Abstract
In this paper, we study estimator inconsistency in Vision-aided Inertial Navigation Systems (VINS) from a standpoint of system observability. We postulate that a leading cause of inconsistency is the gain of spurious information along unobservable directions, resulting in smaller uncertainties, larger estimation errors, and possibly even divergence.We develop an Observability-Constrained VINS (OC-VINS), which explicitly enforces the unobservable directions of the system, hence preventing spurious information gain and reducing inconsistency. Our analysis, along with the proposed method for reducing inconsistency, are extensively validated with simulation trials and real-world experiments.
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Keywords
- Extend Kalman Filter
- Inertial Measurement Unit
- Unscented Kalman Filter
- Observability Property
- Essential Matrix
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Hesch, J.A., Kottas, D.G., Bowman, S.L., Roumeliotis, S.I. (2013). Towards Consistent Vision-Aided Inertial Navigation. In: Frazzoli, E., Lozano-Perez, T., Roy, N., Rus, D. (eds) Algorithmic Foundations of Robotics X. Springer Tracts in Advanced Robotics, vol 86. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36279-8_34
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DOI: https://doi.org/10.1007/978-3-642-36279-8_34
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