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file = {/home/dimitri/Nextcloud/Zotero/storage/WBUCWRPK/Fong and Spivak - 2018 - Seven Sketches in Compositionality An Invitation .pdf;/home/dimitri/Nextcloud/Zotero/storage/MT7MPULY/1803.html}
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}
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@article{sola2018_micro_lie_theor_state_estim_robot,
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author = {Sol{\`a}, Joan and Deray, Jeremie and Atchuthan,
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Dinesh},
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title = {A Micro Lie Theory for State Estimation in Robotics},
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journal = {CoRR},
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year = {2018},
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url = {http://arxiv.org/abs/1812.01537v7},
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abstract = {A Lie group is an old mathematical abstract object
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dating back to the XIX century, when mathematician
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Sophus Lie laid the foundations of the theory of
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continuous transformation groups. As it often
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happens, its usage has spread over diverse areas of
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science and technology many years later. In
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robotics, we are recently experiencing an important
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trend in its usage, at least in the fields of
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estimation, and particularly in motion estimation
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for navigation. Yet for a vast majority of
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roboticians, Lie groups are highly abstract
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constructions and therefore difficult to understand
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and to use. This may be due to the fact that most of
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the literature on Lie theory is written by and for
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mathematicians and physicists, who might be more
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used than us to the deep abstractions this theory
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deals with. In estimation for robotics it is often
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not necessary to exploit the full capacity of the
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theory, and therefore an effort of selection of
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materials is required. In this paper, we will walk
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through the most basic principles of the Lie theory,
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with the aim of conveying clear and useful ideas,
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and leave a significant corpus of the Lie theory
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behind. Even with this mutilation, the material
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included here has proven to be extremely useful in
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modern estimation algorithms for robotics,
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especially in the fields of SLAM, visual odometry,
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and the like. Alongside this micro Lie theory, we
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provide a chapter with a few application examples,
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and a vast reference of formulas for the major Lie
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groups used in robotics, including most jacobian
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matrices and the way to easily manipulate them. We
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also present a new C++ template-only library
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implementing all the functionality described here.},
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archivePrefix ={arXiv},
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eprint = {1812.01537},
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primaryClass = {cs.RO},
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}
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@book{stillwell2008_naive_lie_theor,
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author = {John Stillwell},
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title = {Naive Lie Theory},
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year = 2008,
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publisher = {Springer New York},
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url = {https://doi.org/10.1007/978-0-387-78214-0},
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DATE_ADDED = {Sat Oct 31 17:40:56 2020},
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doi = {10.1007/978-0-387-78214-0},
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pages = {nil},
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series = {Undergraduate Texts in Mathematics},
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}
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@ -112,12 +112,27 @@ higher dimension) on which we can compute derivatives at every
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point. This means that there is a tangent hyperplane at each point,
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which is a nice vector space where our derivatives will live.
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And /that's all!/ What we have defined so far is a /Lie
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You can think of the manifold as a tablecloth that has a weird shape,
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all kinds of curvatures, but no edges or spikes. The idea here is that
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we can define an /atlas/, i.e. a local approximation of the manifold
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as a plane. The name is telling: they're called atlases because they
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play the exact same role as maps. The Earth is not flat, it is a
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sphere with all kinds of deformations (mountains, canyons, oceans),
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but we can have maps that represent a small area with a very good
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precision. Similarly, atlases are the vector spaces that provide the
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best linear approximation of a small region around a point on the
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manifold.
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So we know what a group and a differential manifold are. As it turns
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out, that's all we need to know! What we have defined so far is a /Lie
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group/[fn:lie], i.e. a group that is also a differentiable
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manifold. To take the example of rotation matrices:
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manifold. The tangent vector space at the identity element is called
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the /Lie algebra/.
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To take the example of rotation matrices:
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- We can combine them (i.e. by matrix multiplication): they form a
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group.
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- if we have a function $R : \mathbb{R} \rightarrow
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- If we have a function $R : \mathbb{R} \rightarrow
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\mathrm{GL}_3(\mathbb{R})$ defining a trajectory (e.g. the
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successive attitudes of a object in space), we can find derivatives
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of this trajectory! They would represent instantaneous orientation
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@ -130,6 +145,41 @@ general theory for differential equations.
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* Interesting properties of Lie groups
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For a complete overview of Lie theory, there are a lot of interesting
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material that you can find online.[fn:princeton_companion] I
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especially recommend the tutorial by
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cite:sola2018_micro_lie_theor_state_estim_robot: just enough maths to
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understand what is going on, but without losing track of the
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applications. There is also a [[https://www.youtube.com/watch?v=QR1p0Rabuww][video tutorial]] for the [[https://www.iros2020.org/][IROS2020]]
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conference[fn::More specifically for the workshop on [[https://sites.google.com/view/iros2020-geometric-methods/][Bringing
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geometric methods to robot learning, optimization and control]].]. For a
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more complete treatment, cite:stillwell2008_naive_lie_theor is
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great.[fn::{-} John Stillwell is one of the best textbook writers. All
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his books are extremely clear and a pleasure to read. You generally
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read a book because you're interested in learning the topic; you begin
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learning a topic just because Stillwell wrote a book on it.]
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[fn:princeton_companion] {-} There is also a chapter on Lie theory in
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the amazing /Princeton Companion to Mathematics/
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[[citep:gowersPrincetonCompanionMathematics2010][::, §II.48]].
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The Lie group is a set of elements. At every element, there is a
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tangent vector space. The tangent vector space at the identity is
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called the Lie algebra, and as we will see, it plays a special role.
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TODO
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* Applications
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Lie theory is useful because it gives strong theoretical guarantees
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whenever we need to linearize something. If you have a system evolving
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on a complex geometric structure (for example, the space of rotations,
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which is definitely not linear), but you need to use a linear
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operation (if you need uncertainties, or you have differential
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equations), you have to approximate somehow.
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Using the Lie structure of the underlying space, you immediately get a
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principled way of defining derivatives, random variables, and so on.
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* References
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