Real-Time Information-Theoretic Exploration with Gaussian Mixture Model Maps

This paper develops an exploration framework that leverages Gaussian mixture models (GMMs) for high-fidelity perceptual modeling and exploits the compactness of the distributions for information sharing in communications-constrained applications. State-of-the-art, high-resolution perceptual modeling techniques do not always consider the implications of transferring the model across limited bandwidth communications channels, which is critical for real time information sharing. To bridge this gap in the state of the art, this paper presents a system that compactly represents sensor observations as GMMs and maintains a local occupancy grid map for a sampling-based motion planner that maximizes an information-theoretic objective function. The method is extensively evaluated in long duration simulations on an embedded PC and deployed to an aerial robot equipped with a 3D LiDAR. The result is significant memory efficiency as compared to state-of-the-art techniques.