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

2025-01-06

AI Summary

To address the challenge of long-term, high-precision localization for outdoor robots in GNSS-denied environments, this paper proposes a tightly coupled navigation framework integrating wheel odometry and IMU with GNSS. Leveraging an extended Kalman filter (EKF), we formulate a unified state vector to jointly estimate robot pose, sensor biases, and critical mounting parameters—including lever arm, misalignment angles, and wheel radius errors—enabling real-time online identification. Unlike conventional GNSS/INS or odometry-only fusion paradigms, our approach significantly mitigates pose drift during prolonged GNSS outages. Experimental results demonstrate superior positioning accuracy during GNSS outages compared to state-of-the-art multi-sensor fusion methods. The proposed modeling framework exhibits both robustness and practicality for real-world deployment, and the implementation code is publicly released.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Computer Vision: Motion & Tracking

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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).

Problem

• Outdoor Robot Localization
• High Precision
• Satellite Signal Degradation

Innovation

• Wheel-GINS
• Extended Kalman Filter
• Wheel-IMU Optimization