Pure Inertial Navigation in Challenging Environments with Wheeled and Chassis Mounted Inertial Sensors
For autonomous vehicles and wheeled robots operating in GNSS-denied or degraded environments, this work provides a practical method to reduce inertial drift, though the improvement is incremental over existing multi-IMU approaches.
WiCHINS combines wheel-mounted and chassis-mounted inertial sensors with a three-stage extended Kalman filter to achieve pure inertial navigation with an average position error of 11.4m (2.4% of traveled distance) over 228.6 minutes of data, outperforming four baselines.
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.