基于使用注意力图神经网络的深度学习点线特征匹配的立体视觉里程计
Stereo Visual Odometry with Deep Learning-Based Point and Line Feature Matching using an Attention Graph Neural Network
AI总结：
提出一种基于注意力图神经网络的StereoVO技术，通过深度学习匹配点线特征，在雾、雨、雪及夜间眩光等低能见度条件下实现了优于现有算法的稳健特征匹配与定位性能。
摘要：
稳健的特征匹配构成了大多数视觉同步定位与建图（vSLAM）、视觉里程计、3D重建以及运动恢复结构（SfM）算法的骨干。然而，从弱纹理场景中恢复特征匹配是一项重大挑战，且仍然是一个开放的研究领域。在本文中，我们提出了一种基于点特征和线特征的立体视觉里程计（StereoVO）技术，该技术使用了一种基于注意力图神经网络的新型特征匹配机制，旨在即使在雾、霾、雨和雪等恶劣天气条件，以及夜间光照和眩光场景等动态光照条件下也能良好运行。我们在多个真实和合成数据集上进行了实验，通过稳健的点和线匹配来验证我们的方法在低能见度天气和光照条件下执行StereoVO的能力。结果表明，我们的方法比最先进的线匹配算法获得了更多的线特征匹配，当与点特征匹配互补时，在恶劣天气和动态光照条件下表现始终良好。
英文摘要：
Robust feature matching forms the backbone for most Visual Simultaneous Localization and Mapping (vSLAM), visual odometry, 3D reconstruction, and Structure from Motion (SfM) algorithms. However, recovering feature matches from texture-poor scenes is a major challenge and still remains an open area of research. In this paper, we present a Stereo Visual Odometry (StereoVO) technique based on point and line features which uses a novel feature-matching mechanism based on an Attention Graph Neural Network that is designed to perform well even under adverse weather conditions such as fog, haze, rain, and snow, and dynamic lighting conditions such as nighttime illumination and glare scenarios. We perform experiments on multiple real and synthetic datasets to validate the ability of our method to perform StereoVO under low visibility weather and lighting conditions through robust point and line matches. The results demonstrate that our method achieves more line feature matches than state-of-the-art line matching algorithms, which when complemented with point feature matches perform consistently well in adverse weather and dynamic lighting conditions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
