论文标题
DeepSemantIchPPC：基于假设的计划对不确定语义点云的计划
DeepSemanticHPPC: Hypothesis-based Planning over Uncertain Semantic Point Clouds
论文作者
论文摘要
在非结构化环境中的计划是具有挑战性的 - 它依赖于感应，感知，场景重建以及对各种不确定性的推理。我们提出了DeepSemantichPPC，这是一种基于非结构化环境的基于不确定性的假设计划者。我们的算法管道包括：一个深贝叶斯神经网络，该网络段表面不确定性估计；灵活的点云场景表示；第二好的策划者使用稀疏的视觉测量来最大程度地减少场景语义的不确定性；以及一个基于假设的路径规划师，提出了多个运动学上可行的路径，并具有不断发展的安全性信心给定次要视图的测量。我们的管道迭代地沿计划的路径降低语义不确定性，以高度置信度过滤了不安全的路径。我们表明，我们的框架计划在现有路径计划者通常失败的现实环境中的安全路径。
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.
