WMINet: A Wheel-Mounted Inertial Learning Approach for Mobile Robot Positioning

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. In that sense, we demonstrate that the best place to mount a single IMU will be on the wheel, not on the chassis. We further incorporate a wheelbase constraint (WC) as a physically informed loss term that enforces the known physically fixed distance between wheels to enhance prediction accuracy. To evaluate our proposed approach, we recorded using the Rosbot-XL a wheel-mounted initial dataset totaling 190 min, 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.