  Complementing images with inertial measurements has become one of the most
popular approaches to achieve highly accurate and robust real-time camera pose
tracking. In this paper, we present a keyframe-based approach to
visual-inertial simultaneous localization and mapping (SLAM) for monocular and
stereo cameras. Our visual-inertial SLAM system is based on a real-time capable
visual-inertial odometry method that provides locally consistent trajectory and
map estimates. We achieve global consistency in the estimate through online
loop-closing and non-linear optimization. Furthermore, our system supports
relocalization in a map that has been previously obtained and allows for
continued SLAM operation. We evaluate our approach in terms of accuracy,
relocalization capability and run-time efficiency on public indoor benchmark
datasets and on newly recorded outdoor sequences. We demonstrate
state-of-the-art performance of our system compared to a visual-inertial
odometry method and baseline visual SLAM approaches in recovering the
trajectory of the camera.
