  Planning in unstructured environments is challenging -- it relies on sensing,
perception, scene reconstruction, and reasoning about various uncertainties. We
propose DeepSemanticHPPC, a novel uncertainty-aware hypothesis-based planner
for unstructured environments. Our algorithmic pipeline consists of: a deep
Bayesian neural network which segments surfaces with uncertainty estimates; a
flexible point cloud scene representation; a next-best-view planner which
minimizes the uncertainty of scene semantics using sparse visual measurements;
and a hypothesis-based path planner that proposes multiple kinematically
feasible paths with evolving safety confidences given next-best-view
measurements. Our pipeline iteratively decreases semantic uncertainty along
planned paths, filtering out unsafe paths with high confidence. We show that
our framework plans safe paths in real-world environments where existing path
planners typically fail.

Robotics