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arXiv · 2309.02525

Learning Observation Models with Incremental Non-Differentiable Graph Optimizers in the Loop for Robotics State Estimation

Mohamad Qadri·Michael Kaess
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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.

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Mohamad Qadri, Michael Kaess. 2023-09-05. Learning Observation Models with Incremental Non-Differentiable Graph Optimizers in the Loop for Robotics State Estimation. https://arxiv.org/abs/2309.02525

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