Learning Localisation Based on Landmarks using Self-Organisation Kaustubh Chokshi, Stefan Wermter, and Cornelius Weber Centre for Hybrid Intelligent Systems, School of Computing and Technology, University of Sunderland St. Peter’s Campus, Sunderland SR6 0DD, United Kingdom http://www.his.sunderland.ac.uk/ Abstract. In order to have an autonomous robot, the robot must be able to nav- igate independently within an environment. Place cells are cells that respond to the environment the animal is in. In this paper we present a model of place cells based on Self Organising Maps. The aim of this paper is to show that localisation can be performed even without having a built in map. The model presented shows that the landmarks are selected without any human interference. After training, a robot can localise itself within a learnt environment. 1 Introduction Ideally an autonomous robot should have a self-contained system which allows it to adapt and modify its behaviour to all possible situations it might face. The classical method is to pre-define the internal model of the robot relating it to the external world. The problem which arises is that the external world is very complex thus making it often unpredictable [2]. With a pre-defined program the robot can navigate only within a highly controlled environment [8]. There are various ways to address the problem of localisation. The most common approach is to ignore the errors of localisation [7]. This has the advantage of being sim- ple but a major disadvantage is that it cannot be used as a global planning method. To overcome this problem, another technique updates the robot location by bar codes and laser sensors in the environment, thus giving it an explicit location. This method was purely motivated by the “go until you get there” philosophy [7]. Another approach was to use topological maps where symbolic information for localisation at certain points for instance gateways was being used [3, 12, 4]. With gateways as a navigational strat- egy the robot can change its direction for instance at intersections of hallways. Here the robot is not concerned with how long the hallway is but it knows that at the end of the hallway there is an intersection. Therefore the gateways are critical for localisation, path planning and map making. The main disadvantage here is that it is very hard to have unique gateways. Another technique of localisation is to match raw sensor data to an a priori map [7]. Usually the sensors used are distance sensors such as sonar, infrared, laser etc. The problem here is that the sensor data rarely comes in without noise but it can be used in highly restricted environments. It is used in certain industrial robots who operate in highly controlled environments but they fail to work in dynamic environ- ments. This problem is overcome by generating small local maps and then integrating