Learning to Navigate: Exploiting Deep Networks to Inform Sample-Based Planning During Vision-Based Navigation
Learning to Navigate: Exploiting Deep Networks to Inform Sample-Based Planning During Vision-Based Navigation
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学习导航：在基于视觉的导航过程中利用深度网络为基于样本的规划提供信息
DOI：
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发表时间：
2018
期刊：
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通讯作者：
P. Vela
中科院分区：
文献类型：
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作者：
Justin S. Smith;J. Hwang;Fu;P. Vela
最近深度学习在导航中的应用已经产生了端到端的导航解决方案，其中视觉传感器输入被映射到控制信号或运动基元。由此产生的视觉导航策略在避免碰撞方面非常有效，并且在实时操作时具有与传统反应式导航算法相匹配的性能。人们普遍认为，这些解决方案无法提供与全局规划器相同水平的性能。然而，目前尚不清楚如何将此类端到端系统集成到完整的导航管道中。我们在完整的导航管道中评估典型的端到端解决方案，以暴露其弱点。这样做阐明了如何更好地将深度学习方法集成到导航管道中。特别是，我们表明它们是为基于样本的规划者提供知情样本的有效手段。与传统规划器进行比较的受控模拟表明，在保持导航性能的同时，样本数量可以减少一个数量级。移动机器人上的实施与模拟的性能结果相匹配。
Recent applications of deep learning to navigation have generated end-to-end navigation solutions whereby visual sensor input is mapped to control signals or to motion primitives. The resulting visual navigation strategies work very well at collision avoidance and have performance that matches traditional reactive navigation algorithms while operating in real-time. It is accepted that these solutions cannot provide the same level of performance as a global planner. However, it is less clear how such end-to-end systems should be integrated into a full navigation pipeline. We evaluate the typical end-to-end solution within a full navigation pipeline in order to expose its weaknesses. Doing so illuminates how to better integrate deep learning methods into the navigation pipeline. In particular, we show that they are an efficient means to provide informed samples for sample-based planners. Controlled simulations with comparison against traditional planners show that the number of samples can be reduced by an order of magnitude while preserving navigation performance. Implementation on a mobile robot matches the simulated performance outcomes.