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通用监控任务。近年来深度学习的进展使得图像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译。常用的基于监督学习的语义分割需要大量像素级标注数据，而此类标注工作既繁琐又成本高昂。航空环境特有的视觉特征往往导致预训练模型无法直接应用于公开数据集。为解决这一问题，我们提出了一种面向无人机的新型通用规划框架，使其能够自主采集用于模型再训练的信息化训练图像。我们利用多种采集函数并将其融合为概率地形图，进而将地形图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人工标注的信息化航空图像，以支持模型再训练。在真实世界数据与逼真仿真环境中的实验结果表明，本框架能最大化模型性能，并显著降低标注工作量。基于地图的规划器在性能上优于当前最先进的局部规划方法。