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.

本文提出了基于关键帧的视觉惯性同步定位与地图构建（SLAM）方法，通过在线环路闭合和非线性优化方法，使得我们的系统在单目和立体相机上具有实时、准确和鲁棒的位姿估计能力，支持先前建立地图的重定位和持续的 SLAM 操作，并在室内和户外公开数据集上展示了我们方法高精度、高效的性能。