This paper presents an overview of the Full Motion Video-Geopositioning Test Bed (FMV-GTB) developed to
investigate algorithm performance and issues related to the registration of motion imagery and subsequent extraction of
feature locations along with predicted accuracy. A case study is included corresponding to a video taken from a
quadcopter. Registration of the corresponding video frames is performed without the benefit of a priori sensor attitude
(pointing) information. In particular, tie points are automatically measured between adjacent frames using standard
optical flow matching techniques from computer vision, an a priori estimate of sensor attitude is then computed based
on supplied GPS sensor positions contained in the video metadata and a photogrammetric/search-based structure from
motion algorithm, and then a Weighted Least Squares adjustment of all a priori metadata across the frames is performed.
Extraction of absolute 3D feature locations, including their predicted accuracy based on the principles of rigorous error
propagation, is then performed using a subset of the registered frames. Results are compared to known locations (check
points) over a test site. Throughout this entire process, no external control information (e.g. surveyed points) is used
other than for evaluation of solution errors and corresponding accuracy.
John Dolloff, Bryant Hottel, David Edwards, Henry Theiss, and Aaron Braun, "Geopositioning with a quadcopter: Extracted feature locations and predicted accuracy without a priori sensor attitude information," Proc. SPIE 10199, Geospatial Informatics, Fusion, and Motion Video Analytics VII, 1019906 (Presented at SPIE Defense + Security: April 12, 2017; Published: 1 May 2017); https://doi.org/10.1117/12.2263856.
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