Pure Inertial Navigation in Challenging Environments with Wheeled and Chassis Mounted Inertial Sensors

Autonomous vehicles and wheeled robots are widely used in many applications in both indoor and outdoor settings. In practical situations with limited GNSS signals or degraded lighting conditions, the navigation solution may rely only on inertial sensors and as result drift in time due to errors in the inertial measurement. In this work, we propose WiCHINS, a wheeled and chassis inertial navigation system by combining wheel-mounted-inertial sensors with a chassis-mounted inertial sensor for accurate pure inertial navigation. To that end, we derive a three-stage framework, each with a dedicated extended Kalman filter. This framework utilizes the benefits of each location (wheel/body) during the estimation process. To evaluate our proposed approach, we employed a dataset with five inertial measurement units with a total recording time of 228.6 minutes. We compare our approach with four other inertial baselines and demonstrate an average position error of 11.4m, which is $2.4\%$ of the average traveled distance, using two wheels and one body inertial measurement units. As a consequence, our proposed method enables robust navigation in challenging environments and helps bridge the pure-inertial performance gap.

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. 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.