Abstract: Navigating and exploring an unknown environment is a challenging task for autonomous robots, especially in complex and unstructured environments. We propose a new framework that can simultaneously accomplish multiple objectives that are essential to robot autonomy including identifying free space for navigation, building a metric-topological representation for mapping, and ensuring good spatial coverage for unknown space exploration. Different from existing work that model these critical objectives separately, we show that navigation, mapping, and exploration can be derived with the same foundation modeled with a sparse variant of Gaussian Process. Specifically, in our framework the robot navigates by following frontiers computed from a local Gaussian Process perception model, and along the way builds a map in a metric-topological form where nodes are adaptively selected from important perception frontiers. The topology expands towards unexplored areas by assessing a low-cost global uncertainty map also computed from a sparse Gaussian Process. Through evaluations in various cluttered and unstructured environments, we validate that the proposed framework can explore unknown environments faster and with a traveled distance less than the start-of-art frontier exploration approaches. Through field demonstration, we have begun to lay the groundwork for field robots to explore challenging environments such as forests that humans have yet to set foot in.
