SLAM (Simultaneous Localization and Mapping) and Odometry are important systems for estimating the position of mobile devices, such as robots and cars, utilizing one or more sensors. Particularly in camera-based SLAM or Odometry, effectively tracking visual features is important as it significantly impacts system performance. In this paper, we propose a method that leverages deep learning to robustly track visual features in monocular camera images. This method operates reliably even in textureless environments and situations with rapid lighting changes. Additionally, we evaluate the performance of our proposed method by integrating it into VINS-Fusion (Monocular-Inertial), a commonly used Visual-Inertial Odometry (VIO) system.

翻译：SLAM（同步定位与地图构建）与里程计是移动设备（如机器人和汽车）利用一个或多个传感器进行位置估计的重要系统。特别是在基于相机的SLAM或里程计中，有效跟踪视觉特征至关重要，因为它显著影响系统性能。本文提出了一种利用深度学习在单目相机图像中鲁棒跟踪视觉特征的方法。该方法即使在无纹理环境和光照快速变化的情况下也能可靠运行。此外，我们通过将所提方法集成到常用的视觉-惯性里程计（VIO）系统VINS-Fusion（单目-惯性）中，对其性能进行了评估。