Railway transportation is crucial to reducing greenhouse gas emissions and meeting increasing global demand for passenger and freight transport. To maintain a reliable, high-capacity railway infrastructure, regular and accurate maintenance is necessary. The recent development of a Rail-based Track
Surveying System that can be mounted on any operational train, presents a comprehensive solution to acquire track geometry measurements while limiting track unavailability. The system is able to simultaneously acquire measurements of both the absolute- and relative track geometry using a combination of a GNSS / INS integrated navigation system and a laser ranging sensor. The measurement unit is able to obtain consistently accurate absolute track geometry measurements (σ2D = 8 [mm] (Wang et al.2019)) in areas with good GNSS signal reception. However, in areas with limited GNSS coverage, the accuracy of these measurements can fall below the requirements for rapid track inventory acquisition (3 [cm], p = 0.95 (Specht et al. 2016)). This thesis, developed and conducted in lose collaboration with Fugro, aims to improve the accuracy of the trajectory solution for rail-based track surveying systems in such limited GNSS environments.

At the time this research was performed, there was no ground truth or reference trajectory available to assess the accuracy of a trajectory solution. Therefore, the research first establishes quality metrics to provide a measure of the accuracy and certainty of the integrated GNSS/INS estimated trajectory solution. By nature of the data acquisition process of the RTSS, multiple runs, or trajectory estimates, are available of an arbitrary track segment. The observed cross-track trajectory spread (precision) provides a qualitative metric to assess the level of accuracy of multiple passes over the same track segment, given that the
individual measurements are unbiased. To quantify the cross-track trajectory
spread, the standard deviation and smallest circle radius are selected as statistical and absolute metrics.

Next, the research identifies the existence of unreliable or inaccurate GNSS positioning updates in the integrated trajectory solution by linking the cross-track trajectory spread to the Quality Control metrics of individual runs. The level of accuracy of the GNSS / IMU integrated trajectory solution was found to be decreased at track segments where one (or multiple) runs showed sustained periods of high (> 5) PDOP values. A decreased accuracy in the order of 10 [cm] was observed for the high PDOP GNSS positioning updates. Furthermore, these inaccurate position updates were weighted too heavily in the Loosely Coupled integration scheme, reducing the accuracy of the integrated GNSS/IMU
trajectory solution. To improve the accuracy and certainty of the integrated trajectory solution, the GNSS positioning updates in segments with sustained high PDOP values can be inactivated. Inactivating these high PDOP GNSS positioning updates, reduced the observed cross-track trajectory spread by
as much as c.50%.

The thesis provides valuable results for the improvement of the accuracy and certainty of GNSS/IMU trajectory determination in rail-based applications. Furthermore, the framework and data-analysis algorithm developed and presented in this thesis research could be used to quantify the effect of different
quality parameters and processing thresholds on the accuracy of the trajectory solution. The main conclusion and recommendation of this report - processing the trajectory against a tight PDOP constraint - leads to a trajectory estimate for a rail-based surveying application with improved accuracy and certainty. Moreover, recommendations for research implementation, further trajectory accuracy gains and continuation of this research are also presented in this report.