ROFT-VINS: Robust Feature Tracking-based Visual-Inertial State Estimation for Harsh Environment
This addresses the problem of accurate localization for robots and cars in harsh environments, but appears incremental as it builds on existing VIO systems.
The paper tackled robust visual feature tracking in monocular camera images for visual-inertial state estimation, proposing a deep learning-based method that operates reliably in textureless environments and rapid lighting changes, and integrated it into VINS-Fusion to evaluate performance.
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.