I have a dataset that contains position information from tracking a robot in the environment. The position data comes both from a very accurate optical tracking system (Vicon or similar) and an IMU. I need to compare both position data (either integrating the IMU or differentiating the optical tracking data).
The main problem is that both systems have different reference frames, so in order to compare I first need to align both reference frames. I have found several solutions; the general problem of aligning two datasets seems to be called "the absolute orientation problem".
My concern is that if I use any of these methods I will get the rotation and translation that aligns both datasets minimizing the error over the whole dataset, which means that it will also compensate up to some extent for the IMU's drift. But I am especially interested in getting a feeling of how much the IMU drifts, so that solution does not seem to be applicable.
Anyone has any pointer on how to solve the absolute orientation problem when you do not want to correct for the drift?