The influence of drone flightpath on photogrammetric model quality

Abstract

In 2017 the Netherlands had 7146km of railways [Ramaekers et al., 2009], which are owned and managed by ProRail and have to be frequently surveyed, for quality inspection. Surveying is done using a variety of surveying techniques, many of which require surveyors to walk on or near the tracks, which could cause injuries. An alternative surveying technique would be photogrammetry using images acquired with a camera mounted on a drone. In this technique a 3D point cloud is constructed out of images, which is georeferenced using GPS measurements on the drone and Ground Control Points (GCP). The positions of the rail rods can then be found by modelling the rail rod using the point cloud. The quality of the point cloud, defined here as a combination of accuracy, precision, point density, completeness, and the rail rod feature detectability, is an important factor in deciding if photogrammetry is a sufficient surveying technique for this project.

In this research the influence of the distance between the camera and the track, the baseline, and the number and spread of GCPs on the quality of the resulting point cloud. The distance to the track was set to 25, 30 and35m and the baseline was varied between 2 and 10m. The requirement set for the accuracy is 15mm, while the precision has to be below 10mm. For the point density an K-nearest neighbors (K-nn) of at least 10 is required, the completeness requires less than 10% missing cells and finally the feature detectability requires that at least once every 2m the location of the rail can be determined. The features are detected using a 2D model of the rail that is fitted in a flattened slice of the point cloud using a modified version of the iterative closest point algorithm.

It is shown that the accuracy and precision are influenced by the distance and baseline, but no clear relation was found, while the accuracy improves when using GCPs. For the point density it is shown that increasing the distance between the camera and the track linearly decreases the point density, while the baseline has no impact and for the completeness a decreasing trend was found when increasing the baseline, while no relation could be found between the completeness and the distance between the camera and the track. For the rail rod feature detectability it was shown that the percentage of accurately found rail rod positions goes down when increasing the distance to the track and the baseline. Overall it is proven that when using a 100MP camera, 5 GCPs, a distance of 25m, and a baseline of 2.17m the rail rods could be surveyed so that the requirements set for this research were met.