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Vision-Aided Navigation

QuNav’s vision-aided inertial solution maintains robust and consistent navigation performance in GPS-denied scenarios.

QuNav’s vision-aided inertial mechanization supports:

  • Monocular video images

  • Higher-grade and consumer-grade MEMS inertial

  • Ground vehicle and UAV applications;


Imaging systems are increasingly used in various applications where they serve not only the purpose of object detection and recognition but also as a navigation aid. A key challenge of vision-aided navigation is consistency of its estimation performance. First, the assumption of a Gaussian distribution for measurement errors may not be valid due to outliers commonly resulting from complicated algorithmic processing of images (e.g., feature extraction, feature matching, and frame-to-frame tracking). Second, the conventional extended Kalman filter (EKF) is known to be optimistic in its estimation error covariance. The inconsistency stems from under-estimation of nonlinear errors and may be attributed to non-observability of some system states.


To enable consistent performance of vision-aided navigation, QuNav has developed a real-time prototype of Assured Vision-Aided Inertial Localization (AVAIL). AVAIL mitigates outliers in vision measurements with probabilistic data association filtering that:

  • Computes a probability of an outlier missed-detection by statistical gating, and

  • De-weights vision measurements accordingly.


Correct observability properties are enabled by a batch estimator that:

  • Processes feature tracks (rather than individual vision measurements),

  • Separates INS and feature error states (via a null-state projection), and

  • Applies inverse-depth parameterization of monocular vision measurements (to mitigate linearization errors).


Consistent estimation performance of AVAIL was successfully demonstrated for:

  • Ground vehicle and UAV test scenarios;

  • Higher-grade MEMS and consumer-grade MEMS inertial sensors aided by monocular video images.

Real time display

Real time display

Click to download feature articles from the ION NAVIGATION Journal and AIAA.

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