多传感器融合提升攀爬机器人高精度定位能力
High-Precision Climbing Robot Localization Using Planar Array UWB/GPS/IMU/Barometer Integration
- 采用注意力机制融合UWB/GPS/IMU/气压计数据
- 实测定位误差仅0.48米，最大误差1.50米
- 适合复杂高空环境下的机器人导航应用
为解决复杂高空环境中攀爬机器人高精度定位需求，本文提出一种多传感器融合系统，克服单传感器方法的局限性。首先分析定位场景与问题模型，设计基于注意力机制的融合算法（AMFA）架构，集成平面阵列超宽带（UWB）、GPS、惯性测量单元（IMU）和气压计，应对GPS遮挡与UWB非视距（NLOS）问题。开发了端到端神经网络推理模型用于UWB与气压计数据处理，并引入多模态注意力机制实现自适应数据融合。采用无迹卡尔曼滤波（UKF）对轨迹进行优化，提升定位精度与鲁棒性。真实环境实验表明，该方法定位精度达0.48米，最大误差低于1.50米，优于GPS/INS-EKF等基线算法，展现出更强的鲁棒性。
原文摘要 · Abstract (English)
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.
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