jaxlie
[ API reference ] [ PyPI ]
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:
| Group | Description | Parameterization |
|---|---|---|
jaxlie.SO2 |
Rotations in 2D. | (real, imaginary): unit complex (∈ S1) |
jaxlie.SE2 |
Proper rigid transforms in 2D. | (real, imaginary, x, y): unit complex & translation |
jaxlie.SO3 |
Rotations in 3D. | (qw, qx, qy, qz): wxyz quaternion (∈ S3) |
jaxlie.SE3 |
Proper rigid transforms in 3D. | (qw, qx, qy, qz, x, y, z): wxyz quaternion & translation |
Where each group supports:
- Forward- and reverse-mode AD-friendly
exp(),log(),adjoint(),apply(),multiply(),inverse(),identity(),from_matrix(), andas_matrix()operations. (see ./examples/se3_example.py) - Taylor approximations near singularities.
- Helpers for optimization on manifolds (see
./examples/se3_optimization.py,
jaxlie.manifold.*). - Compatibility with standard JAX function transformations. (see ./examples/vmap_example.py)
- Broadcasting for leading axes.
- (Un)flattening as pytree nodes.
- Serialization using flax.
We also implement various common utilities for things like uniform random
sampling (sample_uniform()) and converting from/to Euler angles (in the
SO3 class).
Install (Python >=3.7)
# Python 3.6 releases also exist, but are no longer being updated.
pip install jaxlie
Misc
jaxlie was originally written when I was learning about Lie groups for our IROS 2021 paper
(link):
@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)}
}
1 maintainer
Metadata
1 maintainer
Release files for jaxlie 1.5.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| jaxlie-1.5.0.tar.gz | 26.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| jaxlie-1.5.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 50.3 kB
Release files / jaxlie-1.5.0.tar.gz
| Download URL | jaxlie-1.5.0.tar.gz |
|---|---|
| Size | 26.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
d9981df6e1cc6a63c6c65d647c2f4848685afd10f725f4964b7e68a1b434c3df
|
|
BLAKE2b-256 checksum How to use checksums |
30edc7c701f5635d7e276d0ccba2e28fc0259d23474593b9028f3707f4c4e5f2
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.13.3
|
Release files / jaxlie-1.5.0-py3-none-any.whl
| Download URL | jaxlie-1.5.0-py3-none-any.whl |
|---|---|
| Size | 24.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
44cc55aab07f9ee49e681e7f2a957338337f548fa4d53e81450f563273757a96
|
|
BLAKE2b-256 checksum How to use checksums |
06cf9084fbc451c3773e6623b94b18b40e949479c567c9af8dea4d23484c0336
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.13.3
|
