WMINet: A Wheel-Mounted Inertial Learning Approach For Mobile-Robot Positioning
This work addresses the challenge of inertial drift for mobile robots in GPS-denied or low-visibility conditions, representing an incremental advancement by combining existing methods with a new constraint.
The paper tackles the problem of mobile robot positioning in environments with limited satellite signals or poor lighting by proposing WMINet, a wheel-mounted inertial deep learning approach that uses only inertial sensors, resulting in a 66% improvement over state-of-the-art methods.
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.