Unmannded aircraft systems, visual-inertial odometry, relative navigation


The future impact of small unmanned aircraft will depend in part on how well they can navigate in GPS-denied and GPS-degraded environments. While several GPS-denied navigation methods have been introduced, small fixed-wing aircraft have, for the most part, been neglected. This paper introduces a method to enable GPS-denied fixed-wing flight while accounting for fixed-wing-specific sensing requirements. This work uses a methodology called relative navigation as an overarching framework. The development of an odometry-like, front-end, EKF-based estimator that utilizes only a monocular camera and an inertial measurement unit is presented. The filter uses the measurement model of the multi-state-constraint Kalman filter. The filter also regularly resets its origin in coordination with the declaration of keyframe images. The keyframe-to-keyframe odometry estimates and their covariances are provided to a global back end that represents the global state as a pose graph. The back end is better suited to represent nonlinear uncertainties and incorporate opportunistic global constraints. In addition to the front-end development, we provide a method to account for front-end velocity bias in the back-end optimization. The paper provides simulation and hardware flight-test results of the front-end estimator and several back-end optimization examples to show the value of the proposed method.

Document Type

Working Paper

Publication Date





Ira A. Fulton College of Engineering and Technology


Mechanical Engineering

University Standing at Time of Publication

Graduate Student