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.

翻译：鲁棒的特征匹配构成了大多数视觉同时定位与地图构建（vSLAM）、视觉里程计、三维重建及运动恢复结构（SfM）算法的核心基础。然而，从纹理贫瘠场景中恢复特征匹配是一项重大挑战，至今仍是开放的研究领域。本文提出了一种基于点线特征的立体视觉里程计（StereoVO）技术，该技术采用基于注意力图神经网络的新型特征匹配机制，旨在雾、霾、雨、雪等恶劣天气条件以及夜间照明、眩光等动态光照条件下仍能良好运行。我们在多个真实与合成数据集上进行实验，验证了所提方法通过鲁棒的点线匹配在低能见度天气与光照条件下执行StereoVO的能力。结果表明，与当前最先进的线匹配算法相比，我们的方法实现了更多的线特征匹配，这些线特征与点特征匹配相结合后，能在恶劣天气与动态光照条件下始终保持稳定性能。