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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.

We present a multirotor architecture capable of aggressive autonomous flight and collision-free teleoperation in unstructured, GPS-denied environments. The proposed system enables aggressive and safe autonomous flight around clutter by integrating recent advancements in visual-inertial state estimation and teleoperation. Our teleoperation framework maps user inputs onto smooth and dynamically feasible motion primitives. Collision-free trajectories are ensured by querying a locally consistent map that is incrementally constructed from forward-facing depth observations. Our system enables a non-expert operator to safely navigate a multirotor around obstacles at speeds of 10 m/s. We achieve autonomous flights at speeds exceeding 12 m/s and accelerations exceeding 12 m/s 2 in a series of outdoor field experiments that validate our approach.Authors are with the