UAV Autonomous Precise Localization Method Based on Ultra-wideband Intelligent Decision-making
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摘要: 在卫星信号拒止环境中实现无人机的高精度定位是一项关键且具有挑战性的任务. 针对这一难题, 提出一种基于超宽带信息智能决策的无人机自主精确定位方法, 通过超宽带的全局测距校正视觉惯性里程计的累计误差, 从而显著提升定位结果的精确性与鲁棒性. 具体来说, 采用复合干扰滤波方法对超宽带定位中存在的多源异质干扰进行处理; 同时构建超宽带信息评估模块, 对定位结果的可靠性进行量化评估. 实验结果表明, 所提基于超宽带信息智能决策的无人机自主精确定位方法有效提高无人机定位精度.Abstract: Achieving high-precision unmanned aerial vehicle (UAV) localization in global positioning system-denied environments remains a critical and challenging task. To address this problem, a UAV autonomous precise localization method based on ultra-wideband (UWB) intelligent decision-making is proposed, which can compensate for the accumulated drift of the visual inertial odometry by the global UWB measurements, thereby significantly improving the accuracy and robustness of the localization results. Specifically, a composite disturbance filtering framework is introduced to handle multi-source heterogeneous disturbances, and the UWB information evaluation module is developed to assess the reliability of the localization results. Experimental results verify that the proposed UAV autonomous precise localization method based on UWB intelligent decision-making effectively enhances the localization accuracy of UAV.
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Key words:
- UAV /
- sensor fusion /
- composite disturbance filtering /
- UWB localization
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图 2 UWB偏斜$ t $测量噪声的概率图模型
Fig. 2 Probabilistic graphical model of UWB skew-$ t $ measurement noise
图 6 室内实验场景1的示意图(左: 实验场地图, 中: 实验场地俯视示意图, 右: 最小特征值分布图(z=−0.5m))
Fig. 6 The diagram of the indoor experimental site in scenario 1 (left: images of the experimental site, center: diagram of the experimental site from overhead view, right: distribution diagrams of the minimum eigenvalues(z=−0.5m))
图 12 室内实验场景2的示意图(左: 实验场地图, 中: 实验场地俯视示意图, 右: 最小特征值分布图(z=−0.5m))
Fig. 12 The diagram of the indoor experimental site in scenario 2 (left: images of the experimental site, center: diagram of the experimental site from overhead view, right: distribution diagrams of the minimum eigenvalues(z=−0.5m))
表 1 实验1中误差指标对比(m)
Table 1 Comparison of error indices in experiment 1(m)
Opti-VIO CDF-UWB 所提方法 $ MAE_{\rm{x}} $ 0.3344 0.3697 0.1203 $ MAE_{\rm{y}} $ 0.1214 0.3125 0.1318 $ MAE $ 0.3558 0.4841 0.1784 $ STD_{\rm{x}} $ 0.2350 0.4179 0.0560 $ STD_{\rm{y}} $ 0.1368 0.1908 0.1135 $ STD $ 0.2719 0.4594 0.1266 $ RMSE_{\rm{x}} $ 0.4086 0.5579 0.1327 $ RMSE_{\rm{y}} $ 0.1829 0.3661 0.1739 $ RMSE $ 0.4477 0.6673 0.2187 
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表 2 实验2中误差指标对比(m)
Table 2 Comparison of error indices in experiment 2(m)
Opti-VIO CDF-UWB 所提方法 $ MAE_{\rm{x}} $ 0.3234 0.2267 0.1681 $ MAE_{\rm{y}} $ 0.2283 0.2911 0.2504 $ MAE $ 0.3959 0.3690 0.3016 $ STD_{\rm{x}} $ 0.2653 0.1956 0.1245 $ STD_{\rm{y}} $ 0.1555 0.2938 0.1362 $ STD $ 0.3075 0.3529 0.1845 $ RMSE_{\rm{x}} $ 0.4183 0.2994 0.2092 $ RMSE_{\rm{y}} $ 0.2762 0.4135 0.2851 $ RMSE $ 0.5013 0.5105 0.3536
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