VINS-Mono is a robust and versatile monocular visual-inertial state estimator that combines a camera and a low-cost inertial measurement unit (IMU) to estimate six degrees-of-freedom (DOF) state information. The system addresses challenges in IMU processing, estimator initialization, extrinsic calibration, and nonlinear optimization. It uses a tightly-coupled, nonlinear optimization-based method to fuse pre-integrated IMU measurements and feature observations for high-accuracy visual-inertial odometry. A loop detection module enables relocalization with minimal computation overhead, and a four DOF pose graph optimization ensures global consistency. The system is validated on public datasets and real-world experiments, and tested on a MAV platform with onboard closed-loop autonomous flight. It is also ported to an iOS-based demonstration. VINS-Mono is open-sourced for both PCs and iOS devices. The system is complete, reliable, and applicable for various applications requiring high-accuracy localization. It improves upon previous works by incorporating better IMU pre-integration with bias correction, tightly-coupled relocalization, global pose graph optimization, and extensive experimental evaluation. The system is efficient and easy to use, with real-time performance for drone navigation, large-scale localization, and mobile AR applications.VINS-Mono is a robust and versatile monocular visual-inertial state estimator that combines a camera and a low-cost inertial measurement unit (IMU) to estimate six degrees-of-freedom (DOF) state information. The system addresses challenges in IMU processing, estimator initialization, extrinsic calibration, and nonlinear optimization. It uses a tightly-coupled, nonlinear optimization-based method to fuse pre-integrated IMU measurements and feature observations for high-accuracy visual-inertial odometry. A loop detection module enables relocalization with minimal computation overhead, and a four DOF pose graph optimization ensures global consistency. The system is validated on public datasets and real-world experiments, and tested on a MAV platform with onboard closed-loop autonomous flight. It is also ported to an iOS-based demonstration. VINS-Mono is open-sourced for both PCs and iOS devices. The system is complete, reliable, and applicable for various applications requiring high-accuracy localization. It improves upon previous works by incorporating better IMU pre-integration with bias correction, tightly-coupled relocalization, global pose graph optimization, and extensive experimental evaluation. The system is efficient and easy to use, with real-time performance for drone navigation, large-scale localization, and mobile AR applications.