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A monocular visual-inertial system (VINS), consisting of a camera and alow-cost inertial measurement unit (IMU), forms the minimum sensor suite formetric six degrees-of-freedom (DOF) state estimation. However, the lack ofdirect distance measurement poses significant challenges in terms of IMUprocessing, estimator initialization, extrinsic calibration, and nonlinearoptimization. In this work, we present VINS-Mono: a robust and versatilemonocular visual-inertial state estimator.Our approach starts with a robustprocedure for estimator initialization and failure recovery. A tightly-coupled,nonlinear optimization-based method is used to obtain high accuracyvisual-inertial odometry by fusing pre-integrated IMU measurements and featureobservations. A loop detection module, in combination with our tightly-coupledformulation, enables relocalization with minimum computation overhead.Weadditionally perform four degrees-of-freedom pose graph optimization to enforceglobal consistency. We validate the performance of our system on publicdatasets and real-world experiments and compare against other state-of-the-artalgorithms. We also perform onboard closed-loop autonomous flight on the MAVplatform and port the algorithm to an iOS-based demonstration. We highlightthat the proposed work is a reliable, complete, and versatile system that isapplicable for different applications that require high accuracy localization.We open source our implementations for both PCs and iOS mobile devices.

VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator