Pure Inertial Navigation in Challenging Environments with Wheeled and Chassis Mounted Inertial Sensors
🤖 AI Summary
This work addresses the significant positioning drift in pure inertial navigation caused by sensor error accumulation under GNSS-denied or poor-illumination conditions. To mitigate this issue, the authors propose WiCHINS, a novel system that uniquely integrates multi-IMU data from both wheel ends and the vehicle body within a three-stage extended Kalman filter framework, augmented with a wheeled motion constraint model. By leveraging the complementary strengths of multi-source inertial sensing, the approach substantially enhances the accuracy and robustness of pure inertial navigation in complex environments. Experimental validation over 228.6 minutes of real-world driving demonstrates an average position error of only 11.4 meters—equivalent to 2.4% of the total traveled distance—outperforming four state-of-the-art inertial baseline methods.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Inertial Navigation
Autonomous Vehicles
Sensor Drift
Challenging Environments
Wheeled Robots
Innovation
Methods, ideas, or system contributions that make the work stand out.
pure inertial navigation
wheel-mounted IMU
chassis-mounted IMU
extended Kalman filter
sensor fusion
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