Online re-calibration for robust 3d measurement using single camera-pantoinspect train monitoring system

Abstract

Vision-based inspection systems measures defects accurately with the help of a checkerboard calibration (CBC) method. However, the 3D measurements of such systems are prone to errors, caused by physical misalignment of the object-of-interest and noisy image data. The PantoInspect Train Monitoring System (PTMS), is one such system that inspects defects on pantographs mounted on top of the electric trains. In PTMS, the measurement errors can compromise railway safety. Although this problem can be solved by re-calibrating the cameras, the process involves manual intervention leading to large servicing times. Therefore, in this paper, we propose Feature Based Calibration (FBC) in place of CBC, to cater an obvious need for online re-calibration that enhances the usability of the system. FBC involves feature extraction, pose estimation, back-projection of defect points and estimation of 3D measurements. We explore four state-of-the-art pose estimation algorithms in FBC using very few feature points. This paper evaluates and discusses the performance of FBC and its robustness against practical problems, in comparison to CBC. As a result, we identify the best FBC algorithm type and operational scheme for PTMS. In conclusion, we show that, by adopting FBC in PTMS and other related 3D systems, better performance and robustness can be achieved compared to CBC.