Visual–Inertial Autonomous UAV Navigation in Complex Illumination and Highly Cluttered Under-Canopy Environments
The under-canopy environment, which is inherently inaccessible to humans, necessitates the use of unmanned aerial vehicles (UAVs) for data collection. The implementation of UAV autonomous navigation in such environments faces challenges, including dense obstacles, GNSS signal interference, and varyi...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/9/1/27 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588664946819072 |
|---|---|
| author | Leyang Zhao Weixi Wang Qizhi He Li Yan Xiaoming Li |
| author_facet | Leyang Zhao Weixi Wang Qizhi He Li Yan Xiaoming Li |
| author_sort | Leyang Zhao |
| collection | DOAJ |
| description | The under-canopy environment, which is inherently inaccessible to humans, necessitates the use of unmanned aerial vehicles (UAVs) for data collection. The implementation of UAV autonomous navigation in such environments faces challenges, including dense obstacles, GNSS signal interference, and varying lighting conditions. This paper introduces a UAV autonomous navigation method specifically designed for under-canopy environments. Initially, image enhancement techniques are integrated with neural network-based visual feature extraction. Subsequently, employs a high-dimensional error-state optimizer coupled with a low-dimensional height filter to achieve high-precision localization of the UAV in under-canopy environments. Furthermore, proposes a boundary sampling autonomous exploration algorithm and an advanced Rapidly exploring Random Tree (RRT) path planning algorithm. The objective is to enhance the reliability and safety of UAV operations beneath the forest canopy, thereby establishing a technical foundation for surveying vertically stratified natural resources. |
| format | Article |
| id | doaj-art-f2e7e31597ba4e20bb3b550a7f134061 |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-f2e7e31597ba4e20bb3b550a7f1340612025-01-24T13:29:41ZengMDPI AGDrones2504-446X2025-01-01912710.3390/drones9010027Visual–Inertial Autonomous UAV Navigation in Complex Illumination and Highly Cluttered Under-Canopy EnvironmentsLeyang Zhao0Weixi Wang1Qizhi He2Li Yan3Xiaoming Li4Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities, Shenzhen University, Shenzhen 518060, ChinaGuangdong-Hong Kong-Macau Joint Laboratory for Smart Cities, Shenzhen University, Shenzhen 518060, ChinaYangtze River Delta Research Institute of Beijing, Beijing 100086, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan 430079, ChinaGuangdong-Hong Kong-Macau Joint Laboratory for Smart Cities, Shenzhen University, Shenzhen 518060, ChinaThe under-canopy environment, which is inherently inaccessible to humans, necessitates the use of unmanned aerial vehicles (UAVs) for data collection. The implementation of UAV autonomous navigation in such environments faces challenges, including dense obstacles, GNSS signal interference, and varying lighting conditions. This paper introduces a UAV autonomous navigation method specifically designed for under-canopy environments. Initially, image enhancement techniques are integrated with neural network-based visual feature extraction. Subsequently, employs a high-dimensional error-state optimizer coupled with a low-dimensional height filter to achieve high-precision localization of the UAV in under-canopy environments. Furthermore, proposes a boundary sampling autonomous exploration algorithm and an advanced Rapidly exploring Random Tree (RRT) path planning algorithm. The objective is to enhance the reliability and safety of UAV operations beneath the forest canopy, thereby establishing a technical foundation for surveying vertically stratified natural resources.https://www.mdpi.com/2504-446X/9/1/27aircraft navigationfeature extractionplanningforestry |
| spellingShingle | Leyang Zhao Weixi Wang Qizhi He Li Yan Xiaoming Li Visual–Inertial Autonomous UAV Navigation in Complex Illumination and Highly Cluttered Under-Canopy Environments Drones aircraft navigation feature extraction planning forestry |
| title | Visual–Inertial Autonomous UAV Navigation in Complex Illumination and Highly Cluttered Under-Canopy Environments |
| title_full | Visual–Inertial Autonomous UAV Navigation in Complex Illumination and Highly Cluttered Under-Canopy Environments |
| title_fullStr | Visual–Inertial Autonomous UAV Navigation in Complex Illumination and Highly Cluttered Under-Canopy Environments |
| title_full_unstemmed | Visual–Inertial Autonomous UAV Navigation in Complex Illumination and Highly Cluttered Under-Canopy Environments |
| title_short | Visual–Inertial Autonomous UAV Navigation in Complex Illumination and Highly Cluttered Under-Canopy Environments |
| title_sort | visual inertial autonomous uav navigation in complex illumination and highly cluttered under canopy environments |
| topic | aircraft navigation feature extraction planning forestry |
| url | https://www.mdpi.com/2504-446X/9/1/27 |
| work_keys_str_mv | AT leyangzhao visualinertialautonomousuavnavigationincomplexilluminationandhighlyclutteredundercanopyenvironments AT weixiwang visualinertialautonomousuavnavigationincomplexilluminationandhighlyclutteredundercanopyenvironments AT qizhihe visualinertialautonomousuavnavigationincomplexilluminationandhighlyclutteredundercanopyenvironments AT liyan visualinertialautonomousuavnavigationincomplexilluminationandhighlyclutteredundercanopyenvironments AT xiaomingli visualinertialautonomousuavnavigationincomplexilluminationandhighlyclutteredundercanopyenvironments |