Enabling High-Frequency Cross-Modality Visual Positioning Service for Accurate Drone Landing
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911187661225984 |
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| author | Wang, Haoyang Luo, Xinyu Ding, Wenhua Xu, Jingao Chen, Xuecheng Duan, Ruiyang Chen, Jialong Zhang, Haitao Liu, Yunhao Chen, Xinlei |
| author_facet | Wang, Haoyang Luo, Xinyu Ding, Wenhua Xu, Jingao Chen, Xuecheng Duan, Ruiyang Chen, Jialong Zhang, Haitao Liu, Yunhao Chen, Xinlei |
| contents | After years of growth, drone-based delivery is transforming logistics. At its core, real-time 6-DoF drone pose tracking enables precise flight control and accurate drone landing. With the widespread availability of urban 3D maps, the Visual Positioning Service (VPS), a mobile pose estimation system, has been adapted to enhance drone pose tracking during the landing phase, as conventional systems like GPS are unreliable in urban environments due to signal attenuation and multi-path propagation. However, deploying the current VPS on drones faces limitations in both estimation accuracy and efficiency. In this work, we redesign drone-oriented VPS with the event camera and introduce EV-Pose to enable accurate, high-frequency 6-DoF pose tracking for accurate drone landing. EV-Pose introduces a spatio-temporal feature-instructed pose estimation module that extracts a temporal distance field to enable 3D point map matching for pose estimation; and a motion-aware hierarchical fusion and optimization scheme to enhance the above estimation in accuracy and efficiency, by utilizing drone motion in the \textit{early stage} of event filtering and the \textit{later stage} of pose optimization. Evaluation shows that EV-Pose achieves a rotation accuracy of 1.34$\degree$ and a translation accuracy of 6.9$mm$ with a tracking latency of 10.08$ms$, outperforming baselines by $>$50\%, \tmcrevise{thus enabling accurate drone landings.} Demo: https://ev-pose.github.io/ |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_00646 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enabling High-Frequency Cross-Modality Visual Positioning Service for Accurate Drone Landing Wang, Haoyang Luo, Xinyu Ding, Wenhua Xu, Jingao Chen, Xuecheng Duan, Ruiyang Chen, Jialong Zhang, Haitao Liu, Yunhao Chen, Xinlei Robotics After years of growth, drone-based delivery is transforming logistics. At its core, real-time 6-DoF drone pose tracking enables precise flight control and accurate drone landing. With the widespread availability of urban 3D maps, the Visual Positioning Service (VPS), a mobile pose estimation system, has been adapted to enhance drone pose tracking during the landing phase, as conventional systems like GPS are unreliable in urban environments due to signal attenuation and multi-path propagation. However, deploying the current VPS on drones faces limitations in both estimation accuracy and efficiency. In this work, we redesign drone-oriented VPS with the event camera and introduce EV-Pose to enable accurate, high-frequency 6-DoF pose tracking for accurate drone landing. EV-Pose introduces a spatio-temporal feature-instructed pose estimation module that extracts a temporal distance field to enable 3D point map matching for pose estimation; and a motion-aware hierarchical fusion and optimization scheme to enhance the above estimation in accuracy and efficiency, by utilizing drone motion in the \textit{early stage} of event filtering and the \textit{later stage} of pose optimization. Evaluation shows that EV-Pose achieves a rotation accuracy of 1.34$\degree$ and a translation accuracy of 6.9$mm$ with a tracking latency of 10.08$ms$, outperforming baselines by $>$50\%, \tmcrevise{thus enabling accurate drone landings.} Demo: https://ev-pose.github.io/ |
| title | Enabling High-Frequency Cross-Modality Visual Positioning Service for Accurate Drone Landing |
| topic | Robotics |
| url | https://arxiv.org/abs/2510.00646 |