Should Quaternion based 3D Cameras accumulate Quaternions or Euler angles?

Question

So I have written a Quaternion based 3D Camera oriented toward new programmers so it is ultra easy for them to integrate and begin using.

While I was developing it, at first I would take user input as Euler angles, then generate a Quaternion based off of the input for that frame. I would then take the Camera's Quaternion and multiply it by the one we generated for the input, and in theory that should simply add the input rotation to the current state of the camera's rotation, and things would be all fat and happy. Lets call this: Accumulating Quaternions, because we are storing and adding Quaternions only.

But I noticed that there was a problem with this method. The more I used it, even if I was only rotating on one Euler angle, say Yaw, it would, over some iterations, begin bleeding over into another, say Pitch. It was slight, but fairly unacceptable.

So I did some more research and found an article stating it was better to accumulate Euler angles, so the camera stores it's current rotation as Euler angles, and input is simply added to them each frame. Then I generate a Quaternion from them each frame, which is in turn used to generate my rotation matrix. And this fixed the issue of rotation bleeding into improper axes.

So do any Stackoverflow members have any insight into this problem? Is that a proper way of doing things?

Answer 1

Multiplying quaternions is going to suffer from accumulation of floating-point roundoff issues (even simple angles like 45 degrees won't be exact). It's a great way to composite rotations, but the precision of each of your quaternion components is going to drop-off over time. The bleed-through is one side-effect, a visually worse one though is your quaternion could start incorporating a scale factor - to recover that, you'd have to renormalize back to Euler angles in any case. A fixed-point Euler angle isn't going to accumulate roundoff.

Recalculating the quaternion per-frame is minimal. I wouldn't bother trying to optimize it out. You could probably allow a few quaternions to accumulate before you renormalized to get the accuracy back, but it really isn't worth the effort.

Answer 2

Accumulation is an inexact process. Accumulating lots of incremental rotations will accumulate roundoff error whether you do it with quaternions or matrices.

I imagine something like this: you got your code up and running, but noticed that after a certain amount of navigation your camera was heeling over annoyingly -- violating an invariant you hadn't thought of in advance. Effectively, you've realized you don't want to accumulate rotations; instead you want to do something else.

You can look at this as more of an interface design issue than a numerical accuracy issue. Basically, people expect a camera to navigate according to pitch, yaw, and roll, so choosing to control and represent the angles directly can avoid a lot of problems.

The bummer here is that the quaterions seem to have become redundant (for this particular usage, at least). You still want the quaternions, though -- interpolating with the raw pitch/yaw/roll angles can be ugly. Again, it's an interface design question: you need to figure out where you'll need the quaternions, and how to get them in and out...