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jaxlie
is a library containing implementations of Lie groups commonly used for
rigid body transformations, targeted at computer vision & robotics
applications written in JAX. Heavily inspired by the C++ library
Sophus.
We implement Lie groups as high-level (data)classes:
Where each group supports:
exp()
, log()
,adjoint()
, apply()
, multiply()
, inverse()
,identity()
, from_matrix()
, and as_matrix()
operations. (seeWe also implement various common utilities for things like uniform random
sampling (sample_uniform()
) and converting from/to Euler angles (in the
SO3
class).
# Python 3.6 releases also exist, but are no longer being updated.
pip install jaxlie
jaxlie
to nonlinear leastjaxlie
.jaxlie
was originally written for our IROS 2021 paper
(link). If it's useful for you, you're
welcome to cite:
@inproceedings{yi2021iros,
author={Brent Yi and Michelle Lee and Alina Kloss and Roberto Mart\'in-Mart\'in and Jeannette Bohg},
title = {Differentiable Factor Graph Optimization for Learning Smoothers},
year = 2021,
BOOKTITLE = {2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}
}