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：无人机（UAV）对于航空测绘及通用监测任务至关重要。深度学习的最新进展使得图像自动化语义分割成为可能，从而促进了对大规模复杂环境的理解。常用的有监督深度学习分割方法依赖于大量像素级标注数据，这种标注工作既繁琐又成本高昂。航空环境的领域特异性外观往往导致无法使用在静态数据集上预训练的模型。为解决此问题，我们提出了一种全新的通用规划框架，使无人机能够自主获取信息丰富的训练图像以进行模型重训练。我们利用多种采集函数并将其融合到概率地形图中。该框架将映射后的采集函数信息整合到无人机的规划目标中，从而实现无人机自适应地获取信息丰富的航空图像，以供人工标注后用于模型重训练。在真实数据及高保真仿真环境中的实验结果表明，我们的框架能够最大化模型性能并大幅减少标注工作量。基于地图的规划器性能优于现有最先进的局部规划方法。