One popular means of aerial localization and navigation in GPS-denied environments is visual terrain relative navigation or also called geo-registration. Terrain relative navigation involves performing image registration with sensed aerial camera imagery and georeferenced satellite maps to produce the geographic translation and rotation of the camera. One popular terrain relative navigation technique depends on matching feature descriptors. These features, however, are intolerant to major changes in perspective, light, vegetation, season, and other scene changes. In these cases, they produce excessive amounts of false matches. Alternatively, image correlation can be used for registering a sensed image to a reference image but is extremely intolerant to perspective differences for 6 degree of freedom (6DOF) camera systems. This research explores the use of a combination of corners detection and normalized cross correlation for aerial vehicles at different altitudes. New methods for using dynamic search windows within reference satellite imagery is explored to constrain the pose estimation and increase image matching accuracy. The algorithms are tested with both simulated aerial imagery and experimentally sensed imagery captured with rigid mounted cameras. The algorithm is evaluated on its successful match rate and pose estimation error compared to truth.
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