Volume XLII-2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 917-921, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-917-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 917-921, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-917-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 May 2018

30 May 2018

REPRESENTING ROAD RELATED LASERSCANNED DATA IN CURVED REGULAR GRID: A SUPPORT TO AUTONOMOUS VEHICLES

V. Potó1, A. Csepinszky2, and Á. Barsi1 V. Potó et al.
  • 1Dept. of Photogrammetry and Geoinformatics, Budapest University of Technology and Economics, 1111 Budapest, Hungary
  • 2NNG, 1037 Budapest, Hungary

Keywords: curved regular grid, road surface, terrestrial laser scanning, mobile mapping, autonomous vehicle, transformation

Abstract. The terrestrial and mobile laser scanning has become nowadays a mature technology applied in several technical and non-technical applications. The transportation infrastructure can be surveyed by these technologies in an excellent way, then 3D maps, fly-through videos and road furniture inventories can be derived among many other applications. The very detailed measurement and the realistic feature enable even to be used in games or simulators. This advantage was to be analyzed in vehicular simulation environment; the primary goal of the paper was to demonstrate a potential workflow and use case for such laser scanning data. The selected simulation package was the OpenCRG, which is being a component of OpenDRIVE-OpenCRG-OpenSCENARIO system, where it has been developed for microscopic simulations, e.g. vibrations, tire models or vehicle suspension systems. Because of the realistic visualization of CRG models it is very popular in the design and development of autonomous vehicles. The paper presents two different paved pilot sites surveyed by these technologies, then the raw data preparation is described and the details of the CRG model building is shown. The results of the experiments bring an overview, how the captured field data can be represented and interpreted in road surface context. The diagrams illustrate the potential of the very high resolution (1 cm) model, which allows to identify each separate cobble stone or to study surface roughness.