  We propose a novel feature re-identification method for real-time
visual-inertial SLAM. The front-end module of the state-of-the-art
visual-inertial SLAM methods (e.g. visual feature extraction and matching
schemes) relies on feature tracks across image frames, which are easily broken
in challenging scenarios, resulting in insufficient visual measurement and
accumulated error in pose estimation. In this paper, we propose an efficient
drift-less SLAM method by re-identifying existing features from a
spatial-temporal sensitive sub-global map. The re-identified features over a
long time span serve as augmented visual measurements and are incorporated into
the optimization module which can gradually decrease the accumulative error in
the long run, and further build a drift-less 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 [11], our method
achieves 67.3% and 87.5% absolute translation error reduction with only a small
additional computational cost on two public SLAM benchmark DBs: EuRoC and
TUM-VI respectively.
