An ISVD and SFFSD-based vehicle ego-positioning method and its application on indoor parking guidance

Abstract

Applications in intelligent transportation systems may not function properly due to loss of positioning precision or reliability in certain GPS-denied environment, e.g., underground parking lots, street surrounded by tall buildings, tunnels, etc. To overcome these challenges, this study proposes a new video-based vehicle ego-positioning method for intelligent transportation applications. Our method follows the procedure of finding feature correspondences from consecutive frames and minimizing its re-projection error. Firstly, we propose an image singular value decomposition algorithm to mitigate the impact of picture blurring. Secondly, a statistic filter of feature space displacement and a circle matching method are proposed to screen or prune potential matched features so as to remove the outliers caused by the mismatching. Thirdly, experiments are conducted on a benchmark dataset KITTI and real outdoor and indoor image data with blur, weak texture or illumination changes to verify the validation of the proposed algorithm. Lastly, the proposed algorithm is successfully applied to indoor parking guidance. The results demonstrate that compared with other state-of-the-art approaches, the proposed video-based vehicle ego-positioning scheme has more precise and reliable performance with a lower cost, which provides an appropriate way for indoor parking guidance.