RTAPM: A Robust Top-View Absolute Positioning Method with Visual–Inertial Assisted Joint Optimization
In challenging environments such as disaster aid or forest rescue, unmanned aerial vehicles (UAVs) have been hampered by inconsistent or even denied global navigation satellite system (GNSS) signals, resulting in UAVs becoming incapable of operating normally. Currently, there is no unmanned aerial v...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/9/1/37 |
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| author | Pengfei Tong Xuerong Yang Xuanzhi Peng Longfei Wang |
| author_facet | Pengfei Tong Xuerong Yang Xuanzhi Peng Longfei Wang |
| author_sort | Pengfei Tong |
| collection | DOAJ |
| description | In challenging environments such as disaster aid or forest rescue, unmanned aerial vehicles (UAVs) have been hampered by inconsistent or even denied global navigation satellite system (GNSS) signals, resulting in UAVs becoming incapable of operating normally. Currently, there is no unmanned aerial vehicle (UAV) positioning method that is capable of substituting or temporarily replacing GNSS positioning. This study proposes a reliable UAV top-down absolute positioning method (RTAPM) based on a monocular RGB camera that employs joint optimization and visual–inertial assistance. The proposed method employs a bird’s-eye view monocular RGB camera to estimate the UAV’s moving position. By comparing real-time aerial images with pre-existing satellite images of the flight area, utilizing components such as template geo-registration, UAV motion constraints, point–line image matching, and joint state estimation, a method is provided to substitute satellites and obtain short-term absolute positioning information of UAVs in challenging and dynamic environments. Based on two open-source datasets and real-time flight experimental tests, the method proposed in this study has significant advantages in positioning accuracy and system robustness over existing typical UAV absolute positioning methods, and it can temporarily replace GNSS for application in challenging environments such as disaster aid or forest rescue. |
| format | Article |
| id | doaj-art-a89fb1ee783c466f8ed9d1ecfd02fe17 |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-a89fb1ee783c466f8ed9d1ecfd02fe172025-01-24T13:29:44ZengMDPI AGDrones2504-446X2025-01-01913710.3390/drones9010037RTAPM: A Robust Top-View Absolute Positioning Method with Visual–Inertial Assisted Joint OptimizationPengfei Tong0Xuerong Yang1Xuanzhi Peng2Longfei Wang3The School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen 518106, ChinaThe School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen 518106, ChinaThe School of College of Engineering, Shantou University, Shantou 515063, ChinaThe School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen 518106, ChinaIn challenging environments such as disaster aid or forest rescue, unmanned aerial vehicles (UAVs) have been hampered by inconsistent or even denied global navigation satellite system (GNSS) signals, resulting in UAVs becoming incapable of operating normally. Currently, there is no unmanned aerial vehicle (UAV) positioning method that is capable of substituting or temporarily replacing GNSS positioning. This study proposes a reliable UAV top-down absolute positioning method (RTAPM) based on a monocular RGB camera that employs joint optimization and visual–inertial assistance. The proposed method employs a bird’s-eye view monocular RGB camera to estimate the UAV’s moving position. By comparing real-time aerial images with pre-existing satellite images of the flight area, utilizing components such as template geo-registration, UAV motion constraints, point–line image matching, and joint state estimation, a method is provided to substitute satellites and obtain short-term absolute positioning information of UAVs in challenging and dynamic environments. Based on two open-source datasets and real-time flight experimental tests, the method proposed in this study has significant advantages in positioning accuracy and system robustness over existing typical UAV absolute positioning methods, and it can temporarily replace GNSS for application in challenging environments such as disaster aid or forest rescue.https://www.mdpi.com/2504-446X/9/1/37point–line image matchingmotion constraintsjoint state estimationsatellite signals deniedabsolute positioning |
| spellingShingle | Pengfei Tong Xuerong Yang Xuanzhi Peng Longfei Wang RTAPM: A Robust Top-View Absolute Positioning Method with Visual–Inertial Assisted Joint Optimization Drones point–line image matching motion constraints joint state estimation satellite signals denied absolute positioning |
| title | RTAPM: A Robust Top-View Absolute Positioning Method with Visual–Inertial Assisted Joint Optimization |
| title_full | RTAPM: A Robust Top-View Absolute Positioning Method with Visual–Inertial Assisted Joint Optimization |
| title_fullStr | RTAPM: A Robust Top-View Absolute Positioning Method with Visual–Inertial Assisted Joint Optimization |
| title_full_unstemmed | RTAPM: A Robust Top-View Absolute Positioning Method with Visual–Inertial Assisted Joint Optimization |
| title_short | RTAPM: A Robust Top-View Absolute Positioning Method with Visual–Inertial Assisted Joint Optimization |
| title_sort | rtapm a robust top view absolute positioning method with visual inertial assisted joint optimization |
| topic | point–line image matching motion constraints joint state estimation satellite signals denied absolute positioning |
| url | https://www.mdpi.com/2504-446X/9/1/37 |
| work_keys_str_mv | AT pengfeitong rtapmarobusttopviewabsolutepositioningmethodwithvisualinertialassistedjointoptimization AT xuerongyang rtapmarobusttopviewabsolutepositioningmethodwithvisualinertialassistedjointoptimization AT xuanzhipeng rtapmarobusttopviewabsolutepositioningmethodwithvisualinertialassistedjointoptimization AT longfeiwang rtapmarobusttopviewabsolutepositioningmethodwithvisualinertialassistedjointoptimization |