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Fix typo #1

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Fix typo #1

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2 changes: 1 addition & 1 deletion _pages/bingham-rotation-learning.md
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Expand Up @@ -13,7 +13,7 @@ youtubeId: 8QMcNmCPYR0
[<i class="fa fa-github" aria-hidden="true"></i> View it on Github](https://github.com/utiasSTARS/bingham-rotation-learning){: .btn .btn-green }


There are many ways to represent rotations: Euler angles, rotation matrices, axis-angle vectors, or unit quaternions, for example. In deep learning, it is common to use unit quaternions for their simple geometric and alebraic structure. However, unit quaternions lack an important <strong>smoothness property</strong> that makes learning 'large' rotations difficult, and other representations are not easily amenable to learning uncertainty. In this work, we address this gap and present a smooth representation that defines a <em>belief</em> (or distribution) over rotations.
There are many ways to represent rotations: Euler angles, rotation matrices, axis-angle vectors, or unit quaternions, for example. In deep learning, it is common to use unit quaternions for their simple geometric and algebraic structure. However, unit quaternions lack an important <strong>smoothness property</strong> that makes learning 'large' rotations difficult, and other representations are not easily amenable to learning uncertainty. In this work, we address this gap and present a smooth representation that defines a <em>belief</em> (or distribution) over rotations.

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