State Observability through Prior Knowledge: Analysis of the Height Map Prior for Track Cycling

Abstract

Inertial navigation systems suffer from unbounded errors in the position and orientation estimates. This drift can be corrected by applying prior knowledge, instead of using exteroceptive sensors. We want to show that the use of prior knowledge can yield full observability of the position and orientation. A previous study showed that track cyclers can be tracked drift-free with an IMU as the only sensor and the knowledge that the bike drives on the track. In this paper, we analyze the observability of the pose in the experiment we conducted. Furthermore, we improve the pose estimation of the previous study. The observability is analyzed by testing the weak observability criterion with a Jacobian rank test. The improved estimator is presented and evaluated on a dataset with three 60-round trials (10 km each). The average RMS is 1.08 m and the estimate is drift-free. The observability analysis reveals that the system can gain complete observability in the curves and observability of the orientation on the straight parts of the race track.

Sensors and Sensing Technologies for Indoor Positioning and Indoor Navigation

“…IMU based systems generally suffer from unbounded errors in the position and orientation estimates. To overcome these issues, in [ 4 ], the authors propose applying prior knowledge instead of using exteroceptive sensors for track cycling. In their paper, the authors effectively show that the use of prior knowledge can yield full observability of the position and orientation.…”

The Interacting Multiple Model Filter and Smoother on Boxplus-Manifolds

“…It only allows changes to the manifold, which do not break its structure. It gained attention in pose tracking in the last years since it is a general approach to handle manifolds in nonlinear filtering [ 19 , 20 ] and least squares optimization [ 21 ]. The ⊞-method encapsulates manifolds as black boxes, so that algorithms can handle them generically.…”