1 May 2017 Geopositioning with a quadcopter: Extracted feature locations and predicted accuracy without a priori sensor attitude information
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Abstract
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.
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John Dolloff, John Dolloff, Bryant Hottel, Bryant Hottel, David Edwards, David Edwards, Henry Theiss, Henry Theiss, Aaron Braun, 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 (1 May 2017); doi: 10.1117/12.2263856; https://doi.org/10.1117/12.2263856
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