用艾伦方差法优化惯导参数，提升融合系统精度
The Setting of IMU Parameters in Kalman Filtering-based Information Fusion
- 基于艾伦方差分析建模惯性传感器噪声特性
- 将功率谱密度与艾伦方差关联，精准设定卡尔曼滤波过程噪声
- 在三种融合系统中验证方法有效性，适合导航算法开发者
惯性测量单元（IMU）参数的设置在传感器融合中极为棘手，根源在于其工作状态远比静态标定复杂。静态条件下校准的噪声与零偏稳定性无法适应动态场景，因此参数调优高度依赖经验或对系统的深刻理解。本文在卡尔曼滤波框架下深入研究基于艾伦方差的IMU参数设置方法，利用功率谱密度与艾伦方差之间的关系，构建连续时间滤波中的过程不确定性模型。通过考察惯性导航/全球导航卫星系统（INS/GNSS）融合、激光雷达-惯性里程计（LiDAR-inertial odometry）和视觉-惯性里程计（visual-inertial odometry）三类典型系统，验证了该参数设置流程的可行性与有效性。
原文摘要 · Abstract (English)
The setting or tuning of specifications for the inertial measurement unit (IMU) is tricky in sensor fusion. The underneath conundrum is caused by the fact that the working condition of IMU is more complex than the stationary calibration scenario. Since the noises and biases instabilities calibrated under static condition cannot accommodate other cases, the effective tuning of IMU parameters largely hinges on the experience or profound understanding of the system. In the current work, the setting method of IMU parameters based on Allan variance calibration is delved into within the Kalman filtering framework. Specifically, the relationship between the power sepctral density and Allan variance is leveraged in formulating the process uncertainty in continuous-time filtering. Three typical IMU-based sensor fusion systems, including INS/GNSS integration, LiDAR-inertial odometry, and visual-inertial odometry are considered to show the feasibility and effectiveness of this parameter setting process.
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