Towards hierarchical representation of space

Various robotic systems, performing efficient navigation, localization and place recognition in their surrounding environments, have already been developed. These systems posess a representation of space that is based on some engineered knowledge. There is still no such system that would know about the structure of space in general, and whose knowledge would be obtained by learning. We believe that people learn about properties of space through interaction with the environment. Therefore, since people perform really well in the spatial related tasks, we expect that a robotic system that would obtain such knowledge would also perform better. With this in mind, we are developing an algorithm for learning a compositional hierarchical representation of space that is based on statistically significant observations. For now, we have focused on a two dimensional space, since many robots perceive their surroundings in two dimensions with the use of a laser range finder or a sonar. In this paper we evaluate our early work on this topic through room categorization problem. Based on the lower layers of the hierarchy, we obtained encouraging classification results with three different types of rooms.