On-Manifold Preintegration for Real-Time Visual--Inertial Odometry

@article{Forster2017OnManifoldPF,
  title={On-Manifold Preintegration for Real-Time Visual--Inertial Odometry},
  author={C. Forster and L. Carlone and F. Dellaert and D. Scaramuzza},
  journal={IEEE Transactions on Robotics},
  year={2017},
  volume={33},
  pages={1-21}
}
  • C. Forster, L. Carlone, +1 author D. Scaramuzza
  • Published 2017
  • Computer Science, Mathematics
  • IEEE Transactions on Robotics
  • Current approaches for visual--inertial odometry (VIO) are able to attain highly accurate state estimation via nonlinear optimization. However, real-time optimization quickly becomes infeasible as the trajectory grows over time; this problem is further emphasized by the fact that inertial measurements come at high rate, hence, leading to the fast growth of the number of variables in the optimization. In this paper, we address this issue by preintegrating inertial measurements between selected… CONTINUE READING

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