Coordinate Systems

Believe it or not, all of the Math we’ve been discussing up until now has been leading us to this point. Now we have enough knowledge to introduce the Coordinate_space. In the Dubious Engine, a Coordinate_space represents an object’s position and rotation. Given what we’ve learned about Vectors, Points, and Quaternions, I hope it’s not surprising to see the data in the class:

class Coordinate_space {
    Point               m_position;
    Unit_quaternion     m_rotation;

That’s all we need to describe where an object is, and which way it’s facing. Before we take a look at how all the pieces come together to move one of these things around, let’s discuss “handedness.”

Right Handed Coordinate Space

I’ve been purposefully glossing over this subject up until now because I thought it was too much detail. But now that we’re finalizing our Math section, it’s time to discuss the ambiguity in our Z axis. From your earliest Math classes dealing with X and Y axis you’ve probably been told that the X axis goes from left to right, by which I mean +100 is further to the right then -100. Likewise you probably see the Y axis as going up (ie +100 is higher up then -100). But what about the Z axis? Does it point out of your computer screen towards you, or from you into the screen? Well it turns out that both systems are equally valid, and different Math packages use one way or the other. It’s not a big deal to convert between them, but it’s definitely a lot simpler to pick one and stick with it. OpenGL and Bullet Physics both use a system where the positive Z axis points from your screen out to you (ie your nose is +100 and the area behind your screen is -100). I decided I wanted my coordinate space to match OpenGL so it would be easy to draw what I was building in my Physics Engine.

What’s this have to do with hands? Well to help us give a name to the two different ways of orienting your Z axis, someone came up with a way to illustrate the X, Y, and Z using  your fingers. If you hold up your right hand and point your thumb along the +X Axis, and your index finger along the +Y Axis, you can then point your middle finger at your nose and call that the +Z Axis. This is a Right Handed Coordinate System. If you try to do the same thing with your left hand you’ll see that your middle finger points away from your nose, a Left Handed Coordinate System.

There’s one more hand trick that might even be more important when representing rotations using Axis and Angle. You may have noticed that up until now I’ve said that the Axis is the Vector you rotate around, and the Angle is the amount you rotate, but I never specified which direction to rotate. By using our hands we can finally answer that question. If you represent your Axis Vector as running along your thumb (the thumb nail is +100, palm is -100) then a positive rotation is the direction in which the rest of your fingers curl when you make a fist.

Let’s put all those bits together with an example. Let’s demonstrate a positive rotation around the +Z Axis using the Right Handed Coordinate System. Start by holding your right hand in front of your face with all of your fingers open and with your thumb pointing at your nose. Your thumb now represents the positive Z Axis. Curl your fingers into a fist, that is positive rotation. You should see your fingers curling counter clockwise. When you see information about a Right Hand Coordinate System online you’ll often see that positive rotation is counter clockwise.

If you’re trying that out and it’s not working as expected, re-read this and try again. It’s worth getting right because bugs in the direction of rotation are really hard to figure out. The more you understand it now, the easier your life will be.

Actions on a Coordinate_space

Turning our attention back to the Coordinate_space, let’s look at the functions it supports. At the core, it has some transform functions that serve to convert global Points and Vectors to local Points and Vectors, and vice versa. The usage of types makes these very transparent:

Vector        transform( const Local_vector& v ) const;
Local_vector  transform( const Vector&       v ) const;
Point         transform( const Local_point&  p ) const;
Local_point   transform( const Point&        p ) const;

The code that drives these is based on Quaternion magic, so I can’t pretend to understand everything about why it works, but I am comfortable that it does.

As you can see, there’s no need to specify in the function name what you’re transforming from and to, we just use the type system to do the right thing. This is another good argument for why a Vector and a Point should be different. Since Vectors have no position, the transform function simply rotates them. And since Points do have a position, the transform function rotates them and moves them. For example, if we have a Coordinate_space that is moved to (1,0,0) and rotated 90 degrees around (0,1,0), and we have a global point at (2,0,0), by now you should be able to reason that if we converted that Point into the Coordinate_space it would be at (0,0,1). Try out a bit of invisible knitting yourself, to see if you get the same result.

