The United States has the world's longest railway network serving both freight and passenger transportation. For safe and reliable operation of the trains, railway tracks are critically important as the train stability, speed, and ride quality hinge upon them. Poor track geometry can cause enhanced wear and tear on the train components, increase maintenance costs, and raise risk of derailments. For this reason, railway tracks are surveyed regularly to ensure that they meet the required safety standards. To assist in doing this, track geometry and inspection vehicles (TGIVs) provide alignment and profile information. However, inherent inaccuracies of TGIV estimates leads to increased reliance on manual surveys. The high cost of surveys discourages the needed frequent track geometry monitoring and assessment.
This research project proposes to integrate camera modules with TGIVs and fuse image data with Inertial Measurement Unit (IMU) measurements to provide cost effective and faster and more accurate railway track geometry estimates and condition insights. IMU measurements are excellent for monitoring high frequency position and orientation variations in tracks while camera-based measurements are great for observing low frequency variations. So, by fusing image data with IMU measurements, it is possible to produce highly accurate and complete track geometry data.
The project work will be carried out in collaboration with the New Jersey Transit, the partner transit agency. Specific geometric parameters required by the partner transit agency as well as the agency’s hardware (e.g., track and TGIV) specifications will be determined. Image processing and feature tracking algorithms for vision-based data extraction will be developed along with deflection compensation algorithm based on example TGIV data provided to the research team. A data fusion algorithm will then be developed, based on state estimator, AI, and statistical approaches. All algorithms will be combined into an automated framework, and their capabilities tested using computer simulation. A camera module with the required specifications will be procured, installed, and tested to identify the ideal module placement on the TGIV with respect to position and orientation. The camera will be installed on a TGIV and several passes over an off-line track will be conducted to compare its results with the laser scans of the track being tested.
The proposed approach has a high potential for replacing the need for separate TGIV and surveying deployments and provide a unified and continuous geometry estimation platform that would improve safety and reliability of railroads at a reduced cost for operators.
