A monocular visual-inertial system (VINS), consisting of a camera and a
low-cost inertial measurement unit (IMU), forms the minimum sensor suite for
metric six degrees-of-freedom (DOF) state estimation. However, the lack of
direct distance measurement poses significant challenges in terms of IMU
processing, estimator initialization, extrinsic calibration, and nonlinear
optimization. In this work, we present VINS-Mono: a robust and versatile
monocular visual-inertial state estimator.Our approach starts with a robust
procedure for estimator initialization and failure recovery. A tightly-coupled,
nonlinear optimization-based method is used to obtain high accuracy
visual-inertial odometry by fusing pre-integrated IMU measurements and feature
observations. A loop detection module, in combination with our tightly-coupled
formulation, enables relocalization with minimum computation overhead.We
additionally perform four degrees-of-freedom pose graph optimization to enforce
global consistency. We validate the performance of our system on public
datasets and real-world experiments and compare against other state-of-the-art
algorithms. We also perform onboard closed-loop autonomous flight on the MAV
platform and port the algorithm to an iOS-based demonstration. We highlight
that the proposed work is a reliable, complete, and versatile system that is
applicable for different applications that require high accuracy localization.
We open source our implementations for both PCs and iOS mobile devices.Comment: journal pape
