HIGH-PRECISION OBJECT DELINEATION WITH UAV – DEMONSTRATED ON A TRACK SYSTEM

• Main Authors: M. Gerke, Y. Ghassoun, A. Alamouri, M. Bobbe, Y. Khedar, F. Plöger
• Format: Article
• Language: English
• Published: Copernicus Publications 2020-08-01
• Series: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Online Access: https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-1-2020/293/2020/isprs-annals-V-1-2020-293-2020.pdf

The proper function of rail-based transport networks relies on the accurate positioning of the tracks. Regular control and maintenance intervals are in place to guarantee safe and reliable operation. This also holds for the crane rails of the storage cranes in the container terminal in the Hamburg harbour. Especially in the terminal “Altenwerder” the geomorphological conditions of the soil lead to a permanent subsidence of the tracks and thus ask for intensive surveying and maintenance activities. The allowed tolerances are in the range of 10mm in the XY-plane on a stretch of 300m. In the daily practice, the measurements are done using traditional tachymetric survey, in combination with a rail car carrying a reflector. This method is reliable but comes with the disadvantage that the operation of cranes needs to be interrupted. In this paper we present an alternative, automatic approach which employs state-of-the-art UAV-based photogrammetry to measure the actual location of the rail. The mid-format camera system combined with a 150mm tele-lens results in a GSD of 0.9mm at 35m flying height. Challenges addressed concern the proper setup and installation of the ground control network, the flight planning and bundle adjustment. Furthermore, an automated rail delineation in the derived surface model was developed. First experiments show that an automatic workflow is possible, including the delineation task. Remaining obstacles concern, for instance, the compliance with the requirements regarding absolute positional accuracy, since the inner block geometry is theoretically much more accurate than the realised control point network.