Consistency analysis and improvement of vision-aided inertial navigation

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

Research output: Contribution to journalArticlepeer-review

202 Scopus citations


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.

Original languageEnglish (US)
Article number6605544
Pages (from-to)158-176
Number of pages19
JournalIEEE Transactions on Robotics
Issue number1
StatePublished - Feb 2014


  • Consistency
  • Nonlinear estimation
  • Observability analysis
  • Vision-aided inertial navigation


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