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Open AccessArticle

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

Multi-Sensor Interactive Systems Group, University of Bremen, 28359 Bremen, Germany
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This paper is an extension version of the conference paper: Koller, T.L.; Frese, U. State Observability through Prior Knowledge: Tracking Track Cyclers with Inertial Sensors. In Proceedings of the 2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Pisa, Italy, 30 September–3 October 2019.
Sensors 2020, 20(9), 2438; https://doi.org/10.3390/s20092438
Received: 30 March 2020 / Revised: 20 April 2020 / Accepted: 21 April 2020 / Published: 25 April 2020
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. View Full-Text
Keywords: observability analysis; IMU; INS; prior knowledge; C = context knowledge; vehicle constraint; pose estimation; tracking observability analysis; IMU; INS; prior knowledge; C = context knowledge; vehicle constraint; pose estimation; tracking
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Koller, T.L.; Frese, U. State Observability through Prior Knowledge: Analysis of the Height Map Prior for Track Cycling. Sensors 2020, 20, 2438.

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