Visual homing in environments with anisotropic landmark distribution

Abstract Gradient descent in image distances can lead a navigating agent to the goal location, but in environments with an anisotropic distribution of landmarks, gradient home vectors deviate from the true home direction. These deviations can be reduced by applying Newton’s method to matched-filter descent in image distances (MFDID). Based on several image databases we demonstrate that the home vectors produced by the Newton-based MFDID method are usually closer to the true home direction than those obtained from the original MFDID method. The greater accuracy of Newton-MFDID home vectors in the vicinity of the goal location would allow a navigating agent to approach the goal on straighter trajectories, improve the results of triangulation procedures, and enhance a robot’s ability to detect its arrival at a goal.