Camera-IMU-based localization: Observability analysis and consistency improvement

Joel A. Hesch, Dimitrios G. Kottas, Sean L. Bowman, Stergios I. Roumeliotis

Research output: Contribution to journalArticlepeer-review

147 Scopus citations


This work investigates the relationship between system observability properties and estimator inconsistency for a Vision-aided Inertial Navigation System (VINS). In particular, first we introduce a new methodology for determining the unobservable directions of nonlinear systems by factorizing the observability matrix according to the observable and unobservable modes. Subsequently, we apply this method to the VINS nonlinear model and determine its unobservable directions analytically. We leverage our analysis to improve the accuracy and consistency of linearized estimators applied to VINS. Our key findings are evaluated through extensive simulations and experimental validation on real-world data, demonstrating the superior accuracy and consistency of the proposed VINS framework compared to standard approaches.

Original languageEnglish (US)
Pages (from-to)182-201
Number of pages20
JournalInternational Journal of Robotics Research
Issue number1
StatePublished - Jan 2014

Bibliographical note

Funding Information:
This work was supported by the University of Minnesota (DTC) and AFOSR (grant number FA9550-10-1-0567).

Copyright 2014 Elsevier B.V., All rights reserved.


  • Vision-aided inertial navigation
  • estimator consistency
  • observability analysis
  • visual-inertial odometry


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