RA-L 201948 citations

A White-Noise-on-Jerk Motion Prior for Continuous-Time Trajectory Estimation on SE(3)

Tim Yuqing Tang, David Juny Yoon, Timothy D. Barfoot

Abstract

Simultaneous trajectory estimation and mapping (STEAM) offers an efficient approach to continuous-time trajectory estimation, by representing the trajectory as a Gaussian process (GP). Previous formulations of the STEAM framework use a GP prior that assumes white-noise-on-acceleration, with the prior mean encouraging constant body-centric velocity. We show that such a prior cannot sufficiently represent trajectory sections with nonzero acceleration, resulting in a bias to the posterior estimates. This letter derives a novel motion prior that assumes white-noise-on-jerk, where the prior mean encourages constant body-centric acceleration. With the new prior, we formulate a variation of STEAM that estimates the pose, body-centric velocity, and body-centric acceleration. By evaluating across several datasets, we show that the new prior greatly outperforms the white-noise-on-acceleration prior in terms of the solution accuracy.

BibTeX
@inproceedings{ral2019_awhitenoiseonjer,
  title = {A White-Noise-on-Jerk Motion Prior for Continuous-Time Trajectory Estimation on SE(3)},
  author = {Tim Yuqing Tang and David Juny Yoon and Timothy D. Barfoot},
  booktitle = {RA-L 2019},
  year = {2019}
}
A White-Noise-on-Jerk Motion Prior for Continuous-Time Trajectory Estimation on SE(3) · RA-L 2019