Vision-based Terrain Referenced Navigation for Unmanned Aerial Vehicles using Homography Relationship

Abstract

A vision-based terrain referenced navigation (TRN) system is addressed for autonomous navigation of unmanned aerial vehicles (UAVs). A typical TRN algorithm blends inertial navigation data with measured terrain information to estimate vehicle's position. In this paper, however, we replace the low-cost inertial navigation system (INS) with a monocular vision system. The homography decomposition algorithm is utilized to estimates the relative translational motion using features on the ground with simple assumptions. A numerical integration point-mass filter based on Bayesian estimation is employed to combine the translation information obtained from the vision system with the measured terrain height. Numerical simulations are constructed to evaluate the performance of the proposed method. The results show that the precise autonomous navigation of unmanned aircrafts is achieved by the vision-based TRN algorithm.