Abstract
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.
Original language | English |
---|---|
Title of host publication | 2004 IEEE International Joint Conference on Neural Networks |
Subtitle of host publication | proceedings : Budapest, Hungary, 25-29 July, 2004 |
Publisher | IEEE |
Pages | 2501 - 2506 |
Number of pages | 6 |
Volume | 4 |
ISBN (Print) | 0-7803-8359-1 |
DOIs | |
Publication status | Published - 2004 |
Externally published | Yes |
Event | 2004 IEEE International Joint Conference on Neural Networks - Budapest, Hungary Duration: 25 Jul 2004 → 29 Jul 2004 |
Conference
Conference | 2004 IEEE International Joint Conference on Neural Networks |
---|---|
Abbreviated title | IJCNN |
Country/Territory | Hungary |
City | Budapest |
Period | 25/07/04 → 29/07/04 |