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Exploration of unknown environments is a fundamental problem in robotics andan essential component in numerous applications of autonomous systems. A majorchallenge in exploring unknown environments is that the robot has to plan withthe limited information available at each time step. While most currentapproaches rely on heuristics and assumption to plan paths based on thesepartial observations, we instead propose a novel way to integrate deep learninginto exploration by leveraging 3D scene completion for informed, safe, andinterpretable exploration mapping and planning. Our approach, SC-Explorer,combines scene completion using a novel incremental fusion mechanism and anewly proposed hierarchical multi-layer mapping approach, to guarantee safetyand efficiency of the robot. We further present an informative path planningmethod, leveraging the capabilities of our mapping approach and a novelscene-completion-aware information gain. While our method is generallyapplicable, we evaluate it in the use case of a Micro Aerial Vehicle (MAV). Wethoroughly study each component in high-fidelity simulation experiments usingonly mobile hardware, and show that our method can speed up coverage of anenvironment by 73% compared to the baselines with only minimal reduction in mapaccuracy. Even if scene completions are not included in the final map, we showthat they can be used to guide the robot to choose more informative paths,speeding up the measurement of the scene with the robot's sensors by 35%. Wevalidate our system on a fully autonomous MAV, showing rapid and reliable scenecoverage even in a complex cluttered environment. We make our methods availableas open-source.

SC-Explorer: Incremental 3D Scene Completion for Safe and Efficient Exploration Mapping and Planning