In the construction of high-speed railway infrastructure, a CRTS-III slab ballastless track plate has been widely used. Anchor sealing is an essential step in the production of track plates. We design a novel automated platform based on industrial robots with vision guidance to improve the automation of a predominantly human-powered anchor sealing station. This paper proposes a precise and efficient target localization method for large and high-resolution images to obtain accurate target position information. To accurately update the robot’s work path and reduce idle waiting time, this paper proposes a low-cost and easily configurable visual localization system based on dual monocular cameras, which realizes the acquisition of track plate position information and the correction of position deviation in the robot coordinate system. We evaluate the repeatable positioning accuracy and the temporal performance of the visual localization system in a real production environment. The results show that the repeatable positioning accuracy of this localization system in the robot coordinate system can reach ±0.150 mm in the x- and y-directions and ±0.120° in the rotation angle. Moreover, this system completes two 18-megapixel image acquisitions, and the whole process takes around 570 ms to meet real production needs.

Six degrees of freedom pose estimation technology constitutes the cornerstone for precise robotic control and similar tasks. Addressing the limitations of current 6-DoF pose estimation methods in handling object occlusions and unknown objects, we have developed a novel two-stage 6-DoF pose estimation method that integrates RGB-D data with CAD models. Initially, targeting high-quality zero-shot object instance segmentation tasks, we innovated the CAE-SAM model based on the SAM framework. In addressing the SAM model’s boundary blur, mask voids, and over-segmentation issues, this paper introduces innovative strategies such as local spatial-feature-enhancement modules, global context markers, and a bounding box generator. Subsequently, we proposed a registration method optimized through a hybrid distance metric to diminish the dependency of point cloud registration algorithms on sensitive hyperparameters. Experimental results on the HQSeg-44K dataset substantiate the notable improvements in instance segmentation accuracy and robustness rendered by the CAE-SAM model. Moreover, the efficacy of this two-stage method is further corroborated using a 6-DoF pose dataset of workpieces constructed with CloudCompare and RealSense. For unseen targets, the ADD metric achieved 2.973 mm, and the ADD-S metric reached 1.472 mm. This paper significantly enhances pose estimation performance and streamlines the algorithm’s deployment and maintenance procedures.