Micro Maneuvers: Obstacle Detection for Standing Vehicles using Monocular Camera

Structure-from-motion (SfM) is still the preferred solution for 3D perception of rigid geometry in vehicles using monocular cameras. SfM provides the motion estimation of the ego-vehicle and the localization of obstacles. For the computation of 3D structure from monocular video, camera motion is required. The motion is provided by the movement of the vehicle. To avoid accidents at the beginning of the drive, the obstacle detection is needed before the vehicle starts to move, especially for autonomous vehicles. The idea followed in this paper is the execution of a small vehicle maneuver (micro maneuver) to compute the surrounding scene using SfM. The maneuver leads to the desired camera motion and is executed before starting to drive without any driver interaction. Then, obstacles are detected and considered for any driving action. Two micro maneuver types executed on a standing vehicle are under consideration: (a) steering the front wheels and (b) using the vehicle's handbrake and engine torque. We analyze the generated camera motion and the reconstructed scene. Since the resulting camera motion is small, state of the art keyframe selection techniques are compared. The application of obstacle detection using the 3D point cloud showcases the accuracy of the reconstructed scene. Based on the experiments, the most promising methodology is identified.