Wheel-GINS: A GNSS/INS Integrated Navigation System with a Wheel-mounted IMU

TL;DR

Wheel-GINS addresses the challenge of long-term localization drift in GNSS/INS systems by fusing GNSS with a wheel-mounted IMU using a loosely coupled EKF. It extends Wheel-INS with a 26-dimensional error-state that includes online estimation of Wheel-IMU leverarm, mounting angle, and wheel radius scale, and introduces a wheel angular velocity constraint to accelerate mounting-angle convergence. The approach achieves centimeter-level positioning comparable to traditional ODO-GINS when GNSS is available, and significantly reduces drift during GNSS outages, enhancing 3D localization robustness outdoors. Importantly, Wheel-GINS eliminates offline calibration by estimating installation parameters online and provides publicly available code for practical deployment on diverse wheeled platforms.

Abstract

A long-term accurate and robust localization system is essential for mobile robots to operate efficiently outdoors. Recent studies have shown the significant advantages of the wheel-mounted inertial measurement unit (Wheel-IMU)-based dead reckoning system. However, it still drifts over extended periods because of the absence of external correction signals. To achieve the goal of long-term accurate localization, we propose Wheel-GINS, a Global Navigation Satellite System (GNSS)/inertial navigation system (INS) integrated navigation system using a Wheel-IMU. Wheel-GINS fuses the GNSS position measurement with the Wheel-IMU via an extended Kalman filter to limit the long-term error drift and provide continuous state estimation when the GNSS signal is blocked. Considering the specificities of the GNSS/Wheel-IMU integration, we conduct detailed modeling and online estimation of the Wheel-IMU installation parameters, including the Wheel-IMU leverarm and mounting angle and the wheel radius error. Experimental results have shown that Wheel-GINS outperforms the traditional GNSS/Odometer/INS integrated navigation system during GNSS outages. At the same time, Wheel-GINS can effectively estimate the Wheel-IMU installation parameters online and, consequently, improve the localization accuracy and practicality of the system. The source code of our implementation is publicly available (https://github.com/i2Nav-WHU/Wheel-GINS).

Figures (11)

• Figure 1: The vehicle trajectories estimated by the proposed Wheel-GINS and the conventional GNSS/Odometer/INS integrated navigation system (ODO-GINS) against the ground truth in our car experiment. The car traversed the same road back and forth twice. The enlarged view shows the position estimates during 120s GNSS outage. We can see that Wheel-GINS has significantly improved the positioning accuracy compared to ODO-GINS during GNSS outages.
• Figure 2: System overview of Wheel-GINS. $\bm\omega$ and $\bm{f}$ are the angular velocity and specific force measured by the Wheel-IMU, respectively.
• Figure 3: Illustration of the vehicle-frame, wheel-frame, and IMU body-frame niu2021. More details can be found in Table \ref{['tab:coordinates']}. $\boldsymbol{l}_w$ indicates the leverarm between the Wheel-IMU and the wheel center; the non-parallelism of the wheel-frame and body-frame indicates the mounting angle between the Wheel-IMU and the wheel. Note that the origin of the vehicle-frame is defined at the wheel center (see Table \ref{['tab:coordinates']}). Here, we plot it on the vehicle body only for easy visualization.
• Figure 4: The number of GNSS satellites used in the three sequences.
• Figure 5: The experimental platforms and trajectories in the three experimental sequences.