A GNSS/IMU/vision multi-source fusion localization method based on refined pre-integration
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摘要:
针对传统预积分算法固定地球重力值和忽略地球自转的问题，提出一种考虑地球自转和重力变化的惯性测量单元(inertial measurement unit, IMU)预积分算法。参照高精度捷联惯性导航解算的动力学模型，在IMU预积分动力学模型的姿态更新中引入地球自转角速率，速度和位置更新中引入由地球自转引起的科里奥利加速度，同时将由载体位置引起的地球重力变化及时反馈至预积分算法中，详细推导了引入地球自转和重力变化后预积分算法的具体过程，实现对传统预积分模型的精化。并将精化的预积分算法应用于基于紧耦合全球导航卫星系统(global navigation satellite system, GNSS)/IMU/视觉多源融合系统中，实测实验结果表明：利用精化的预积分模型可使系统预积分的模型误差有效减小，显著提升多源融合系统整体的定位定姿精度，其中系统定位精度提升32.41%，航向角精度提升4.23%。
Abstract:A pre-integration algorithm taking into account the earth's rotation and gravity change is developed in order to address the issue that the conventional pre-integration algorithm fixes the value of the earth's gravity and ignores the Earth's rotation. Referring to the dynamic model of high-precision strapdown inertial navigation, the earth rotation angle rate is introduced in the attitude update of the pre-integral dynamic model, and the Coriolis acceleration caused by the earth rotation is introduced in the velocity and position update. Simultaneously, the traditional pre-integral model is improved, taking into account that the carrier's position can feed back the gravity change to the pre-integral algorithm in time. All the process of the pre-integral algorithm are derived in detail after the Earth's rotation and the gravity change are introduced. The refined pre-integration algorithm is applied to a multi-source fusion system based on tightly coupled GNSS/INS/vision. The experimental results show that the model error of the system pre-integration can be effectively reduced by using the refined pre-integration model, and the positioning and attitude accuracy of the multi-source fusion system is improved by 32.41% and 4.23%, respectively.
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图 4 引入重力变化后系统定位误差对比图
Figure 4. The comparison of system positioning error after the introduction of the gravity variation
图 5 引入地球自转后系统定位与航向角误差对比图
Figure 5. The comparison of positioning and heading angle error after the introduction of the earth rotation
表 1 IMU误差设置
Table 1. IMU error setting
陀螺仪
零偏误差/
((°)·hr−1)加速度计
零偏误差/
$({\mathrm{m}}\cdot({\mathrm{s}}\cdot{\mathrm{ hr}}^{\tfrac{1}{2}})^{-1}) $陀螺仪
随机噪声/
((°)·hr−1)加速度计
随机噪声/
$({\mathrm{m}}\cdot({\mathrm{s}}\cdot{\mathrm{ hr}}^{\tfrac{1}{2}})^{-1}) $3 0.02 0.15 0.02 
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表 2 预积分残差的均方根
Table 2. The root-mean-square of pre-integration residual
不同情况 位置RMSE/m 姿态RMSE/(°) x y z x y z 传统预积分 0.011 5 0.006 8 0.012 0 0.048 5 0.001 8 0.023 7 引入重力变化 0.005 9 0.006 2 0.009 4 0.028 1 0.015 2 0.021 8 引入地球自转 0.006 6 0.007 7 0.009 8 0.028 3 0.016 4 0.022 2 引入地球自转和
重力变化0.004 8 0.004 0 0.007 5 0.025 6 0.015 0 0.020 6
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表 3 系统定位误差和航向角误差的均方根
Table 3. The root-mean-square of positioning and heading angle error
不同情况 位置
RMSE/m位置RMSE
精度提升/%航向角
RMSE/(°)航向角RMSE
精度提升/%传统预积分 0.404 3 3.357 9 引入重力变化 0.299 1 26.00 3.308 0 1.48 引入地球自转 0.322 8 20.15 3.245 9 3.34 引入地球自转和
重力变化0.273 2 32.42 3.216 0 4.23
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