  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 non-zero
acceleration, resulting in a bias to the posterior estimates.
  This paper 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 solution accuracy.
