Documentation for this package can be found at https://python-graphslam.readthedocs.io/.
This package implements a Graph SLAM solver in Python.
- Optimize \mathbb{R}^2, \mathbb{R}^3, SE(2), and SE(3) datasets
- Analytic Jacobians
- Supports odometry edges
- Import and export .g2o files for SE(2) and SE(3) datasets
pip install graphslam
>>> from graphslam.load import load_g2o_se3
>>> g = load_g2o_se3("data/parking-garage.g2o") # https://lucacarlone.mit.edu/datasets/
>>> g.plot(vertex_markersize=1)
>>> g.calc_chi2()
16720.02100546733
>>> g.optimize()
>>> g.plot(vertex_markersize=1)Output:
Iteration chi^2 rel. change
--------- ----- -----------
0 16720.0210
1 45.6644 -0.997269
2 1.2936 -0.971671
3 1.2387 -0.042457
4 1.2387 -0.000001
| Original | Optimized |
|
|
>>> from graphslam.load import load_g2o_se2
>>> g = load_g2o_se2("data/input_INTEL.g2o") # https://lucacarlone.mit.edu/datasets/
>>> g.plot()
>>> g.calc_chi2()
7191686.382493544
>>> g.optimize()
>>> g.plot()Output:
Iteration chi^2 rel. change
--------- ----- -----------
0 7191686.3825
1 319916668.8138 43.484235
2 124888469.1437 -0.609622
3 338171.6169 -0.997292
4 734.5693 -0.997828
5 215.8405 -0.706167
6 215.8405 -0.000000
| Original | Optimized |
|
|
- Grisetti, G., Kummerle, R., Stachniss, C. and Burgard, W., 2010. A tutorial on graph-based SLAM. IEEE Intelligent Transportation Systems Magazine, 2(4), pp.31-43.
- Blanco, J.L., 2010. A tutorial on SE(3) transformation parameterizations and on-manifold optimization. University of Malaga, Tech. Rep, 3.
- Carlone, L., Tron, R., Daniilidis, K. and Dellaert, F., 2015, May. Initialization techniques for 3D SLAM: a survey on rotation estimation and its use in pose graph optimization. In 2015 IEEE international conference on robotics and automation (ICRA) (pp. 4597-4604). IEEE.
- Carlone, L. and Censi, A., 2014. From angular manifolds to the integer lattice: Guaranteed orientation estimation with application to pose graph optimization. IEEE Transactions on Robotics, 30(2), pp.475-492.
Thanks to Luca Larlone for allowing inclusion of the Intel and parking garage datasets in this repo.
If you're interested, you can watch as I coded this up.