Unsupervised incremental learning for long-term autonomy

Abstract

We present an approach to automatically learn the visual appearance of an environment in terms of object classes. The procedure is totally unsupervised, incremental, and can be executed in real time. The traversability property of an unseen object is also learnt without human supervision by the interaction between the robot and the environment. An incremental version of affinity propagation, a state-of-the-art clustering procedure, is used to cluster image patches into groups of similar visual appearance. For each of these clusters, we obtain the probability of representing an obstacle through the interaction of the robot with the environment. This information then allows the robot to navigate safely through the environment based solely on visual information. Experimental results show that our method extracts meaningful clusters from the images and learns the appearance of objects efficiently. We show that the approach generalises well to both indoor and outdoor environments and that the amount of learning reduces as the robot explores the environment. This is a fundamental property for autonomous adaptation and long-term autonomy.