发表机构
Tongji University（同济大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对卡尔曼滤波信息融合中IMU参数设置难题，提出基于Allan方差校准的方法，验证了其在两类IMU传感器融合系统中的可行性与有效性。
AI 中文摘要
在传感器融合中，惯性测量单元（IMU）规格的设置或调参十分棘手，这一难题源于IMU的工作条件比静态校准场景更复杂。由于静态条件下校准的噪声和偏置不稳定性无法适配其他场景，IMU参数的有效调参很大程度上依赖经验或对系统的深刻理解。本研究在卡尔曼滤波框架内，深入探讨了基于Allan方差校准的IMU参数设置方法，具体利用功率谱密度与Allan方差的关系来构建连续时间滤波中的过程不确定性；同时考虑了两种典型的基于IMU的传感器融合系统，以验证该参数设置过程的可行性与有效性。
英文摘要
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.
Comments2026 International Conference on Guidance, Navigation and Control
