Title
Consistency Analysis and Improvement of Vision-aided Inertial Navigation
Abstract
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.
Year
DOI
Venue
2014
10.1109/TRO.2013.2277549
IEEE Transactions on Robotics
Keywords
Field
DocType
Jacobian matrices,Observability,Vectors,Analytical models,Cameras,Robot sensing systems,Visualization
Inertial navigation system,Observability,Control theory,Odometry,Kalman filter,Estimation theory,Simultaneous localization and mapping,Spurious relationship,Unobservable,Mathematics
Journal
Volume
Issue
ISSN
30
1
1552-3098
Citations 
PageRank 
References 
34
1.05
29
Authors
4
Name
Order
Citations
PageRank
Joel A. Hesch127313.62
Dimitrios G. Kottas22019.35
Sean L. Bowman31788.12
Stergios I. Roumeliotis42124151.96