A Scale-based Interest Operator for Autonomous Robotic Exploration

For a variety of use cases, the utility of autonomous mobile robots is strengthened by a capacity to explore spatial environments with particular focus on objects or phenomena of interest. To generally support such a capacity, an agile, application-agnostic interest operator is constructed to visually guide focused autonomous exploration. An interest operator is proposed that serves to identify unique features in a scene in an application-agnostic manner by maintaining a sort of "short-term memory" and identifying regions that are unique within the context of what has already been ascertained about the scene, not necessarily what is unique or of interest for a given application. The proposed interest operator is particularly suited to render-based mapping and perception, taking advantage of the natural characterization of spaces comprising an environment as free, occupied, and unknown, and the rich, highly-detailed texture information supported in graphical renderings of visual scenes. With focus on two scales of image features, one being driven by discontinuities between surfaces and the other based on variations in texture of surfaces in a region, the proposed interest operator can inform robot sensing and navigation decisions and resource allocation during autonomous exploration. Results from numerical experiments demonstrate the efficacy of the approach.