Keywords

Unmannded aircraft systems, visual-inertial odometry, relative navigation

Abstract

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

2019-08-07

College

Ira A. Fulton College of Engineering and Technology

Department

Mechanical Engineering

University Standing at Time of Publication

Graduate Student

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