Accurate Visual-Inertial SLAM by Feature Re-identification

Most of the state-of-the-art visual inertial SLAM methods pay less attention to 2D-2D and 3D-2D matching with more reliable features in a long time span, which easily results in continuous estimation drift. In this paper, we propose an efficient drift-free visual-inertial SLAM method by a pose guided feature matching method to re-identify existing features from a spatial-temporal sensitive sub-global map. The re-identified features serve as augmented visual measurements to anchor the current frame and gradually decrease the accumulated error in the long run. When incorporating the measurements into the optimization module, it benefits to build a drift-free global map in the system. Extensive experiments show that our feature re-identification method is both effective and efficient. Specifically, when combining the feature re-identification with the state-of-the-art SLAM method [1], our method achieves 67.3% and 87.5% absolute trajectory error reduction with only a small additional computational cost on two public SLAM benchmark DBs: EuRoC and TUM-VI respectively.