Unmanned aerial vehicles (UAVs) are frequently used 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 publicly available datasets. 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：无人机（UAV）常被用于航空测绘与常规监测任务。深度学习的最新进展使得图像的自动语义分割成为可能，从而促进了对大规模复杂环境的解读。常用的监督式深度学习分割方法依赖于大量像素级标注数据，然而该标注过程既繁琐又成本高昂。航空环境特有的视觉特征往往导致无法直接使用在公共数据集上预训练的模型。为解决这一问题，我们提出一种新型通用规划框架，使无人机能够自主获取用于模型再训练的有效训练图像。我们利用多种采集函数并将其融合至概率地形图中。该框架将映射后的采集函数信息整合至无人机的规划目标中。通过这种方式，无人机自适应地采集需要人工标注的有效航空图像以进行模型再训练。基于真实世界数据与逼真仿真环境的实验结果表明，我们的框架能够最大化模型性能并显著降低标注工作量。基于地图的规划器在性能上超越了当前最先进的局部规划方法。