A fast robot homing approach using sparse image waypoints

This paper proposes a fast image sequence-based navigation approach for a flat route represented in sparse waypoints. Instead of purely optimizing the length of the path, this paper aims to speed up the navigation by lengthening the distance between consecutive waypoints. When local visual homing in a variable velocity is applied for robot navigation between two waypoints, the robot's speed changes according to the distance between waypoints. Because long distance implies large scale difference between the robot's view and the waypoint image, log-polar transform is introduced to find a correspondence between images and infer a less accurate motion vector. In order to maintain the navigation accuracy, our prior work on local visual homing with SIFT feature matching is adopted when the robot is relatively close to the waypoint. Experiments support the proposed navigation approach in a multiple-waypoint route. Compared to other prior work on visual homing with SIFT feature matching, the proposed navigation approach requires fewer waypoints and the navigation speed is improved without compromising the accuracy in navigation.

► We propose a fast image sequence-based navigation which represents a route in sparse waypoints.
► Images are registered by using log-polar transform in order to lengthen the distance between consecutive waypoints.
► The robot can depart from previous waypoint at a higher speed due to the lengthened distance.
► The navigation accuracy is maintained by adopting prior work on local visual homing based on SIFT and epipolar geometry.