Relative navigation of fixed‐wing aircraft in GPS‐denied environments
Gary Ellingson, Kevin Brink, Timothy W. McLain
Abstract
This work enables GPS-denied flight on fixed-wing UAS by accounting for fixed-wing-specific sensing requirements and using 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 (IMU) 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 sent 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. We also introduce 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 performs several back-end optimizations on the front-end data.