Invariant filtering for wheeled vehicle localization with unknown wheel radius and unknown GNSS lever arm
<|reference_start|>Invariant filtering for wheeled vehicle localization with unknown wheel radius and unknown GNSS lever arm: We consider the problem of observer design for a nonholonomic car (more generally a wheeled robot) equipped with wheel speeds with unknown wheel radius, and whose position is measured via a GNSS antenna placed at an unknown position in the car. In a tutorial and unified exposition, we recall the recent theory of two-frame systems within the field of invariant Kalman filtering. We then show how to adapt it geometrically to address the considered problem, although it seems at first sight out of its scope. This yields an invariant extended Kalman filter having autonomous error equations, and state-independent Jacobians, which is shown to work remarkably well in simulations. The proposed novel construction thus extends the application scope of invariant filtering.<|reference_end|>
@article{chauchat2024invariant,
    title={Invariant filtering for wheeled vehicle localization with unknown wheel
  radius and unknown GNSS lever arm},
    author={Paul Chauchat (AMU SCI, AMU, LIS, DIAPRO), Silv`ere Bonnabel (CAOR),
  Axel Barrau},
    journal={2024 IEEE 63th Annual Conference on Decision and Control (CDC),
  Dec 2024, Milan, Italy},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.07050},
    primaryClass={cs.RO}
}