LAUSR

During parking, the vision-based parking slot detection system has error or missed detection due to distortion, illumination variation, occlusion and limited field of view (FOV). Thus, the parking slot position sent to path planning system is inaccurate and discontinuous. Besides, the intelligent parking system (IPS) primarily relies on dead reckoning to send vehicle position to the motion control system, whereas the error will accumulate with the rise in driving distance. All the mentioned factors will cause parking deviation, incline or even line-pressing. In this paper, the idea of visual simultaneous localization and mapping (SLAM) is adopted innovatively to achieve the parking slot tracking. Moreover, the extended Kalman filter (EKF) is used to achieve the fusion of vision and vehicle chassis information, to solve the two problems of discontinuous and inaccurate parking slot detection, as well as cumulative error in dead reckoning. Finally, the effectiveness of the proposed algorithm is verified using the vehicle tests.