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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.23801 |
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| _version_ | 1866917040350035968 |
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| author | Zhang, Shuning Zhu, Zhanchen Chen, Xiangyu Wang, Yunheng Jiang, Xu Duan, Peibo Xu, Renjing |
| author_facet | Zhang, Shuning Zhu, Zhanchen Chen, Xiangyu Wang, Yunheng Jiang, Xu Duan, Peibo Xu, Renjing |
| contents | To address the need for high-precision localization of climbing robots in complex high-altitude environments, this paper proposes a multi-sensor fusion system that overcomes the limitations of single-sensor approaches. Firstly, the localization scenarios and the problem model are analyzed. An integrated architecture of Attention Mechanism-based Fusion Algorithm (AMFA) incorporating planar array Ultra-Wideband (UWB), GPS, Inertial Measurement Unit (IMU), and barometer is designed to handle challenges such as GPS occlusion and UWB Non-Line-of-Sight (NLOS) problem. Then, End-to-end neural network inference models for UWB and barometer are developed, along with a multimodal attention mechanism for adaptive data fusion. An Unscented Kalman Filter (UKF) is applied to refine the trajectory, improving accuracy and robustness. Finally, real-world experiments show that the method achieves 0.48 m localization accuracy and lower MAX error of 1.50 m, outperforming baseline algorithms such as GPS/INS-EKF and demonstrating stronger robustness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_23801 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | High-Precision Climbing Robot Localization Using Planar Array UWB/GPS/IMU/Barometer Integration Zhang, Shuning Zhu, Zhanchen Chen, Xiangyu Wang, Yunheng Jiang, Xu Duan, Peibo Xu, Renjing Robotics To address the need for high-precision localization of climbing robots in complex high-altitude environments, this paper proposes a multi-sensor fusion system that overcomes the limitations of single-sensor approaches. Firstly, the localization scenarios and the problem model are analyzed. An integrated architecture of Attention Mechanism-based Fusion Algorithm (AMFA) incorporating planar array Ultra-Wideband (UWB), GPS, Inertial Measurement Unit (IMU), and barometer is designed to handle challenges such as GPS occlusion and UWB Non-Line-of-Sight (NLOS) problem. Then, End-to-end neural network inference models for UWB and barometer are developed, along with a multimodal attention mechanism for adaptive data fusion. An Unscented Kalman Filter (UKF) is applied to refine the trajectory, improving accuracy and robustness. Finally, real-world experiments show that the method achieves 0.48 m localization accuracy and lower MAX error of 1.50 m, outperforming baseline algorithms such as GPS/INS-EKF and demonstrating stronger robustness. |
| title | High-Precision Climbing Robot Localization Using Planar Array UWB/GPS/IMU/Barometer Integration |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.23801 |