ROFT-VINS: 基于鲁棒特征跟踪的视觉-惯性状态估计用于恶劣环境
ROFT-VINS: Robust Feature Tracking-based Visual-Inertial State Estimation for Harsh Environment
AI总结：
本文提出一种利用深度学习在单目图像中鲁棒跟踪视觉特征的方法，适用于纹理缺失和快速光照变化的恶劣环境，并评估了其在VINS-Fusion系统中的性能。
摘要：
SLAM（同时定位与建图）和里程计是利用一个或多个传感器估计移动设备（如机器人和汽车）位置的重要系统。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。本文提出了一种方法，利用深度学习在单目相机图像中鲁棒地跟踪视觉特征。该方法在纹理缺失环境和快速光照变化情况下也能可靠运行。此外，我们通过将其整合到VINS-Fusion（单目-惯性）系统中，评估了所提出方法的性能。
英文摘要：
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.
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。
