Region of Interest Constrained Negative Obstacle Detection and Tracking With a Stereo Camera

Road object detection has been increasingly considered an essential task in automotive applications. Road objects are divided into two groups: i) above-ground objects such as vehicles and pedestrians and ii) road obstacles such as potholes and cracks. While above-ground object detection has been actively studied and achieved great advance, road obstacle detection has not been extensively studied. This paper proposes the road obstacle detection method based on 3D point cloud candidates extracted by height analysis. The 2D candidates of road obstacles are extracted from 3D candidates. A variant of RANSAC is applied to reduce point cloud matching errors in 3D reconstruction. The proposed system is implemented on the Jetson Xavier NX platform with a 20Hz camera, providing an accurate detection rate with feature tracking and visual inertial odometry in real-time. The proposed method is evaluated on a database consisting of concrete and tar-roads scenes. The experimental results verify the superiority of the proposed method in detecting road obstacles over the state-of-the-art methods.