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Planning in unstructured environments is challenging -- it relies on sensing,perception, scene reconstruction, and reasoning about various uncertainties. Wepropose DeepSemanticHPPC, a novel uncertainty-aware hypothesis-based plannerfor unstructured environments. Our algorithmic pipeline consists of: a deepBayesian neural network which segments surfaces with uncertainty estimates; aflexible point cloud scene representation; a next-best-view planner whichminimizes the uncertainty of scene semantics using sparse visual measurements;and a hypothesis-based path planner that proposes multiple kinematicallyfeasible paths with evolving safety confidences given next-best-viewmeasurements. Our pipeline iteratively decreases semantic uncertainty alongplanned paths, filtering out unsafe paths with high confidence. We show thatour framework plans safe paths in real-world environments where existing pathplanners typically fail.

DeepSemanticHPPC: Hypothesis-based Planning over Uncertain Semantic Point Clouds