A White-Noise-On-Jerk Motion Prior for Continuous-Time Trajectory Estimation on SE(3)
This is an incremental improvement for robotics and SLAM applications, addressing a specific limitation in trajectory estimation.
The paper tackled the problem of bias in continuous-time trajectory estimation by deriving a white-noise-on-jerk motion prior that encourages constant body-centric acceleration, showing it greatly outperforms previous methods in solution accuracy across datasets.
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.