State Observability through Prior Knowledge: A Conceptional Paradigm in Inertial Sensing

Koller, Tom L.; Laue, Tim; Frese, Udo

doi:10.5220/0007952307810788

Cited by 2 publications

(2 citation statements)

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“…Their yaw is also constrained to follow the 1D track approximately, because they have to take the track counter clockwise. The 1D formulation of the track has the advantage that we can apply Theorem 1 from [5], which states that a 1D system is observable from gyrometer measurements alone if the rotation axis changes. At the entries and exits of the curves, the rotation axis changes.…”

Section: Initial Guessmentioning

confidence: 99%

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

Koller

Frese

2020

Sensors

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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.

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Section: Initial Guessmentioning

confidence: 99%

“…Fusing IMU measurements with prior knowledge is a powerful concept with which to gain drift-free estimates [5]. The knowledge allows one to observe otherwise unobservable states.…”

Section: Introductionmentioning

confidence: 99%