The goal of this project is to develop a new method of Route following for mobile robots in a GNSSdenied environment like urban canyons or indoor. We used a robust biologically constrained neural model inspired by ants developed previously in simulation to assess the familiarity index of a panorama. A visual compass algorithm consists in determining the orientation of the maximum familiarity index with respect to the learned panoramas along a path. A car-like robot was equipped with a 220°fisheye camera. The visual compass algorithm used low resolution images of 44x44 pixels (5°/pixel) indoors and outdoors to determine the direction to follow the previously visually-learned path. Finally, the car-like robot was automated to recall the learned path indoors. The biologically constrained neural model compressed the visual information with a high efficiency so that the visual memory has a very low footprint of a few tens of kilobits that does not depend directly on the path length.