通过加权平均融合多惯性传感器，生成可调位置的虚拟惯性单元。
Tunable Virtual IMU Frame by Weighted Averaging of Multiple Non-Collocated IMUs
- 用加权平均法融合多个分离的惯性传感器数据。
- 实测与仿真均显示噪声更低，定位更准。
- 适合需精准对齐相机或GNSS的设备集成场景。
我们提出一种新方法，通过加权平均将多个刚性连接但物理分离的惯性测量单元（IMU）融合为一个虚拟IMU（VIMU）。该方法具有两个优势：(i) 通过平均降低过程噪声；(ii) 可调节VIMU的位置。例如可将其置于与相机帧或GNSS帧重合的位置，从而免除传播模型中考虑杠杆臂项的复杂性。我们还提出一种二次规划方法来确定权重，在最小化噪声的同时实现参考帧的灵活配置。在仿真和真实数据集上验证了该方法的有效性。结果表明，即使在大间距情况下，该平均技术仍有效，相比单个IMU在仿真与实测中均表现出性能提升。
原文摘要 · Abstract (English)
We present a new method to combine several rigidly connected but physically separated IMUs through a weighted average into a single virtual IMU (VIMU). This has the benefits of (i) reducing process noise through averaging, and (ii) allowing for tuning the location of the VIMU. The VIMU can be placed to be coincident with, for example, a camera frame or GNSS frame, thereby offering a quality-of-life improvement for users. Specifically, our VIMU removes the need to consider any lever-arm terms in the propagation model. We also present a quadratic programming method for selecting the weights to minimize the noise of the VIMU while still selecting the placement of its reference frame. We tested our method in simulation and validated it on a real dataset. The results show that our averaging technique works for IMUs with large separation and performance gain is observed in both the simulation and the real experiment compared to using only a single IMU.
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