Unmanned aerial vehicles (UAVs) are crucial for aerial mapping and general monitoring tasks. Recent progress in deep learning enabled automated semantic segmentation of imagery to facilitate the interpretation of large-scale complex environments. Commonly used supervised deep learning for segmentation relies on large amounts of pixel-wise labelled data, which is tedious and costly to annotate. The domain-specific visual appearance of aerial environments often prevents the usage of models pre-trained on a static dataset. To address this, we propose a novel general planning framework for UAVs to autonomously acquire informative training images for model re-training. We leverage multiple acquisition functions and fuse them into probabilistic terrain maps. Our framework combines the mapped acquisition function information into the UAV's planning objectives. In this way, the UAV adaptively acquires informative aerial images to be manually labelled for model re-training. Experimental results on real-world data and in a photorealistic simulation show that our framework maximises model performance and drastically reduces labelling efforts. Our map-based planners outperform state-of-the-art local planning.

翻译：无人驾驶飞行器(UAVs)对于空中绘图和一般监测任务至关重要。最近深层次学习的进展使得图像的自动语义分离能够促进大规模复杂环境的解释。通常使用的有监督的深层次分离学习依赖于大量的像素标签数据,这些数据既乏味又耗资备注。具体领域的航空环境视觉外观往往妨碍在静态数据集上预先培训的模型的使用。为此,我们提议为UAVs提供一个新的总体规划框架,以便自动获得用于模型再培训的信息化培训图像。我们利用多种获取功能,并将这些功能结合到概率性地形图中。我们的框架将所绘制的获取功能信息与UAVS的规划目标结合起来。这样,UAVS在适应性地获取了信息性航空图像,以便手工进行模型再培训。真实世界数据实验结果和光真模拟显示,我们的框架将模型性能最大化,并大幅降低标签工作。我们的地图规划者比当地规划更像化。