IMU Preintegration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation

@inproceedings{Forster2015IMUPO,
  title={IMU Preintegration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation},
  author={Christian Forster and Luca Carlone and Frank Dellaert and Davide Scaramuzza},
  booktitle={Robotics: Science and Systems},
  year={2015}
}
Recent results in monocular visual-inertial navigation (VIN) have shown that optimization-based approaches outperform filtering methods in terms of accuracy due to their capability to relinearize past states. [] Key Method Our first contribution is a preintegration theory that properly addresses the manifold structure of the rotation group and carefully deals with uncertainty propagation.
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Robust initialization of monocular visual-inertial estimation on aerial robots
  • Tong Qin, S. Shen
  • Computer Science
    2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • 2017
TLDR
A robust on-the-fly estimator initialization algorithm to provide high-quality initial states for monocular visual-inertial systems (VINS) and makes the implementation open source, which is the initialization part integrated in the VINS-Mono1.
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