Advances and Emerging Trends in UAV Sensor Data Fusion Technology
With the acceleration of large-scale application of UAV in agricultural monitoring, disaster rescue, logistics and distribution and other fields, its complex environmental perception bottleneck is becoming increasingly prominent. The traditional single sensor system is limited in GNSS signal shading...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | MATEC Web of Conferences |
| Online Access: | https://www.matec-conferences.org/articles/matecconf/pdf/2025/04/matecconf_menec2025_04023.pdf |
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| _version_ | 1849706679919181824 |
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| author | Xiong Liukun Zheng Ningning |
| author_facet | Xiong Liukun Zheng Ningning |
| author_sort | Xiong Liukun |
| collection | DOAJ |
| description | With the acceleration of large-scale application of UAV in agricultural monitoring, disaster rescue, logistics and distribution and other fields, its complex environmental perception bottleneck is becoming increasingly prominent. The traditional single sensor system is limited in GNSS signal shading, IMU cumulative drift and visual photosensitivity, which seriously restricts the system reliability and adaptability of the system. This paper systematically reviews the breakthrough progress of multi-source data fusion technology: the space-time fusion architecture based on the Kalman filter (UKF) reduces the dynamic attitude estimation error by 18%, and the GPU accelerated particle filtering scheme realizes 20 Hz real-time processing in real-time. The proposed precision-cost-power consumption triangle constraint criterion guides the combination of multispectrum and RTK-GNSS to balance 95% classification accuracy with 10 cm localization error in agricultural monitoring. Typical application verification shows that the bionic radar-vision system achieves 0.32 m positioning accuracy in the occlusion environment (41% improvement compared with the traditional method), and the LiDAR-photogrammetry combined leveling technology improves the absolute accuracy of terrain modeling by 23%. The multi- dimensional evaluation system constructed in this study (localization error of 5 cm, response delay of <100 ms, work15 W) provides theoretical support and decision basis for engineering deployment under complex working conditions. |
| format | Article |
| id | doaj-art-99f71bd6c7ea4368993d93040860053b |
| institution | DOAJ |
| issn | 2261-236X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | MATEC Web of Conferences |
| spelling | doaj-art-99f71bd6c7ea4368993d93040860053b2025-08-20T03:16:07ZengEDP SciencesMATEC Web of Conferences2261-236X2025-01-014100402310.1051/matecconf/202541004023matecconf_menec2025_04023Advances and Emerging Trends in UAV Sensor Data Fusion TechnologyXiong Liukun0Zheng Ningning1Maynooth International Engineering College, Fuzhou UniversityMaynooth International Engineering College, Fuzhou UniversityWith the acceleration of large-scale application of UAV in agricultural monitoring, disaster rescue, logistics and distribution and other fields, its complex environmental perception bottleneck is becoming increasingly prominent. The traditional single sensor system is limited in GNSS signal shading, IMU cumulative drift and visual photosensitivity, which seriously restricts the system reliability and adaptability of the system. This paper systematically reviews the breakthrough progress of multi-source data fusion technology: the space-time fusion architecture based on the Kalman filter (UKF) reduces the dynamic attitude estimation error by 18%, and the GPU accelerated particle filtering scheme realizes 20 Hz real-time processing in real-time. The proposed precision-cost-power consumption triangle constraint criterion guides the combination of multispectrum and RTK-GNSS to balance 95% classification accuracy with 10 cm localization error in agricultural monitoring. Typical application verification shows that the bionic radar-vision system achieves 0.32 m positioning accuracy in the occlusion environment (41% improvement compared with the traditional method), and the LiDAR-photogrammetry combined leveling technology improves the absolute accuracy of terrain modeling by 23%. The multi- dimensional evaluation system constructed in this study (localization error of 5 cm, response delay of <100 ms, work15 W) provides theoretical support and decision basis for engineering deployment under complex working conditions.https://www.matec-conferences.org/articles/matecconf/pdf/2025/04/matecconf_menec2025_04023.pdf |
| spellingShingle | Xiong Liukun Zheng Ningning Advances and Emerging Trends in UAV Sensor Data Fusion Technology MATEC Web of Conferences |
| title | Advances and Emerging Trends in UAV Sensor Data Fusion Technology |
| title_full | Advances and Emerging Trends in UAV Sensor Data Fusion Technology |
| title_fullStr | Advances and Emerging Trends in UAV Sensor Data Fusion Technology |
| title_full_unstemmed | Advances and Emerging Trends in UAV Sensor Data Fusion Technology |
| title_short | Advances and Emerging Trends in UAV Sensor Data Fusion Technology |
| title_sort | advances and emerging trends in uav sensor data fusion technology |
| url | https://www.matec-conferences.org/articles/matecconf/pdf/2025/04/matecconf_menec2025_04023.pdf |
| work_keys_str_mv | AT xiongliukun advancesandemergingtrendsinuavsensordatafusiontechnology AT zhengningning advancesandemergingtrendsinuavsensordatafusiontechnology |