Graph-Based Horizon Line Detection for UAV Navigation
Perceiving the horizon line is a critical alternative for unmanned aerial vehicle (UAV) autonomous navigation, especially in the presence of noise-induced drift, unavailability of satellite navigation, and multipath errors. However, it's quite tough to detect the horizon line, due to the...
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IEEE
2021
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oai:doaj.org-article:0a808d609da346bf86da5aa090dfab632021-12-01T00:00:19ZGraph-Based Horizon Line Detection for UAV Navigation2151-153510.1109/JSTARS.2021.3126586https://doaj.org/article/0a808d609da346bf86da5aa090dfab632021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9609583/https://doaj.org/toc/2151-1535Perceiving the horizon line is a critical alternative for unmanned aerial vehicle (UAV) autonomous navigation, especially in the presence of noise-induced drift, unavailability of satellite navigation, and multipath errors. However, it's quite tough to detect the horizon line, due to the remotely sensed big data, the dynamic changes in flight, and the serious consequences of failure. To address these problems, we propose a graph-based horizon line detection technique that is composed of graph-based image segmentation, connected domain cascade filtering, horizon line extraction, and UAV attitude estimation. We improve the graph-based image segmentation algorithm so that the segmentation results are more conducive to horizon line detection. We then determine the sky-component by cascade filtering and extract the horizon line based on the boundaries of the sky-component. Furthermore, we directly compute the roll and pitch according to the extracted horizon line and eliminate the ambiguity of the angles. To validate our approach qualitatively and quantitatively, we designed a fixed-wing UAV system. We then validated our algorithm through extensive flights under various conditions and compared the estimated rolls and pitches to the IMU ones. Statistical results show that the proposed technique provides unbiased attitude angles with error variance of about 2<sup>o</sup>, which verify the validity and robustness of our method. For engineering, our program runs at approximately 60 fps on the fly after optimizing.Yong XuHongtao YanYue MaPengyu GuoIEEEarticleConnected domain cascade filteringgraph-based image segmentationhorizon line detectionunmanned aerial vehicle (UAV) navigationOcean engineeringTC1501-1800Geophysics. Cosmic physicsQC801-809ENIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 11683-11698 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Connected domain cascade filtering graph-based image segmentation horizon line detection unmanned aerial vehicle (UAV) navigation Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 |
| spellingShingle |
Connected domain cascade filtering graph-based image segmentation horizon line detection unmanned aerial vehicle (UAV) navigation Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 Yong Xu Hongtao Yan Yue Ma Pengyu Guo Graph-Based Horizon Line Detection for UAV Navigation |
| description |
Perceiving the horizon line is a critical alternative for unmanned aerial vehicle (UAV) autonomous navigation, especially in the presence of noise-induced drift, unavailability of satellite navigation, and multipath errors. However, it's quite tough to detect the horizon line, due to the remotely sensed big data, the dynamic changes in flight, and the serious consequences of failure. To address these problems, we propose a graph-based horizon line detection technique that is composed of graph-based image segmentation, connected domain cascade filtering, horizon line extraction, and UAV attitude estimation. We improve the graph-based image segmentation algorithm so that the segmentation results are more conducive to horizon line detection. We then determine the sky-component by cascade filtering and extract the horizon line based on the boundaries of the sky-component. Furthermore, we directly compute the roll and pitch according to the extracted horizon line and eliminate the ambiguity of the angles. To validate our approach qualitatively and quantitatively, we designed a fixed-wing UAV system. We then validated our algorithm through extensive flights under various conditions and compared the estimated rolls and pitches to the IMU ones. Statistical results show that the proposed technique provides unbiased attitude angles with error variance of about 2<sup>o</sup>, which verify the validity and robustness of our method. For engineering, our program runs at approximately 60 fps on the fly after optimizing. |
| format |
article |
| author |
Yong Xu Hongtao Yan Yue Ma Pengyu Guo |
| author_facet |
Yong Xu Hongtao Yan Yue Ma Pengyu Guo |
| author_sort |
Yong Xu |
| title |
Graph-Based Horizon Line Detection for UAV Navigation |
| title_short |
Graph-Based Horizon Line Detection for UAV Navigation |
| title_full |
Graph-Based Horizon Line Detection for UAV Navigation |
| title_fullStr |
Graph-Based Horizon Line Detection for UAV Navigation |
| title_full_unstemmed |
Graph-Based Horizon Line Detection for UAV Navigation |
| title_sort |
graph-based horizon line detection for uav navigation |
| publisher |
IEEE |
| publishDate |
2021 |
| url |
https://doaj.org/article/0a808d609da346bf86da5aa090dfab63 |
| work_keys_str_mv |
AT yongxu graphbasedhorizonlinedetectionforuavnavigation AT hongtaoyan graphbasedhorizonlinedetectionforuavnavigation AT yuema graphbasedhorizonlinedetectionforuavnavigation AT pengyuguo graphbasedhorizonlinedetectionforuavnavigation |
| _version_ |
1718406184049836032 |