Online Exploration of Tunnel Networks Leveraging Topological CNN-based World Predictions

Abstract

Robotic exploration requires adaptively selecting navigation goals that result in the rapid discovery and mapping of an unknown world. In many real-world environments, subtle structural cues can provide insight about the unexplored world, which may be exploited by a decision maker to improve the speed of exploration. In sparse subterranean tunnel networks, these cues come in the form of topological features, such as loops or dead-ends, that are often common across similar environments. We propose a method for learning these topological features using techniques borrowed from topological image segmentation and image inpainting to learn from a database of worlds. These world predictions then inform a frontier-based exploration policy. Our simulated experiments with a set of real-world mine environments and a database of procedurally-generated artificial tunnel networks demonstrate a substantial increase in the rate of area explored compared to techniques that do not attempt to predict and exploit topological features of the unexplored world.