Radar based obstacle detection in unstructured scene.

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.