Learning View Graphs for Robot Navigation
Autonomous Robots - Special issue on autonomous agents
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Depth, contrast and view-based homing in outdoor scenes
Biological Cybernetics
Linked Local Navigation for Visual Route Guidance
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Linked Local Visual Navigation and Robustness to Motor Noise and Route Displacement
SAB '08 Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats
Analyzing the effect of landmark vectors in homing navigation
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
How active vision facilitates familiarity-based homing
Living Machines'13 Proceedings of the Second international conference on Biomimetic and Biohybrid Systems
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It is known that ants learn long visually guided routes through complex terrain. However, the mechanisms by which visual information is first learned and then used to control a route direction are not well understood. In this article, we propose a parsimonious mechanism for visually guided route following. We investigate whether a simple approach, involving scanning the environment and moving in the direction that appears most familiar, can provide a model of visually guided route learning in ants. We implement view familiarity as a means of navigation by training a classifier to determine whether a given view is part of a route and using the confidence in this classification as a proxy for familiarity. Through the coupling of movement and viewing direction, a familiar view specifies a familiar direction of viewing and thus a familiar movement to make. We show the feasibility of our approach as a model of ant-like route acquisition by learning a series of nontrivial routes through an indoor environment using a large gantry robot equipped with a panoramic camera.