Visual Navigation Algorithm for Night Landing of Fixed-Wing Unmanned Aerial Vehicle
In the recent years, visual navigation has been considered an effective mechanism for achieving an autonomous landing of Unmanned Aerial Vehicles (UAVs). Nevertheless, with the limitations of visual cameras, the effectiveness of visual algorithms is significantly limited by lighting conditions. Ther...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/9/10/615 |
| _version_ | 1797476318258921472 |
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| author | Zhaoyang Wang Dan Zhao Yunfeng Cao |
| author_facet | Zhaoyang Wang Dan Zhao Yunfeng Cao |
| author_sort | Zhaoyang Wang |
| collection | DOAJ |
| description | In the recent years, visual navigation has been considered an effective mechanism for achieving an autonomous landing of Unmanned Aerial Vehicles (UAVs). Nevertheless, with the limitations of visual cameras, the effectiveness of visual algorithms is significantly limited by lighting conditions. Therefore, a novel vision-based autonomous landing navigation scheme is proposed for night-time autonomous landing of fixed-wing UAV. Firstly, due to the difficulty of detecting the runway caused by the low-light image, a strategy of visible and infrared image fusion is adopted. The objective functions of the fused and visible image, and the fused and infrared image, are established. Then, the fusion problem is transformed into the optimal situation of the objective function, and the optimal solution is realized by gradient descent schemes to obtain the fused image. Secondly, to improve the performance of detecting the runway from the enhanced image, a runway detection algorithm based on an improved Faster region-based convolutional neural network (Faster R-CNN) is proposed. The runway ground-truth box of the dataset is statistically analyzed, and the size and number of anchors in line with the runway detection background are redesigned based on the analysis results. Finally, a relative attitude and position estimation method for the UAV with respect to the landing runway is proposed. New coordinate reference systems are established, six landing parameters, such as three attitude and three positions, are further calculated by Orthogonal Iteration (OI). Simulation results reveal that the proposed algorithm can achieve 1.85% improvement of AP on runway detection, and the reprojection error of rotation and translation for pose estimation are <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0</mn><mo>.</mo><msup><mn>675</mn><mo>∘</mo></msup></mrow></semantics></math></inline-formula> and 0.581%, respectively. |
| first_indexed | 2024-03-09T20:56:10Z |
| format | Article |
| id | doaj.art-2060fc967cdf46518b300b0b58b2043a |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-09T20:56:10Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-2060fc967cdf46518b300b0b58b2043a2023-11-23T22:19:13ZengMDPI AGAerospace2226-43102022-10-0191061510.3390/aerospace9100615Visual Navigation Algorithm for Night Landing of Fixed-Wing Unmanned Aerial VehicleZhaoyang Wang0Dan Zhao1Yunfeng Cao2College of Astronautics, Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Street, Nanjing 211106, ChinaDepartment of Mechanical Engineering, University of Canterbury, 4800 Private Bag, Christchurch 8140, New ZealandCollege of Astronautics, Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Street, Nanjing 211106, ChinaIn the recent years, visual navigation has been considered an effective mechanism for achieving an autonomous landing of Unmanned Aerial Vehicles (UAVs). Nevertheless, with the limitations of visual cameras, the effectiveness of visual algorithms is significantly limited by lighting conditions. Therefore, a novel vision-based autonomous landing navigation scheme is proposed for night-time autonomous landing of fixed-wing UAV. Firstly, due to the difficulty of detecting the runway caused by the low-light image, a strategy of visible and infrared image fusion is adopted. The objective functions of the fused and visible image, and the fused and infrared image, are established. Then, the fusion problem is transformed into the optimal situation of the objective function, and the optimal solution is realized by gradient descent schemes to obtain the fused image. Secondly, to improve the performance of detecting the runway from the enhanced image, a runway detection algorithm based on an improved Faster region-based convolutional neural network (Faster R-CNN) is proposed. The runway ground-truth box of the dataset is statistically analyzed, and the size and number of anchors in line with the runway detection background are redesigned based on the analysis results. Finally, a relative attitude and position estimation method for the UAV with respect to the landing runway is proposed. New coordinate reference systems are established, six landing parameters, such as three attitude and three positions, are further calculated by Orthogonal Iteration (OI). Simulation results reveal that the proposed algorithm can achieve 1.85% improvement of AP on runway detection, and the reprojection error of rotation and translation for pose estimation are <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0</mn><mo>.</mo><msup><mn>675</mn><mo>∘</mo></msup></mrow></semantics></math></inline-formula> and 0.581%, respectively.https://www.mdpi.com/2226-4310/9/10/615fixed-wing unmanned aerial vehiclelow-illumination image enhancementgradient descent schemesfaster R-CNNorthogonal iteration |
| spellingShingle | Zhaoyang Wang Dan Zhao Yunfeng Cao Visual Navigation Algorithm for Night Landing of Fixed-Wing Unmanned Aerial Vehicle Aerospace fixed-wing unmanned aerial vehicle low-illumination image enhancement gradient descent schemes faster R-CNN orthogonal iteration |
| title | Visual Navigation Algorithm for Night Landing of Fixed-Wing Unmanned Aerial Vehicle |
| title_full | Visual Navigation Algorithm for Night Landing of Fixed-Wing Unmanned Aerial Vehicle |
| title_fullStr | Visual Navigation Algorithm for Night Landing of Fixed-Wing Unmanned Aerial Vehicle |
| title_full_unstemmed | Visual Navigation Algorithm for Night Landing of Fixed-Wing Unmanned Aerial Vehicle |
| title_short | Visual Navigation Algorithm for Night Landing of Fixed-Wing Unmanned Aerial Vehicle |
| title_sort | visual navigation algorithm for night landing of fixed wing unmanned aerial vehicle |
| topic | fixed-wing unmanned aerial vehicle low-illumination image enhancement gradient descent schemes faster R-CNN orthogonal iteration |
| url | https://www.mdpi.com/2226-4310/9/10/615 |
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