Visual SLAM-based Vehicle Control for Autonomous Valet Parking

Abstract

This research proposes an efficient vehicle control method using visual SLAM (Simultaneous Localization And Mapping) for AVP (Autonomous Valet Parking). SLAM technology generates a map of the surrounding environment and localizes the vehicle within the map. It is used to identify the layout of the parking lot and track the vehicle by using camera sensors only. In the proposed system, an autonomous driving vehicle is controlled using the coordinates of the keyframe on the visual SLAM map. The vehicle is driven by determining the keyframe in the movable position during the autonomous driving process. This driving procedure is possible because the coordinates of the vehicle and the keyframe can be estimated through the SLAM map. However, the SLAM map, generated using features of the surrounding environment, is likely to change scale while driving due to feature matching errors. Therefore, the system proposes to update the initial scale using the time the vehicle has moved and the changes in vehicle coordinates on the SLAM map. The tracking success rate of autonomous driving and the success rate of autonomous parking were measured to evaluate the performance of the proposed system. The experimental results indicate that autonomous valet parking can be achieved using visual SLAM.