Batch nonlinear continuous-time trajectory estimation as exactly sparse Gaussian process regression

@article{Anderson2015BatchNC,
  title={Batch nonlinear continuous-time trajectory estimation as exactly sparse Gaussian process regression},
  author={Sean R. Anderson and Tim D. Barfoot and Chi Hay Tong and Simo S{\"a}rkk{\"a}},
  journal={Autonomous Robots},
  year={2015},
  volume={39},
  pages={221-238}
}
In this paper, we revisit batch state estimation through the lens of Gaussian process (GP) regression. We consider continuous-discrete estimation problems wherein a trajectory is viewed as a one-dimensional GP, with time as the independent variable. Our continuous-time prior can be defined by any nonlinear, time-varying stochastic differential equation driven by white noise; this allows the possibility of smoothing our trajectory estimates using a variety of vehicle dynamics models (e.g… 
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  • S. Anderson, T. Barfoot
  • Engineering
    2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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