An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mapping

Abstract--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. 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. Our map-based planners replan a UAV's path (orange, bottom-left) to collect the most informative, e.g. Combined with advances in deep learning for semantic segmentation through fully convolutional improve the robot's vision capabilities in initially unknown neural networks (FCNs) [9, 10], deploying UAVs accelerates environments while minimising the total amount of humanlabelled automated scene understanding in large-scale and complex data. To this end, our approach exploits ideas from aerial environments [11]. Classical deep learning-based semantic AL research and incorporates them into a new informative segmentation models often used in this context are path planning (IPP) framework.