Unmanned aerial vehicles can accurately, reliably, and economically compete with terrestrial mapping methods
| Autor: | Christian Stallings, Orrin H. Thomas, Benjamin E. Wilkinson |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Control and Optimization
010504 meteorology & atmospheric sciences Computer science 0211 other engineering and technologies Point cloud Aerospace Engineering 02 engineering and technology 01 natural sciences Computer Science Applications Control and Systems Engineering Automotive Engineering Structure from motion Electrical and Electronic Engineering Spatial analysis 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing |
| Zdroj: | Journal of Unmanned Vehicle Systems. 8:57-74 |
| ISSN: | 2291-3467 |
| Popis: | Structure from motion (SfM) and imagery-derived point clouds (IDPC) are excellent tools for collecting spatial data. However, reported accuracies from unmanned aerial systems (UAS) commonly fall short of their theoretical potential. The research presented here, using a DJI Inspire 2 with post-processed kinematic direct geopositioning, demonstrates that UAS mapping can be consistently accurate enough for use in place of, or in concert with, terrestrial methods (2 cm vertical root mean squared error). We further demonstrate that features that are missing or distorted in IDPC (e.g., roof edges, break lines, and above-ground utilities) can be collected from UAS-imagery stereo models with similar accuracy. Accuracy in the experiments was verified by comparison to data from a total station and terrestrial laser scanner (TLS). Use of the recommended hardware and stereo compilation reduced mapping costs by 40%–75% on three test projects. |
| Databáze: | OpenAIRE |
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