用语义信息指导无人机实时导航，降低计算负担。
Real-Time Navigation for Autonomous Aerial Vehicles Using Video
- 设计新马尔可夫决策框架，减少视觉算法负载。
- 能耗降低，速度提升，准确率损失小。
- 适合资源受限的无人机系统使用。
基于机载摄像头的自主导航多依赖于三维点云构建与处理，过程耗时耗能。另一种快速构建可导航空间的方法是利用语义信息（如交通标志）引导智能体。然而，检测并响应语义信息需依赖计算机视觉算法（如目标检测），对计算资源有限的飞行器而言负担较重。为此，本文提出一种新型马尔可夫决策过程（MDP）框架，有效降低此类视觉算法的工作量。该框架适用于基于特征和神经网络的目标检测任务，并在开环、闭环仿真及软硬件协同模拟中进行测试。结果表明，相比静态特征与神经网络模型，本方法显著降低能耗与延迟，仅带来轻微准确率损失。
原文摘要 · Abstract (English)
Most applications in autonomous navigation using mounted cameras rely on the construction and processing of geometric 3D point clouds, which is an expensive process. However, there is another simpler way to make a space navigable quickly: to use semantic information (e.g., traffic signs) to guide the agent. However, detecting and acting on semantic information involves Computer Vision~(CV) algorithms such as object detection, which themselves are demanding for agents such as aerial drones with limited onboard resources. To solve this problem, we introduce a novel Markov Decision Process~(MDP) framework to reduce the workload of these CV approaches. We apply our proposed framework to both feature-based and neural-network-based object-detection tasks, using open-loop and closed-loop simulations as well as hardware-in-the-loop emulations. These holistic tests show significant benefits in energy consumption and speed with only a limited loss in accuracy compared to models based on static features and neural networks.
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