An Orientation Invariant Visual Homing Algorithm

Visual homing is the ability of an agent to return to a goal position by comparing the currently viewed image with an image captured at the goal, known as the snapshot image. In this paper we present additional mathematical justification and experimental results for the visual homing algorithm first presented in Churchill and Vardy (2008). This algorithm, known as Homing in Scale Space, is far less constrained than existing methods in that it can infer the direction of translation without any estimation of the direction of rotation. Thus, it does not require the current and snapshot images to be captured from the same orientation (a limitation of some existing methods). The algorithm is novel in its use of the scale change of SIFT features as an indication of the change in the feature’s distance from the robot. We present results on a variety of image databases and on live robot trials.