A virtual rail train path recognition method based on binocular stereo vision - ZENG Junwei; GENG Li; LU Chenyang; REN Lihui (机车电传动)
This paper presents a path recognition method based on binocular stereo vision for virtual rail train, to overcome the limitations of monocular vision and improve the path perception ability of virtual rail train. To achieve this, a lane detection model based on Mask R-CNN was developed, incorporating their characteristics such as obvious lane blocks, contour integrity, and unique shape. Meanwhile, a binocular matching algorithm based on multi-target tracking was proposed, addressing the drawbacks of general binocular matching algorithms including substantial computations and deficient ability to match repeated objects. The proposed method incorporated both left and right cameras to detect lane blocks respectively, and enabled the assignment of IDs through multi-target tracking. There features allowed for an orderly and directional binocular matching of lane blocks in the left and right images, and the reconstruction of three-dimensional coordinates of the path in the tram coordinate system. This method was demonstrated effective in improving the path perception ability of trams and providing more direct and accurate input information to enable various functions such as tracking control, autonomous positioning and relative pose estimation of trams. Additionally, experimental results show the high accuracy of the proposed method in 3D path reconstruction and its strong adaptability to different road conditions.