Title:
A Monocular Vision-aided Inertial Navigation System with Improved Numerical Stability
A Monocular Vision-aided Inertial Navigation System with Improved Numerical Stability
Author(s)
Magree, Daniel
Johnson, Eric N.
Johnson, Eric N.
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Abstract
This paper develops a monocular vision-aided inertial navigation system based on the factored extended Kalman filter (EKF) proposed by Bierman and Thornton. The simultaneous localization and mapping (SLAM) algorithm measurement update and propagation steps are formulated in terms of the factored covariance matrix P = UDUT, and a novel method for efficiently adding and removing features from the covariance factors is
presented. The system is compared to the standard EKF formulation in navigation performance and computational requirements. The proposed method is shown to improve
numerical stability with minimal impact on computational requirements. Flight test results are presented which demonstrate navigation performance with a controller in the
loop.
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Date Issued
2015-01
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Text
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Proceedings