DeepSemanticHPPC: Hypothesis-based Planning over Uncertain Semantic Point Clouds
This addresses the challenge of reliable navigation in unstructured environments for robotics applications, though it appears incremental as it builds on existing planning and perception techniques.
The paper tackles the problem of planning safe paths in unstructured environments by proposing DeepSemanticHPPC, an uncertainty-aware hypothesis-based planner that iteratively reduces semantic uncertainty along paths, resulting in successful planning in real-world scenarios where existing methods often fail.
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.