VINS-Mono is a robust and versatile monocular visual-inertial state estimator designed to address the challenges of metric six degrees-of-freedom (DOF) state estimation using a camera and a low-cost inertial measurement unit (IMU). The system introduces a robust initialization procedure and failure recovery mechanism to handle the lack of direct distance measurement, which is a significant challenge in monocular visual-inertial systems. It employs a tightly-coupled, nonlinear optimization-based method to achieve high-accuracy visual-inertial odometry by fusing pre-integrated IMU measurements and feature observations. The system includes a loop detection module for relocalization with minimal computation overhead and performs four degrees-of-freedom pose graph optimization to ensure global consistency. The performance of VINS-Mono is validated through public datasets and real-world experiments, demonstrating superior accuracy compared to other state-of-the-art algorithms. The system has been successfully applied in various applications, including small-scale AR, medium-scale drone navigation, and large-scale state estimation tasks. The authors also provide open-source implementations for both PCs and iOS mobile devices.VINS-Mono is a robust and versatile monocular visual-inertial state estimator designed to address the challenges of metric six degrees-of-freedom (DOF) state estimation using a camera and a low-cost inertial measurement unit (IMU). The system introduces a robust initialization procedure and failure recovery mechanism to handle the lack of direct distance measurement, which is a significant challenge in monocular visual-inertial systems. It employs a tightly-coupled, nonlinear optimization-based method to achieve high-accuracy visual-inertial odometry by fusing pre-integrated IMU measurements and feature observations. The system includes a loop detection module for relocalization with minimal computation overhead and performs four degrees-of-freedom pose graph optimization to ensure global consistency. The performance of VINS-Mono is validated through public datasets and real-world experiments, demonstrating superior accuracy compared to other state-of-the-art algorithms. The system has been successfully applied in various applications, including small-scale AR, medium-scale drone navigation, and large-scale state estimation tasks. The authors also provide open-source implementations for both PCs and iOS mobile devices.