Unmanned aerial vehicles can accurately, reliably, and economically compete with terrestrial mapping methods

Orrin Thomas, Christian Stallings, Benjamin Wilkinson

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

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 foruseinplaceof, 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.

Original languageEnglish
Pages (from-to)57-74
Number of pages18
JournalJournal of Unmanned Vehicle Systems
Volume8
Issue number1
DOIs
StatePublished - 2020
Externally publishedYes

Funding

This research was supported by McKim & Creed, Inc. (author Stallings’ employer) and Cardinal Systems, LLC (author Thomas’ employer). This research was supported by McKim & Creed, Inc. (author Stallings? employer) and Cardinal Systems, LLC (author Thomas? employer).

Keywords

  • Accuracy
  • Drone
  • Mapping
  • Photogrammetry
  • UAS

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