Learning for Ground Robot Navigation with Autonomous Data Collection

Abstract

Robot navigation using vision is a classic example of a scene understanding problem. We describe a novel approach to estimating the traversability of an unknown environment based on modern object recognition methods. Traversability is an example of an affordance jointly determined by the environment and the physical characteristics of a robot vehicle, whose definition is clear in context. However, it is extremely difficult to estimate the traversability of a given terrain structure in general, or to find rules which work for a wide variety of terrain types. However, by learning to recognize similar terrain structures, it is possible to leverage a limited amount of interaction between the robot and its environment into global statements about the traversability of the scene. We describe a novel on-line learning algorithm that learns to recognize terrain features from images and aggregate the traversability information acquired by a navigating robot. An important property of our method, which is desirable for any learning-based approach to object recognition, is the ability to autonomously acquire arbitrary amounts of training data as needed without any human intervention. Tests of our algorithm on a real robot in complicated unknown natural environments suggest that it is both robust and efficient.