1 research outputs found
UAV Autonomous Localization using Macro-Features Matching with a CAD Model
Research in the field of autonomous Unmanned Aerial Vehicles (UAVs) has
significantly advanced in recent years, mainly due to their relevance in a
large variety of commercial, industrial, and military applications. However,
UAV navigation in GPS-denied environments continues to be a challenging problem
that has been tackled in recent research through sensor-based approaches. This
paper presents a novel offline, portable, real-time in-door UAV localization
technique that relies on macro-feature detection and matching. The proposed
system leverages the support of machine learning, traditional computer vision
techniques, and pre-existing knowledge of the environment. The main
contribution of this work is the real-time creation of a macro-feature
description vector from the UAV captured images which are simultaneously
matched with an offline pre-existing vector from a Computer-Aided Design (CAD)
model. This results in a quick UAV localization within the CAD model. The
effectiveness and accuracy of the proposed system were evaluated through
simulations and experimental prototype implementation. Final results reveal the
algorithm's low computational burden as well as its ease of deployment in
GPS-denied environments