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There is a growing demand for unmanned aerial systems as autonomous surveillance, exploration and remote sensing solutions. Amongthe key concerns for robust operation of these systems is the need to reliably navigate the environment without reliance on globalnavigation satellite system (GNSS). This is of particular concern in Defence circles, but is also a major safety issue for commercialoperations. In these circumstances, the aircraft needs to navigate relying only on information from on-board passive sensors such asdigital cameras. An autonomous feature-based visual system presented in this work offers a novel integral approach to the modellingand registration of visual features that responds to the specific needs of the navigation system. It detects visual features from GoogleEarth<sup>*</sup> build a feature database. The same algorithm then detects features in an on-board cameras video stream. On one level thisserves to localise the vehicle relative to the environment using Simultaneous Localisation and Mapping (SLAM). On a second level itcorrelates them with the database to localise the vehicle with respect to the inertial frame.  The performance of the presented visual navigation system was compared using the satellite imagery from different years. Based oncomparison results, an analysis of the effects of seasonal, structural and qualitative changes of the imagery source on the performanceof the navigation algorithm is presented.  <sup>*</sup> The algorithm is independent of the source of satellite imagery and another provider can be use

SATELLITE IMAGERY ASSISTED ROAD-BASED VISUAL NAVIGATION SYSTEM