Aside from these helper type functions, a Coordinate_space has the usual translate and rotate things  you’d expect. I won’t dwell on those, they do what you’d expect, they move and rotate the Coordinate_space. Their implementation is straight forward with the help of the transform functions.

The last function is one that I find myself using a lot, it’s this:

std::tuple<Unit_vector,Unit_vector,Unit_vector> get_axes() const;

This one returns the global X, Y, and Z axis for the Coordinate_space. How is that useful? Let’s say you’re flying in your space ship and you want to fire a laser. What direction is the laser going to go? Clearly it is going to fly straight out from the front of your ship. And what direction is straight out? Well that would be along the +Z Axis. So with this simple function it’s easy to grab the direction in which the laser should fly. In normal usage you would create a new Laser object, drop it into your game, and set it’s velocity to be a Vector of some magnitude (the longer the faster) directly along the +Z Axis that this function returns.

So there you have it, our long journey through the Math of the Dubious Engine is now complete. We have created simple ways to deal with Points, Vectors, Rotations, and Coordinate_spaces. Now we have enough tools at our disposal to go ahead and start talking about the point of this whole thing: Physics.

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Now that we’re starting to get somewhere with Vectors and moving around in 3D, let’s turn our attention to the next topic, rotations. (Get it? “Turn our attention.”) Rotations in 3D are pretty awesome, it’s surprising to me how many clever ways there are to represent them. Unfortunately they all pretty much suck for writing a Physics Engine, but let’s take a look at them and discuss the pros and cons. If you want to jump to the answer, it’s Quaternions, but let’s see why.

Caveat Emptor: I am not a Math major, I am self taught on this stuff. As I’ve learned new things, I’ve often found that my previous understandings were incorrect. The web is awash in articles about 3D rotation that are simply wrong, this may be just another example.

Euler Angles

Apparently this is pronounced “Oiler,” who knew? These are appealing because they’re the simplest to understand. If you’ve played any flight simulators or spaceship fighting games then you already understand them, they’re pitch, yaw, and roll. If you haven’t played those kinds of games then I feel sorry for you. Here’s a refresher, imagine you’re flying an airplane, these angles are:

  • Pitch – is the nose pointing up or down
  • Yaw – this isn’t used much in airplanes, but it’s turning left and right while keeping the wings level. It’s how a car turns
  • Roll – when planes turn one wing goes up and the other goes down, this is roll. Hopefully it’s never used in a car.

This is probably a good a time as any to try to start thinking in terms of X, Y, and Z axis. For our discussions the X axis moves left to right. The Y axis moves up and down. The Z axis moves forward to back. Pretty simple stuff, right? For Euler Angles to make sense we also have to think about the planes that the axes define. In this case I mean plane like a flat surface like a table top or a wall, not the kind you fly in. Take a moment to convince yourself that any 2 axes define a plane. The X and Z axes together define planes like the floor or table top. The XY plane would be a TV you’re watching. The YZ plane defines the walls to your right or left side.

So anyway, if you say your pitch is 45 degrees up from the XZ plane (a table top), 45 degrees to the right of the YZ plane (a wall to your left or right) and then 180 degrees of roll, that would mean you’re facing up and right, and you’re flipped upside down. That’s Euler Angles. Like I said, it’s not very hard to understand. However it suffers from some pretty serious limitations. The first of which is that the order of application is important. If you do the roll first or last you will end up in a completely different place. In our previous example if you started by rolling 180 degrees then you’d be upside down and your concept of “up” would be different. So from there if you went “up” and “right” you’d end up in a different place then if you did the roll last. So if you’re tempted to write a rotation function like

void rotate( float pitch, float yaw, float roll )

Just stop! Nothing about that interface says in which order the operations will be defined, so you’re almost guaranteed to forget and change your assumption and end up with a horrible bug.

