Insects are able to return to important places in their environment by storing an image of the surroundings while at the goal, and later computing a home direction from a matching between this 'snapshot' image and the currently perceived image. Very similar ideas are pursued for the visual navigation of mobile robots. A wide range of different solutions for the matching between the two images have been suggested. This paper explores the application of optical flow techniques for visual homing. The performance of five different flow techniques and a reference method is analysed based on image collections from three different indoor environments. We show that block matching, two simple variants of block matching and two even simpler differential techniques produce robust homing behaviour, despite the simplicity of the matched features. Our analysis reveals that visual homing can succeed even in the presence of many incorrect feature correspondences, and that low-frequency features are sufficient for homing. In particular, the successful application of differential methods opens new vistas on the visual homing problem, both as plausible and parsimonious models of visual insect navigation, and as a starting point for novel robot navigation methods.
In natural images, the distance measure between two images taken at different locations rises smoothly with increasing distance between the locations. This fact can be exploited for local visual homing where the task is to reach a goal location that is characterized by a snapshot image: descending in the image distance will lead the agent to the goal location. To compute an estimate of the spatial gradient in the distance measure, its value must be sampled at three noncollinear points. An animal or robot would have to insert exploratory movements into its home trajectory to collect these samples. Here we suggest a method based on the matched-filter concept that allows one to estimate the gradient without exploratory movements. Two matched filters -optical flow fields resulting from translatory movements in the horizontal plane -are used to predict two images in perpendicular directions from the current location. We investigate the relation to differential flow methods applied to the local homing problem and show that the matched-filter approach produces reliable homing behavior on image databases. Two alternative methods that only require a single matched filter are suggested. The matched-filter concept is also applied to derive a home-vector equation for a Fourier-based parameter method.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.