WMINet: A Wheel-Mounted Inertial Learning Approach For Mobile-Robot Positioning
Gal Versano, Itzik Klein
TL;DR
WMINet addresses the challenge of mobile-robot positioning when traditional navigation cues are unavailable by leveraging wheel-mounted inertial sensors in an end-to-end learning framework. It introduces a dedicated neural network architecture that predicts displacement from wheel-mounted IMU data, and further improves accuracy through a wheelbase constraint that enforces geometric consistency between wheels. The approach is validated on a publicly released wheel-mounted IMU dataset collected on a ROSbot-XL, showing superior performance over both model-based wheel-mounted INS and the MoRPINet learning baseline, with substantial gains in PRMSE and TDE. The method enables robust GNSS-denied navigation for short durations and contributes a valuable dataset to support reproducibility and further research in inertial-based mobile robot localization.
Abstract
Autonomous mobile robots are widely used for navigation, transportation, and inspection tasks indoors and outdoors. In practical situations of limited satellite signals or poor lighting conditions, navigation depends only on inertial sensors. In such cases, the navigation solution rapidly drifts due to inertial measurement errors. In this work, we propose WMINet a wheel-mounted inertial deep learning approach to estimate the mobile robot's position based only on its inertial sensors. To that end, we merge two common practical methods to reduce inertial drift: a wheel-mounted approach and driving the mobile robot in periodic trajectories. Additionally, we enforce a wheelbase constraint to further improve positioning performance. To evaluate our proposed approach we recorded using the Rosbot-XL a wheel-mounted initial dataset totaling 190 minutes, which is made publicly available. Our approach demonstrated a 66\% improvement over state-of-the-art approaches. As a consequence, our approach enables navigation in challenging environments and bridges the pure inertial gap. This enables seamless robot navigation using only inertial sensors for short periods.