Learning Observation Models with Incremental Non-Differentiable Graph Optimizers in the Loop for Robotics State Estimation
Robotics
2023-09-07 v1
Abstract
We consider the problem of learning observation models for robot state estimation with incremental non-differentiable optimizers in the loop. Convergence to the correct belief over the robot state is heavily dependent on a proper tuning of observation models which serve as input to the optimizer. We propose a gradient-based learning method which converges much quicker to model estimates that lead to solutions of much better quality compared to an existing state-of-the-art method as measured by the tracking accuracy over unseen robot test trajectories.
Cite
@article{arxiv.2309.02525,
title = {Learning Observation Models with Incremental Non-Differentiable Graph Optimizers in the Loop for Robotics State Estimation},
author = {Mohamad Qadri and Michael Kaess},
journal= {arXiv preprint arXiv:2309.02525},
year = {2023}
}
Comments
6 pages, 4 figures. Published at the Differentiable Almost Everything Workshop of the 40th International Conference on Machine Learning