navlie: A Python Package for State Estimation on Lie Groups
Charles Champagne Cossette, Mitchell Cohen, Vassili Korotkine, Arturo Del Castillo Bernal, Mohammed Ayman Shalaby, James Richard Forbes
Abstract
The ability to rapidly test a variety of algorithms for an arbitrary state estimation task is valuable in the prototyping phase of navigation systems. Lie group theory is now mainstream in the robotics community, and hence estimation prototyping tools should allow state definitions that belong to manifolds. A new package, called navlie, provides a framework that allows a user to model a large class of problems by implementing a set of classes complying with a generic interface. Once accomplished, navlie provides a variety of on-manifold estimation algorithms that can run directly on these classes. The package also provides a built-in library of common models, as well as many useful utilities. The open-source project can be found at https://github.com/decargroup/navlie
BibTeX
@inproceedings{iros2023_navlieapythonpac,
title = {navlie: A Python Package for State Estimation on Lie Groups},
author = {Charles Champagne Cossette and Mitchell Cohen and Vassili Korotkine and Arturo Del Castillo Bernal and Mohammed Ayman Shalaby and James Richard Forbes},
booktitle = {IROS 2023},
year = {2023}
}