High Performance Outdoor Navigation from Overhead Data using Imitation Learning

High performance, long-distance autonomous navigation is a central problem for field robotics. Efficient navigation relies not only upon intelligent onboard systems for perception and planning, but also the effective use of prior maps and knowledge. While the availability and quality of low cost, high resolution satellite and aerial terrain data continues to rapidly improve, automated interpretation appropriate for robot planning and navigation remains difficult. Recently, a class of machine learning techniques have been developed that rely upon expert human demonstration to develop a function mapping overhead data to traversal cost. These algorithms choose the cost function so that planner behavior mimics an expert's demonstration as closely as possible. In this work, we extend these methods to automate interpretation of overhead data. We address key challenges, including interpolation-based planners, non-linear approximation techniques, and imperfect expert demonstration, necessary to apply these methods for learning to search for effective terrain interpretations. We validate our approach on a large scale outdoor robot during over 300 kilometers of autonomous traversal through complex natural environments.