Accuracy of 3D Landscape Reconstruction without Ground Control Points Using Different UAS Platforms

Drones

Oliver Lucanus¹  Margaret Kalacska¹  Kathryn Elmer¹  Andrew Groves²  Étienne Laliberté³  J.Pablo Arroyo-Mora⁴  George Leblanc⁴ 

[1] Applied Remote Sensing Lab, Department of Geography, McGill University, Montreal, QC H3A 0B9, Canada;Durham Regional Police, Whitby, ON L1N 0B8, Canada;Institut de recherche en biologie végétale, Université de Montréal, Montreal, QC H1X 2B2, Canada;National Research Council of Canada, Flight Research Lab, Ottawa, ON K1A 0R6, Canada;

关键词: GPS;    GNSS;    RTK;    PPK;    photogrammetry;    structure-from-motion;   

DOI  :  10.3390/drones4020013

来源: DOAJ

【 摘 要 】

The rapid increase of low-cost consumer-grade to enterprise-level unmanned aerial systems (UASs) has resulted in the exponential use of these systems in many applications. Structure from motion with multiview stereo (SfM-MVS) photogrammetry is now the baseline for the development of orthoimages and 3D surfaces (e.g., digital elevation models). The horizontal and vertical positional accuracies (x, y and z) of these products in general, rely heavily on the use of ground control points (GCPs). However, for many applications, the use of GCPs is not possible. Here we tested 14 UASs to assess the positional and within-model accuracy of SfM-MVS reconstructions of low-relief landscapes without GCPs ranging from consumer to enterprise-grade vertical takeoff and landing (VTOL) platforms. We found that high positional accuracy is not necessarily related to the platform cost or grade, rather the most important aspect is the use of post-processing kinetic (PPK) or real-time kinetic (RTK) solutions for geotagging the photographs. SfM-MVS products generated from UAS with onboard geotagging, regardless of grade, results in greater positional accuracies and lower within-model errors. We conclude that where repeatability and adherence to a high level of accuracy are needed, only RTK and PPK systems should be used without GCPs.

【 授权许可】

Unknown