Before revision:  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:  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 show 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.