A novel InSAR based off-road positive and negative obstacle detection technique for unmanned ground vehicle
Off-road positive and negative obstacle detection is a challenge problem to be solved by unmanned ground vehicle. Traditional sensors, such as: optical camera, lidar and millimeter wave radar, have limited performance in off-road environments, especially when obstacles are far away or covered by sparse grasses. We have proposed a forward-looking InSAR sensor to tackle the problem and have built a rail-based InSAR prototype. The forward-looking InSAR can provide more information of harsh off-road environments than existing unmanned ground vehicle (UGV) based radars. The forward-looking InSAR can provide a scattering image, a coherence image and a digital terrain model (DTM) of the same scene ahead the radar during each scan. Each type of image can highlight some unique features of an obstacle. In this paper, an obstacle detection method is proposed by combining the shadow feature and the edge scattering feature. The principle method is close related to the scattering property difference between positive obstacles, negative obstacles and other objects. Positive obstacle feature large amplitude followed by low coherence area; while at the same time, negative obstacles feature low coherence area followed by large amplitude. Other objects don't have the unique feature. To mitigate false alarms, shadows are segmented in coherence images, and edge scattering features are extracted in scattering image. Firstly, the coherence image is converted into a binary image by applying a threshold. Shadow areas are roughly segmented as their coherences are low. Then the binary image is filtered by morphologic opening operation to eliminate small patches. Subsequently, an edge detection operation is applied to the filtered image. The edges of positive and negative obstacle are among the detected edge image. For each position of the detected edge, a cut is performed on the same position in the scattering image to extract a slice along the range direction. The judgment is formed by calculating the energy ratio between the near half slice of the farther half slice. Finally, positive and negative obstacles can be discriminated by comparing the judgment with two thresholds in an unsupervised fashion. We have conducted a field experiment on a ground covered by sparse grasses. A pit and a mound are deliberately built in the experiment scene. Experimental results have validated the proposed method.