Autonomous navigation is a major challenge in the development of MAVs. When an algorithm has to be efficient, insect intelligence can be a source of inspiration. An elementary navigation task is homing, which means autonomously returning to the initial location. A promising approach makes use of visual familiarity of a route to determine reference headings during homing. In this thesis an existing biological proof of concept based on desert ants is transferred to MAVs. Vision-in-the-loop experiments in different environments are performed, to investigate the viability of scene familiarity for visual navigation. Trained images are used to determine which control actions to take during homing. To determine familiarity, either a database of stored images is kept or an artificial neural network is used. Different image representations are compared in multiple simulated environments. The use of textons for determining familiarity gives the best performance, but HSV color histograms also perform well and are very efficient. It is concluded that to make this method competitive with other visual navigation approaches, route familiarity should be combined with other methods to improve robustness.