The second major problem with Euler Angles is “Gimbal Lock.” Go to Wikipedia for more information on this one, it’s fascinating but a bit beyond the scope of this. The short version is that while Euler Angles can define every possible rotation, they can’t always be combined. So while you’re perfectly safe representing your current rotation with Euler Angles, if you then want to turn the ship a bit, and maybe spin when a missile hits and you try to combine all of these rotations it might work… or you might end up in a state where the next rotation will be jarring and random.

The third major problem with Euler Angles is Interpolation. This means if you have two different rotations and you want to move some percentage of the way between them, it’s difficult to figure out where that should be. How could this be useful? Well if you know you’re rotating 50 degrees per second and a half a second has passed, how far should you rotate? 25 degrees sounds like an obvious answer. But trying doing this when you’re rotating around all 3 axes at the same time? That’s Interpolation and it’s very hard to do with Euler Angles.

Axis and Angle

This one here is my own personal favorite because it’s just so darned handy. It takes a little bit longer to get used to, but it’s worth the study time. For this system you start with a Vector, and then you rotate around it by the Angle specified. Makes sense, right? Think about a door. They rotate around the axis defined by the hinges, a Vector that points straight up and down. How far they rotate is specified by the Angle. What about if you’re standing straight up and you want to bow? The Axis would be a straight line from your left to right, forming a hinge through your hips. How far you bow around that Axis is the Angle. These start to get a little confusing when you want to rotate around an Axis that isn’t so easy to picture. Like what if the Axis is (1,1,1) that’s sort of a diagonal axis that’s up, right, and back so rotating around that is a little harder to imagine. I often end up holding my hands in front of my face with my fingers pointing in different directions and trying to rotate my arms around them. My wife calls this “Invisible Knitting.”

Another great advantage of Axis and Angle is that it works really well with Cross Products. Imagine you’re pointing directly forward and you want to turn to your right. If you take your starting Vector (forward) and get the Cross Product with your destination Vector (right) you will end up with a new Vector that points straight up. It turns out that this new Vector is exactly the Axis you need to rotate around. And it’s length is proportional to how far you have to rotate, ie the Angle. So yeah, Cross Product and Axis and Angle are made for each other.

So to compare Axis and Angle with Euler Angles I would say that Euler Angles are a little simpler to understand. But that’s about their only win. Axis and Angle may be a bit harder to grasp, but once you become comfortable with them they’re easy. Furthermore they don’t suffer from bugs due to order of execution, and they work really well with Cross Products. Big wins. As far as gimbal lock goes, I think they don’t have this problem. I have to confess I don’t understand all the math on this one, so we’ll chalk that one up as a half point in their favor. Alas Interpolation is still very difficult with Axis and Angle.

Rotation Matrices

I’m afraid if you’ve come here hoping for a deeper understanding of Rotation Matrices then you’ve come to the wrong place. In all my Physics Engines over all my years I have made it a point to never use these. A 3×3 rotation matrix simply has too many numbers in it to make any sense to me. I have no intuitive feel for how Matrices work, so debugging them is basically impossible. The Dubious Engine uses exactly one matrix, and it’s required to pass rotations to OpenGL. I don’t understand it, I just know how to dump my rotation into it.

Here’s the important thing I do know about Rotation Matrices, they also suffer from Gimbal Lock and are hard to Interpolate. So as far as comparisons go, they’re impossible to understand, and suffer from the same exact failures as Euler Angles and Axis and Angles. So why bother learning them?


Enter the Quaternion. For most of my Game Engine life I considered Quaternions to be unknowable. I couldn’t figure out how they worked or how to imagine them, so I just copied some equations I learned online, tested them enough to convince myself they worked, and moved on. However I am pleased to report that after some more study they’ve started to make some sense to me. I’ve recently gone back through my engine and smoothed out some misunderstandings with Quaternions. None of the math changed, so I don’t think I fixed any bugs, but it does make a bit more sense now, which is worthwhile. It’s also a little slower, which is a shame, but worth it for the clarity.

