Local navigation using weight learning on image features

Visual navigation is a challenging subject in robotics, which is involved estimating the target position and direction at an arbitrary location. In this study, we follow the snapshot model, a bio-inspired model to determine the target direction with the snapshots taken at the current location and the target location. From the snapshots, we collect landmarks with three different features, the corner landmarks with SURF (Speeded Up Robust Features), the vertical edge landmarks with HOG (Histogram of Gradient) and the Haar-like feature landmarks. Those methods can play significant roles in finding appropriate visual features depending on the environment. A linear combination of those landmarks, that is, weighted feature landmarks are more suitable to find homing vector than landmarks found with one method alone. We propose that the gradient-descent method should be applied to the weighted feature landmarks to improve the homing performance. The homing results with ALV (Average Landmark Vector) model are demonstrated to show the effectiveness of the method.