# Online Learning of Wheel Odometry Correction for Mobile Robots with Attention-based Neural Network

arXiv: https://arxiv.org/abs/2303.11725 · HTML: https://mlsanity.com/paper/2303.11725/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 21 Mar 2023.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [IONet: Learning to Cure the Curse of Drift in Inertial Odometry](https://mlsanity.com/paper/1802.02209/) (2018)
- [AI-IMU Dead-Reckoning](https://mlsanity.com/paper/1904.06064/) (2019)
- [Robust Ultra-wideband Range Error Mitigation with Deep Learning at the Edge](https://mlsanity.com/paper/2011.14684/) (2020)
- [Approaches, Challenges, and Applications for Deep Visual Odometry: Toward to Complicated and Emerging Areas](https://mlsanity.com/paper/2009.02672/) (2020)
- [Position-Agnostic Autonomous Navigation in Vineyards with Deep Reinforcement Learning](https://mlsanity.com/paper/2206.14155/) (2022)
- [Inertial Odometry on Handheld Smartphones](https://mlsanity.com/paper/1703.00154/) (2017)
- [Marvin: an Innovative Omni-Directional Robotic Assistant for Domestic Environments](https://mlsanity.com/paper/2112.05597/) (2021)
- [DPC-Net: Deep Pose Correction for Visual Localization](https://mlsanity.com/paper/1709.03128/) (2017)
- [Ultra-low-power Range Error Mitigation for Ultra-wideband Precise Localization](https://mlsanity.com/paper/2209.03021/) (2022)

## Abstract (arXiv)

Modern robotic platforms need a reliable localization system to operate daily beside humans. Simple pose estimation algorithms based on filtered wheel and inertial odometry often fail in the presence of abrupt kinematic changes and wheel slips. Moreover, despite the recent success of visual odometry, service and assistive robotic tasks often present challenging environmental conditions where visual-based solutions fail due to poor lighting or repetitive feature patterns. In this work, we propose an innovative online learning approach for wheel odometry correction, paving the way for a robust multi-source localization system. An efficient attention-based neural network architecture has been studied to combine precise performances with real-time inference. The proposed solution shows remarkable results compared to a standard neural network and filter-based odometry correction algorithms. Nonetheless, the online learning paradigm avoids the time-consuming data collection procedure and can be adopted on a generic robotic platform on-the-fly.