I’m not going to try to write another article explaining Quaternions. I’ve found two that did the trick for me, so I’m just going to send you there and let you learn the same way I did. However I will fill in the starting point that I was missing. I was familiar with “Imaginary Numbers,” which I hope you are too. If not, they’re usually represented as i and are defined as:

i = sqrt(-1)
i * i = -1

What I did not know was that there is another kind of number called “Complex Numbers” that are defined as a real number plus an imaginary:

a + bi

I had no idea these existed, and most Quaternion articles start assuming you know them. Luckily not the ones I recently found.

So, at this point, push the pause button on this article, and spend some time learning from these two sources. I am humbled by how well they explain the subject, they do it better then I ever will. When you’re done, feel free to return and we can look at how we can use Quaternions in a game engine:

  • – as taught by an actual University Professor. Unfortunately while attempting to speak and write on the blackboard he occasionally messes up an equation, so don’t copy the Math verbatim. Still an Amazing lecture that will get you up to speed very quickly.
  • – the big one. Nothing about it is easy, you will have to re-read it a bunch of times, but it is complete and awesome. In fact, his whole site is awesome, I will be aspiring to be half as good.

Okay, you know know what I do about Quaternions. Here’s how you can represent one in C++

struct Quaternion {
    float w;
    Vector v;

From the 3D Game Engine Programming article, we know that in a lot of Quaternion Math, the imaginary component acts as a Vector (with dot products, cross products, scalar multiplication, etc). Luckily we already have a Vector class defined that does these things, so it’s easy to reuse. In fact, all of the math for a standard Quaternion is pretty straight forward, so my Quaternion class is relatively simple, here’s a link, there shouldn’t be any surprises.

How do we use them? Well really for 3D rotations it’s a Unit Quaternion we want to use. These are the kind that represent rotations. They can easily be created from an Axis and Angle like this:

Unit_quaternion::Unit_quaternion( const Unit_vector& axis, float angle )
    w = cosf( angle / 2.0f );
    v = Vector(axis) * sinf( angle / 2.0f );

So now we can convert from something we’re comfortable with, Axis and Angle, into a Unit Quaternion. From there the math for combining multiple Quaternions to produce an output Unit Quaternion is fairly easy to code up. Lastly any Unit Quaternion can be pretty easily converted back to three axis: X, Y, and Z.

I’ll tell you one major drawback of working with these things though. There is just no way to get an intuitive sense of them. Here’s a pop quiz for you to see how your 3D intuition is developing. Point (with your finger) to Vector (1, 1, 0) and rotate 45 degrees forward around it. If you pointed diagonally up and right and then kind of rolled forward and left a bit, congrats! It might not be perfect, but at least you have a sense of it. Okay, now imagine Quaternion (1, (1, 1, 0)) and rotate around that. That real bit has something to do with half the cosine of the angle, and the imaginary bit is related to half the sine… I have absolutely no idea where to go. So imagine trying to debug this code. Like you notice in your simulation that some cubes are rotating “oddly.” So you set your breakpoint, look at your rotation and see it’s (0.43255, (0.97666432, 0.43234, 0.124535)). What do you do with that?

So in summary, let’s compare Quaternions to the other Rotation representations we’ve discussed. On the plus side, they do not suffer from Gimbal Lock, or bugs due to order of execution. We didn’t specifically discuss Interpolation, but they do it very well, it’s called “Spherical Linear Interpolation” or SLERP and it’s easy to code up. On the negative side they’re very difficult to understand and debug. However this is somewhat smoothed over by the simplicity in converting between Unit Quaternions and Axis and Angle. All in all, they’re a big win, which is probably why most Physics Engines use them.

Here’s the links:

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