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      UAV Autonomous Localization using Macro-Features Matching with a CAD Model

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          Abstract

          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.

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          Author and article information

          Journal
          30 January 2020
          Article
          10.3390/s20030743
          2001.11610
          c6eb70a0-e67e-4e1b-8de7-a3fb1d793ff3

          http://creativecommons.org/licenses/by/4.0/

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          Custom metadata
          Sensors 2020, 20, 743
          cs.CV

          Computer vision & Pattern recognition
          Computer vision & Pattern recognition

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