Before revision:  Reprojection error, IMU pre-integration error and raw GNSS measurement error are jointly optimized using bundle adjustment in a sliding window, and the asynchronism between images and raw GNSS measurements is considered. Marginalization is performed in the sliding window, and some methods dealing with noisy measurements and vulnerable situations are employed . Experimental results on public dataset in complex urban scenes prove that our proposed approach outperforms state-of-the-art visual-inertial SLAM, GNSS single point positioning, as well as a loose coupling approach, both in the scenes that mainly contain low-rise buildings and the scenes that contain urban canyons.
After revision:  Reprojection error, IMU pre-integration error and raw GNSS measurement error are jointly optimized using bundle adjustment in a sliding window, and the asynchronism between images and raw GNSS measurements is accounted for. In addition, issues such as marginalization, noisy measurements removal, as well as tackling vulnerable situations are employed . Experimental results on public dataset in complex urban scenes prove that our proposed approach outperforms state-of-the-art visual-inertial SLAM, GNSS single point positioning, as well as a loose coupling approach, both in the scenes that mainly contain low-rise buildings and the scenes that contain urban canyons.