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 prob...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/3/743 |
| _version_ | 1828109239122395136 |
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| author | Akkas Haque Ahmed Elsaharti Tarek Elderini Mohamed Atef Elsaharty Jeremiah Neubert |
| author_facet | Akkas Haque Ahmed Elsaharti Tarek Elderini Mohamed Atef Elsaharty Jeremiah Neubert |
| author_sort | Akkas Haque |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T10:59:21Z |
| format | Article |
| id | doaj.art-273fe578893e4a22bc73bac6489c7e85 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T10:59:21Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-273fe578893e4a22bc73bac6489c7e852022-12-22T04:28:40ZengMDPI AGSensors1424-82202020-01-0120374310.3390/s20030743s20030743UAV Autonomous Localization Using Macro-Features Matching with a CAD ModelAkkas Haque0Ahmed Elsaharti1Tarek Elderini2Mohamed Atef Elsaharty3Jeremiah Neubert4Department of Mechanical Engineering, University of North Dakota (UND), Upson II Room 266, 243 Centennial Drive, Stop 8359, Grand Forks, ND 58202, USADepartment of Mechanical Engineering, University of North Dakota (UND), Upson II Room 266, 243 Centennial Drive, Stop 8359, Grand Forks, ND 58202, USASchool of Electrical Engineering and Computer Science, University of North Dakota (UND), Upson II Room 369. 243 Centennial Drive, Stop 7165, Grand Forks, ND 58202, USADepartment of Mechanical Engineering, University of North Dakota (UND), Upson II Room 266, 243 Centennial Drive, Stop 8359, Grand Forks, ND 58202, USADepartment of Mechanical Engineering, University of North Dakota (UND), Upson II Room 266, 243 Centennial Drive, Stop 8359, Grand Forks, ND 58202, USAResearch 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.https://www.mdpi.com/1424-8220/20/3/743autonomous localization3d registrationuavgps-denied environmentreal-time |
| spellingShingle | Akkas Haque Ahmed Elsaharti Tarek Elderini Mohamed Atef Elsaharty Jeremiah Neubert UAV Autonomous Localization Using Macro-Features Matching with a CAD Model Sensors autonomous localization 3d registration uav gps-denied environment real-time |
| title | UAV Autonomous Localization Using Macro-Features Matching with a CAD Model |
| title_full | UAV Autonomous Localization Using Macro-Features Matching with a CAD Model |
| title_fullStr | UAV Autonomous Localization Using Macro-Features Matching with a CAD Model |
| title_full_unstemmed | UAV Autonomous Localization Using Macro-Features Matching with a CAD Model |
| title_short | UAV Autonomous Localization Using Macro-Features Matching with a CAD Model |
| title_sort | uav autonomous localization using macro features matching with a cad model |
| topic | autonomous localization 3d registration uav gps-denied environment real-time |
| url | https://www.mdpi.com/1424-8220/20/3/743 |
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