We consider scenarios where a ground vehicle plans its path using data gathered by an aerial vehicle. In the aerial images, navigable areas of the scene may be occluded due to obstacles. Naively planning paths using aerial images may result in longer paths as a conservative planner may try to avoid regions that are occluded. We propose a modular, deep learning-based framework that allows the robot to predict the existence of navigable areas in the occluded regions. Specifically, we use image inpainting methods to fill in parts of the areas that are potentially occluded, which can then be semantically segmented to determine navigability. We use supervised neural networks for both modules. However, these predictions may be incorrect. Therefore, we extract uncertainty in these predictions and use a risk-aware approach that takes these uncertainties into account for path planning. We compare modules in our approach with non-learning-based approaches to show the efficacy of the proposed framework through photo-realistic simulations. The modular pipeline allows further improvement in path planning and deployment in different settings.

翻译：我们考虑了地面飞行器利用飞行器收集的数据规划其路径的情景。在空中图像中,可航行的场景区域可能由于障碍而隐蔽。使用航空图像的纳米规划路径可能导致更长的道路,因为保守的规划者可能试图避免隐蔽的区域。我们提出了一个模块化的深层次学习框架,使机器人能够预测隐蔽区域中可航行区域的存在。具体地说,我们使用图像油漆方法填补可能隐蔽的部分区域,然后可以用语义分割来确定可航行性。我们使用两个模块的监管神经网络。然而,这些预测可能是不正确的。因此,我们从这些预测中提取不确定性,并使用风险意识方法,在路径规划中考虑到这些不确定性。我们用非学习方法将模块与非学习方法进行比较,以便通过光现实模拟显示拟议框架的功效。模块化管道允许在不同环境中进一步改进路径的规划和部署。