Terrain awareness is an essential milestone to enable truly autonomous off-road navigation. Accurately predicting terrain characteristics allows optimizing a vehicle's path against potential hazards. Recent methods use deep neural networks to predict traversability-related terrain properties in a self-supervised manner, relying on proprioception as a training signal. However, onboard cameras are inherently limited by their point-of-view relative to the ground, suffering from occlusions and vanishing pixel density with distance. This paper introduces a novel approach for self-supervised terrain characterization using an aerial perspective from a hovering drone. We capture terrain-aligned images while sampling the environment with a ground vehicle, effectively training a simple predictor for vibrations, bumpiness, and energy consumption. Our dataset includes 2.8 km of off-road data collected in forest environment, comprising 13 484 ground-based images and 12 935 aerial images. Our findings show that drone imagery improves terrain property prediction by 21.37 % on the whole dataset and 37.35 % in high vegetation, compared to ground robot images. We conduct ablation studies to identify the main causes of these performance improvements. We also demonstrate the real-world applicability of our approach by scouting an unseen area with a drone, planning and executing an optimized path on the ground.

翻译：地形感知是实现真正自主越野导航的关键里程碑。准确预测地形特征能够优化车辆路径以规避潜在危险。现有方法利用深度神经网络以自监督方式预测与通行性相关的地形属性，依赖本体感知作为训练信号。然而，车载相机因其相对于地面的视角存在固有局限，易受遮挡影响且像素密度随距离增加而衰减。本文提出一种利用悬停无人机航拍视角进行自监督地形表征的新方法。我们在使用地面车辆对环境进行采样的同时，获取地形对齐图像，从而有效训练用于预测振动、颠簸度和能量消耗的简易预测器。我们的数据集包含在森林环境中采集的2.8公里越野数据，涵盖13,484张地面图像和12,935张航拍图像。实验结果表明：与地面机器人图像相比，无人机图像使地形属性预测精度在整个数据集上提升21.37%，在高植被区域提升37.35%。我们通过消融研究确定了性能提升的主要原因。此外，我们通过使用无人机侦察未知区域、规划并执行地面优化路径，验证了该方法在现实场景中的适用性。