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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.

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