Full STEAM ahead: Exactly sparse gaussian process regression for batch continuous-time trajectory estimation on SE(3)

Sean Anderson; Timothy D. Barfoot

IROS 2015poster111 citations

Full STEAM ahead: Exactly sparse gaussian process regression for batch continuous-time trajectory estimation on SE(3)

Abstract

This paper shows how to carry out batch continuous-time trajectory estimation for bodies translating and rotating in three-dimensional (3D) space, using a very efficient form of Gaussian-process (GP) regression. The method is fast, singularity-free, uses a physically motivated prior (the mean is constant body-centric velocity), and permits trajectory queries at arbitrary times through GP interpolation. Landmark estimation can be folded in to allow for simultaneous trajectory estimation and mapping (STEAM), a variant of SLAM.

BibTeX

@inproceedings{iros2015_fullsteamaheadex,
  title = {Full STEAM ahead: Exactly sparse gaussian process regression for batch continuous-time trajectory estimation on SE(3)},
  author = {Sean Anderson and Timothy D. Barfoot},
  booktitle = {IROS 2015},
  year = {2015}
}