我正在寻找一种方法来结合指南针和陀螺仪的数据，以便事后确定态度。我将使用一个完整的数据集，其中定期记录 3D 指南针和陀螺仪读数，但我想在后处理中恢复对姿态的估计。
我考虑过简单地使用卡尔曼滤波器，因为它们有很好的文档记录，但我宁愿使用更适合已知完整数据集的情况。我有一种感觉，解决方案“简单地”是一个最小二乘问题，但我希望这里有人能给我指出一两篇处理这个问题（或类似问题）的论文的方向。
此时，我什至不确定该过滤器的名称，因此我很难找到有用的搜索词。任何帮助将不胜感激。
非常感谢！
I am looking for a way to combine data from a compass and gyro in order to determine attitude after the fact. I will be working with a complete data set in which the 3D compass and gyro readings have been recorded at regular intervals, but I want to recover an estimate of attitude in post-processing.
I've considered simply using a Kalman filter, since they are so well documented, but would rather use something more appropriate to a case where the complete data set is known. I have a feeling the solution is "simply" a least squares problem, but I'm hoping someone here can point me in the direction of a paper or two dealing with this problem (or problems like it).
At this point, I'm not even sure what this filter would be called, so I'm having a hard time finding useful search terms. Any help would be appreciated.
Thanks so much!
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如果您详细了解卡尔曼滤波器，您还可以实现所谓的卡尔曼平滑器，它对完整的数据集进行操作。
不过，让我警告你一件事。没有程序员用的卡尔曼滤波器这样的东西。卡尔曼滤波器很难理解。如果您不理解它，您将无法正确实施和使用它。
我的实现几乎就是您正在寻找的。我使用了加速度计和陀螺仪，但没有使用指南针。它基于此手稿，请先阅读。目前最详细的描述是我关于传感器融合。这是一个开源项目，我计划在未来几周。
If you understand the Kalman filter in details, you can also implement the so-called Kalman smoother which operates on the complete data set.
However, let me warn you about one thing. There is no such thing as Kalman Filter for programmers. Kalman filter is difficult to understand. You won't be able to implement and use it correctly if you do not understand it.
My implementation is almost what you are looking for. I used accelerometer and gyroscopes but no compasses. It is based on this manuscript, read it first. The most detailed description I have at the moment is slides 29-32 in my presentation on sensor fusion. It is an open source project, and I plan to release an updated version of the solver in the upcoming weeks.