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

TL;DR

This paper introduces a white-noise-on-jerk motion prior for continuous-time trajectory estimation on SE(3), improving accuracy over traditional white-noise-on-acceleration priors by better modeling non-zero acceleration segments.

Contribution

The paper develops a novel white-noise-on-jerk prior that encourages constant acceleration, enhancing the STEAM framework for more accurate trajectory estimation.

Findings

Significantly outperforms previous white-noise-on-acceleration prior in accuracy

Effectively models non-zero acceleration segments in trajectories

Demonstrates robustness across multiple datasets

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 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.