论文标题
通过分销学习，基于快速和计算有效抽样的本地探索计划
Fast and Compute-efficient Sampling-based Local Exploration Planning via Distribution Learning
论文作者
论文摘要
探索是机器人技术中的一个基本问题。尽管基于抽样的计划者表现出高性能，但它们通常是计算大量的，并且可以表现出很高的差异。为此，我们建议基于机器人地图中的空间上下文直接学习信息意见的基本分布。我们进一步探讨了各种方法来学习信息增益。我们在彻底的实验评估中表明，我们提出的系统将勘探性能提高了多达28％的经典方法，并发现除了抽样分布外，学习收益还可以提供有利的性能与计算限制系统的计算权衡。我们在模拟和低成本移动机器人中证明，我们的系统可以很好地推广到不同的环境。
Exploration is a fundamental problem in robotics. While sampling-based planners have shown high performance, they are oftentimes compute intensive and can exhibit high variance. To this end, we propose to directly learn the underlying distribution of informative views based on the spatial context in the robot's map. We further explore a variety of methods to also learn the information gain. We show in thorough experimental evaluation that our proposed system improves exploration performance by up to 28% over classical methods, and find that learning the gains in addition to the sampling distribution can provide favorable performance vs. compute trade-offs for compute-constrained systems. We demonstrate in simulation and on a low-cost mobile robot that our system generalizes well to varying environments.
