Line detection is widely used in many robotic tasks such as scene recognition, 3D reconstruction, and simultaneous localization and mapping (SLAM). Compared to points, lines can provide both low-level and high-level geometrical information for downstream tasks. In this paper, we propose a novel edge-based line detection algorithm, AirLine, which can be applied to various tasks. In contrast to existing learnable endpoint-based methods which are sensitive to the geometrical condition of environments, AirLine can extract line segments directly from edges, resulting in a better generalization ability for unseen environments. Also to balance efficiency and accuracy, we introduce a region-grow algorithm and local edge voting scheme for line parameterization. To the best of our knowledge, AirLine is one of the first learnable edge-based line detection methods. Our extensive experiments show that it retains state-of-the-art-level precision yet with a 3-80 times runtime acceleration compared to other learning-based methods, which is critical for low-power robots.

翻译：直线检测广泛应用于机器人领域的诸多任务，如场景识别、三维重建以及同时定位与地图构建（SLAM）。与点相比，直线能够为下游任务提供从低层到高层的几何信息。本文提出了一种新型的基于边缘的直线检测算法AirLine，可应用于各类任务。与现有对运行环境几何条件敏感的、基于可学习端点的检测方法不同，AirLine可直接从边缘提取直线段，从而对未知环境具有更强的泛化能力。此外，为平衡效率与精度，我们引入区域生长算法与局部边缘投票机制来实现直线参数化。据我们所知，AirLine是首批可学习的基于边缘的直线检测方法之一。大量实验表明，该方法在保持最先进水准精度的同时，运行速度相较于其他基于学习的方法提升了3至80倍，这对低功耗机器人至关重要。