  This paper presents a novel tightly-coupled monocular visual-inertial
Simultaneous Localization and Mapping algorithm, which provides accurate and
robust localization within the globally consistent map in real time on a
standard CPU. This is achieved by firstly performing the visual-inertial
extended kalman filter(EKF) to provide motion estimate at a high rate. However
the filter becomes inconsistent due to the well known linearization issues. So
we perform a keyframe-based visual-inertial bundle adjustment to improve the
consistency and accuracy of the system. In addition, a loop closure detection
and correction module is also added to eliminate the accumulated drift when
revisiting an area. Finally, the optimized motion estimates and map are fed
back to the EKF-based visual-inertial odometry module, thus the inconsistency
and estimation error of the EKF estimator are reduced. In this way, the system
can continuously provide reliable motion estimates for the long-term operation.
The performance of the algorithm is validated on public datasets and real-world
experiments, which proves the superiority of the proposed algorithm.
