the leader vehicle to complete the initialization of tracking target and it can also be assisted for target tracking. Then during the movement, the dynamic leader is mainly tracked through millimeter wave Radar as this sensor can keep tracking the same target with a constant index and effectively distinguish dynamic vehicle from other static obstacles according to relative speed estimation. In addition, by using GPS/IMU based integrated navigation, the movement trend of the leader can be derived according to the echo vehicle pose information and the relative position relationship. This is helpful to reduce the region of interest for target tracking and improve the real-time performance. In different unstructured environments, we perform the leader-follower formation transportation experiments for hundreds of kilometers. In rough terrain, the maximum tracking speed can still reach 40km/h and the maximum tracking distance can be up to 100 meters. Experiments show that the proposed method is suitable for vehicle target detection and tracking in unstructured environment. It has good robustness and high real-time performance with an average processing frame rate of 20Hz. The proposed method can be used for the formation transportation of unmanned ground vehicles to reduce labor costs.

To solve the problem of obstacle detection in unstructured scene, we propose a novel obstacle detection method based on Radar. Compared with other sensors like 3D-Lidar and color camera, Radar has higher environmental robustness especially for sandy road with dust. According to the relative velocity information of detected obstacles, obstacles are divided into static ones and dynamic ones in the proposed method. For static obstacles, map reconstruction based on Bayesian probability model is used to overcome the problem of sparse obstacle detection result in single frame and obstacle retention in the blind area. The integrated navigation system of GNSS and IMU is used to provide position and posture information during map reconstruction. For dynamic obstacles, the historical trajectory of the same target is recorded in the global coordinate frame, then the Kalman predictor based trajectory tracking and prediction is used to predict the movement trend of moving obstacles. The proposed method has been verified in a variety of unstructured scene. The experimental results show that the proposed method can provide useful obstacle perception result and meet the autonomous navigation application of UGV in unstructured scene. We also compare the proposed method with that based on 3D-Lidar in terms of the same scene. And it has obvious advantages in environmental robustness.