Self organising neural place codes for vision based robot navigation

Autonomous robots must be able to navigate independently within an environment. In the animal brain, so-called place cells respond to the environment where the animal is. We present a model of place cells based on self-organising maps. The aim of this paper is to show how image invariance can improve the performance of the neural place codes and make the model more robust to noise. The paper also demonstrates that localisation can be learned without having a pre-defined map given to the robot by humans and that after training, a robot can localise itself within a learned environment.