Information Technology is a term that encompasses all forms of technology used to create and manipulate information in its various forms. The primary goal of the Centre for Artificial Intelligence Research (CAIR) is to provide strong leadership and a stimulating environment for the development of these different forms of technology in New Zealand. However, our desire is that our research will not be ivory-towered. Since AUT is the only university of technology in this country, this emphasis is an important one; our research must not only be at the leading edge but must also be practically useful.
Given the widely ranging possibilities of research in this area, the Centre has decided to stay focused in its foundation years. It has, in consultation with the School of Computing and Mathematical Sciences and KEDRI, decided to focus on three main areas of research and development work, namely human language technology, speech technology, and robotics.
This paper shows how an indoor mobile robot equipped with a laser sensor and an odometer computes its global map by associating landmarks found in the environment. The approach developed is based on the observation that humans and animals detects where they are in the surrounding by comparing their spatial relation to some known or recognized objects in the environments, i.e. landmarks. In this case, landmarks are defined as 2D surfaces detected in the robot’s surroundings. They are recognised if they are detected in two successive views. From a cognitive standpoint, this work is inspired by two assumptions about the world; (a) the world is relatively stable and (2) there is a significant overlap of spatial information between successive views. In the implementation, the global map is first initialised with the robot’s first view, and then updated each time landmarks are found at every two successive views. The difference here is, where most robot mapping work integrates everything they see in their update, this work takes advantage of updating only the landmarks before adding the nearby objects associated with them. By association, the map is built without error corrections and the final map produced is not metrically precise.