Homing Guidance for UAVs Using Monocular Vision-Based SLAM

Forunmannedaerialvehicles(UAVs)flyinginGPS-deniedenvironments, itisoftenbeneficial in terms of size, weight, and power constraints to rely on widely-available monocular cameras for guidance, navigation, and control. In this work, we explore a monocular vision-based simultaneous localization and mapping (SLAM) framework for the purpose of performing a “homing” maneuver towards a platform or other rigid body moving at initially unknown velocity in an unknown environment. The estimation framework relies on a Harris corner detector that generates distinctive “feature points” for a given image, which are used to generate a database of “features” in the environment. These feature point measurements are fused with measurements fromanonboardIMUtoestimatetheownshipstateandthevelocityofthemovingplatform. The resulting estimates are used in the homing mechanism. The vision-based estimation and homing framework has been evaluated in a MATLAB simulation and a higher-fidelity simulation with realistic physics, sensors, and synthetic imagery.