Looking down: a model for visual route following in flying insects.

Insect visual navigation is often assumed to depend on panoramic views of the horizon, and how these change as the animal moves. However, it is known that honey bees can visually navigate in flat, open meadows where visual information at the horizon is minimal, or would remain relatively constant across a wide range of positions. In this paper we hypothesise that these animals can navigate using view memories of the ground. We find that in natural scenes, low resolution views from an aerial perspective of ostensibly self-similar terrain (e.g. within a field of grass) provide surprisingly robust descriptors of precise spatial locations. We propose a new visual route following approach that makes use of transverse oscillations to centre a flight path along a sequence of learned views of the ground. We deploy this model on an autonomous quadcopter and demonstrate that it provides robust performance in the real world on journeys of up to 30 m. The success of our method is contingent on a robust view matching process which can evaluate the familiarity of a view with a degree of translational invariance. We show that a previously developed wavelet based bandpass orientated filter approach fits these requirements well, exhibiting double the catchment area of standard approaches. Using a realistic simulation package, we evaluate the robustness of our approach to variations in heading direction and aircraft height between inbound and outbound journeys. We also demonstrate that our approach can operate using a vision system with a biologically relevant visual acuity and viewing direction.