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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Towards Consistent Vision-Aided Inertial Navigation
verfasst von : Joel A. Hesch, Dimitrios G. Kottas, Sean L. Bowman, Stergios I. Roumeliotis
Erschienen in: Algorithmic Foundations of Robotics X
Verlag: Springer Berlin Heidelberg
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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.