This paper presents a computationally efficient method for the measurement of a dense image correspondence vector field using supplementary data from an inertial navigation sensor (INS). The application is suited to airborne imaging systems, such as an unmanned air vehicle, where size, weight, and power restrictions limit the amount of onboard processing available. The limited processing will typically exclude the use of traditional, but computationally expensive, optical flow and block matching algorithms, such as Lucas–Kanade, Horn–Schunck, or the adaptive rood pattern search. Alternatively, the measurements obtained from an INS, on board the platform, lead to a closed-form solution to the correspondence field. Airborne platforms are well suited to this application because they already possess INSs and global positioning systems as part of their existing avionics package. We derive the closed-form solution for the image correspondence vector field based on the INS data. We then show, through both simulations and real flight data, that the closed-form inertial sensor solution outperforms traditional optical flow and block matching methods.
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