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 learnable 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. To balance efficiency and accuracy, we introduce a region-grow algorithm and a 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 have shown that it retains state-of-the-art-level precision, yet with a 3 to 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场景识别、三维重建及同步定位与地图构建（SLAM）等众多机器人任务中。相较于点特征，直线能够为下游任务同时提供低级与高级几何信息。本文提出一种新颖的可学习边缘驱动直线检测算法AirLine，可适用于各类任务。与现有对环境几何条件敏感的可学习端点驱动方法不同，AirLine直接从边缘提取直线段，因而对未知环境具有更强的泛化能力。为平衡效率与精度，我们引入区域生长算法和局部边缘投票机制进行直线参数化。据我们所知，AirLine是首个可学习边缘驱动直线检测方法之一。大量实验表明，该方法在保持最先进精度水平的同时，相比其他学习方法实现了3至80倍的运行速度提升，这对于低功耗机器人至关重要。