用深度学习提升单目相机在恶劣环境下的特征跟踪能力
ROFT-VINS: Robust Feature Tracking-based Visual-Inertial State Estimation for Harsh Environment
- 基于深度学习实现鲁棒视觉特征跟踪
- 在无纹理和快速光照变化下仍保持稳定性能
- 可集成至VINS-Fusion系统，适合移动设备定位
SLAM（同时定位与地图构建）和里程计是机器人、汽车等移动设备定位的关键技术，依赖一个或多个传感器。尤其在基于摄像头的SLAM或里程计中，有效追踪视觉特征对系统性能有显著影响。本文提出一种方法，利用深度学习在单目相机图像中实现鲁棒的视觉特征跟踪，可在无纹理环境及快速光照变化条件下可靠运行。此外，我们将该方法集成到常用的视觉-惯性里程计系统VINS-Fusion（单目-惯性）中进行评估，验证其在复杂环境下的有效性。
原文摘要 · Abstract (English)
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.
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