Around one-third of the U.S. rail network is owned and operated by shortlines (Class II and III railroads). The infrastructure conditions of these railroads are marginal, and their revenue and number of employees are insufficient. To conduct proper and timely infrastructure management, it is critical for shortlines to establish a reliable and cost-effective inventory of their existing rail tracks. Significant efforts have been made to develop and employ automatic rail extraction methods as it is a critical step to establishing a rail infrastructure inventory. However, existing methods rely heavily on high-density point cloud datasets with known sensor properties and configurations or sophisticated imaging sensors. These requirements prevent their deployments in shortlines because point cloud data available to shortlines are often in low-density and with unknown specifications constrained by their financial situations. To address those limitations, we propose an automatic, configuration-independent coarse-to-fine extraction method for low-density LiDAR data. The proposed extraction framework is only developed based on high-level geometric features of the rail that can even be captured by low-density point cloud data with unknown sensor properties and configurations. The proposed method is evaluated on the existing grade-crossing dataset collected by the Federal Railroad Administration for grade-crossing safety evaluation with a point cloud density of only 293 points/ $\text{m}^{{2}}$ . The overall performance shows average completeness of 96.97%, correctness of 99.71%, and quality of 96.67% for all extraction scenarios. The promising results provide shortlines a capability to extract rails for any geometry measurements based on any low-density LiDAR data available to them.

Federal Railroad Administration strictly regulates the inspection frequency of all track classes to ensure timely identification of rail defects including irregular gage which is a devastating rail geometry defect. Conventional rail inspection methods are both costly and labor-intensive, whereas existing novel technologies can be expensive and mostly focus on a specific inspection area, e.g. vertical alignment. iPhone 12 Pro was introduced to the public recently with a low-cost, low-resolution light detection and ranging (LiDAR) sensor that is purposed for better photography and virtual reality. Thanks to its portability and computational capacity, iPhone 12 Pro can potentially be used as a portable solution for irregular gage inspection, whose capacity and feasibility are unknown. This study first investigated the capability of the iPhone 12 Pro in calculating unloaded rail gages by its embedded LiDAR sensor. The results showed that uncalibrated raw gage values measured by the iPhone 12 Pro LiDAR sensor were systematically lower than the ground-truth values. The proposed method in this study then introduced logistic regression to calibrate the measured values through balancing the prediction performance and the efficiency, followed by validations using a Gaussian process classifier. The results show that the proposed method correctly identified all 39 high-risk locations with 227 false alarmed locations. The proposed method with the iPhone 12 Pro LiDAR sensor could potentially narrow down the possible “high-risk” gage sections and may result in a significant reduction in the field inspection workload by 48%.

Constructing 3-D rail models from observed lidar point clouds has gained growing interest in the railroad industry: 1) to create digital twins of infrastructure asset inventories; and 2) to continuously monitor infrastructure health conditions. Performance of the existing threshold-based and rules-based extraction methods not only depends on feature-specific properties such as retro-intensity, geometric design rules, and defined feature shapes but also relies on data quality and prior knowledge such as data resolution, scanning angle, and feature orientation. This research proposes a hybrid approach, combining both data-driven filtering algorithms and artificial intelligence (AI) model-based classification approaches for automatically generating 3-D rail models. The main objective is to develop an automatic and computationally efficient procedure for rail 3-D model reconstruction in a massive, noisy, and unevenly distributed railroad scene with high accuracy but without requiring: 1) prior knowledge; 2) high-density point data clouds; or 3) feature-specific global features. In this paper, we develop and compare three hybrid procedures by using data collected by the Federal Railroad Administration (FRA) with a point density of 293 pt/m ² . Both pointwise and lengthwise evaluations were used to evaluate the robustness of the proposed methods. The pointwise evaluation shows an average precision, recall, f1, and intersection over union (IoU) of 0.989, 0.747, 0.852, and 0.741, respectively. The lengthwise evaluation shows average correctness, completeness, and quality of 99.32%, 94.69%, and 94.06%, respectively. The proposed automated, configuration-independent, and global-feature-free method shows its efficient and effective rail extraction capabilities with low-density point clouds for complicated railroad terrains.