Consistency Analysis and Improvement of Vision-aided Inertial Navigation

In this paper, we study estimator inconsistency in vision-aided inertial navigation systems (VINS) from the standpoint of system's observability. We postulate that a leading cause of inconsistency is the gain of spurious information along unobservable directions, which results in smaller uncert...

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Bibliographic Details
Published in:IEEE transactions on robotics 2014-02, Vol.30 (1), p.158-176
Main Authors: Hesch, Joel A., Kottas, Dimitrios G., Bowman, Sean L., Roumeliotis, Stergios I.
Format: Article
Language:English
Subjects:
Analytical models
Cameras
Consistency
Estimators
Experimentation
Gain
Inertial navigation
Jacobian matrices
Kalman filters
Localization
Mapping
Navigation systems
nonlinear estimation
Observability
observability analysis
Robot sensing systems
Simulation
Uncertainty
Vectors
vision-aided inertial navigation
Visual
Visualization
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Summary:In this paper, we study estimator inconsistency in vision-aided inertial navigation systems (VINS) from the standpoint of system's observability. We postulate that a leading cause of inconsistency is the gain of spurious information along unobservable directions, which results in smaller uncertainties, larger estimation errors, and 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. This framework is applicable to several variants of the VINS problem such as visual simultaneous localization and mapping (V-SLAM), as well as visual-inertial odometry using the multi-state constraint Kalman filter (MSC-KF). Our analysis, along with the proposed method to reduce inconsistency, are extensively validated with simulation trials and real-world experimentation.
ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2013.2277549