Railroads companies conduct regular inspections of their tracks to maintain and update the geographic data for railway management. Traditional railroad inspection methods, such as onsite inspections and semi-automated analysis of imagery and video data, are time consuming and ineffective. This study presents an automated effective method to detect tracks on the basis of their physical shape, geometrical properties, and reflection intensity feature. This study aims to investigate the feasibility of fast extraction of railroad using onboard Velodyne puck data collected by mobile laser scanning (MLS) system. Results show that the proposed method can be executed rapidly on an i5 computer with at least 10 Hz. The MLS system used in this study comprises a Velodyne puck/onboard GNSS receiver/inertial measurement unit. The range accuracy of Velodyne puck equipment is 2 cm, which fulfills the need of precise mapping. Notably, positioning STD is lower than 4 cm in most areas. Experiments are also undertaken to evaluate the timing of the proposed method. Experimental results indicate that the proposed method can extract 3D tracks in real-time and correctly recognize pairs of tracks. Accuracy, precision, and sensitivity of total test area are 99.68%, 97.55%, and 66.55%, respectively. Results suggest that in a multi-track area, close collaboration between MLS platforms mounted on several trains is required.

There is an increasing emphasis on achieving high accuracy and robustness in SLAM systems. The traditional visual-inertial simultaneous localization and mapping (VI-SLAM) system often struggles with stability under low light or dynamic illumination, leading to the potential loss of trajectory tracking. High accuracy and robustness are crucial for ensuring the long-term stability and reliable localization performance of SLAM systems. To address the challenges of improving robustness and accuracy in VI-SLAM, this article proposes SuperVINS, a real-time VI-SLAM framework designed for challenging imaging conditions. In contrast to geometric modeling, deep learning-based features are capable of fully leveraging the implicit information present in images, which is often not captured by geometric features. Therefore, SuperVINS, developed as an enhancement of VINS-Fusion, integrates the deep learning neural network model SuperPoint for feature point extraction and loop closure detection. At the same time, a deep learning neural network LightGlue model for associating feature points is integrated into front-end feature matching. We employed the RANSAC algorithm for matching enhancement to improve robustness against outliers. In addition, SuperVINS enables flexibly integrated environment-specific SuperPoint bag-of-words (BOWs) models for improved loop closure detection. The system enables real-time localization and mapping. Experimental validation on the well-known EuRoC and UMA-VI datasets demonstrates that SuperVINS achieves comparable accuracy and robustness to other state-of-the-art VI-SLAM systems, particularly in the most challenging sequences. This article analyzes the advantages of SuperVINS in terms of accuracy, real-time performance, and robustness. To foster knowledge exchange in the field, we have publicly released the code associated with this article. The code is available at https://github.com/luohongk/SuperVINS.