Long-Range Visual Homing

Abstract

A biologically-inspired approach to robot route following is presented. Snapshot images of a robot's environment are captured while learning a route. Later, when retracing the route, the robot uses visual homing to move between positions where snapshot images had been captured. This general approach was inspired by experiments on route following in wood ants. The impact of odometric error and another key parameter is studied in relation to the number of snapshots captured by the learning algorithm. Tests in a photo-realistic simulated environment reveal that route following can succeed even on relatively sparse paths. A major change in illumination reduces, but does eliminate, the robot's ability to retrace a route.