The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B1-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 167–172, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-167-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 167–172, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-167-2022
 
30 May 2022
30 May 2022

SENSOR INTEGRATION AND APPLICATION OF LOW-SIZED MOBILE MAPPING PLATFORM EQUIPPED WITH LIDAR, GPR AND PHOTOGRAMMETRIC SENSORS

K. Bakuła1, A. Lejzerowicz2, M. Pilarska-Mazurek1, W. Ostrowski1, J. Górka1, P. Biernat3, P. Czernic1, K. Załęgowski2, K. Kleszczewska2, K. Węzka1, M. Gąsiewski1, H. Dmowski1, and N. Styś1 K. Bakuła et al.
  • 1Warsaw University of Technology, Faculty of Geodesy and Cartography, Pl. Politechniki 1, 00-661 Warsaw, Poland
  • 2Warsaw University of Technology, Faculty of Civil Engineering, Al. Armii Ludowej 16, 00-637 Warsaw, Poland
  • 3Warsaw University of Technology, Faculty of Electrical Engineering, Pl. Politechniki 1, 00-661 Warsaw, Poland

Keywords: GPR, LiDAR, data integration, road monitoring, mobile mapping, MLS

Abstract. The article aims to analyse the possibilities of GPR, LiDAR, and photogrammetric sensors integration in a specific application, considering the various combination of sensor and their parameters. This text also discusses the possibility of using LiDAR sensors in a low-sized mobile platform for the inventory of the road lane in a dense urban area. The text presents the opportunities and recommendations of GPR and LiDAR sensors for their selection and the possibility of using them. In the case of LiDAR and photogrammetric data, two planned applications were indicated: platform georeferencing and mapping. The accuracy and noise of the Livox Avia LiDAR sensor and point cloud obtained from the Sony A7R camera with image-matching were analysed for a surface inventory. Despite the sufficient density and detail of the data, the intensity distinguishing different surfaces, the noise of LiDAR data at the level of 2 cm was too high to do the inventory of minor damages and analyse road surfaces. Higher accuracy was achieved at the level of 1 cm for photogrammetric point clouds. The article also presents the concept of integrating multi-source data visualised into the form of an oriented point cloud showing both what is above and below the earth's surface, which enables the synergy effect and joint analysis of data with entirely different characteristics.