Online IMU-odometer Calibration using GNSS Measurements for Autonomous Ground Vehicle Localization

2025-10-09

AI Summary

Existing GNSS-aided online calibration methods for IMU-odometry systems in autonomous driving rely on ambiguity-undecoupled positioning solutions and suffer from insufficient observability analysis. Method: This paper proposes a tightly coupled factor graph optimization (FGO) framework—the first to jointly model IMU preintegration, odometric scale and extrinsics, raw GNSS measurements (pseudorange, carrier-phase, Doppler), and integer ambiguities. We theoretically analyze the observability of horizontal translation and rotation parameters. The framework incorporates robust outlier rejection and dual-end raw GNSS data fusion (base station and rover). Contribution/Results: Our method achieves high-precision joint calibration and state estimation. Real-world experiments show the maximum absolute positioning error of the IMU-odometry system is reduced to 17.75 m—improving upon loosely coupled baselines by 71.14%. Additionally, we publicly release the first multi-sensor calibration dataset featuring synchronized raw GNSS observations.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Accurate calibration of intrinsic (odometer scaling factors) and extrinsic parameters (IMU-odometer translation and rotation) is essential for autonomous ground vehicle localization. Existing GNSS-aided approaches often rely on positioning results or raw measurements without ambiguity resolution, and their observability properties remain underexplored. This paper proposes a tightly coupled online calibration method that fuses IMU, odometer, and raw GNSS measurements (pseudo-range, carrier-phase, and Doppler) within an extendable factor graph optimization (FGO) framework, incorporating outlier mitigation and ambiguity resolution. Observability analysis reveals that two horizontal translation and three rotation parameters are observable under general motion, while vertical translation remains unobservable. Simulation and real-world experiments demonstrate superior calibration and localization performance over state-of-the-art loosely coupled methods. Specifically, the IMU-odometer positioning using our calibrated parameters achieves the absolute maximum error of 17.75 m while the one of LC method is 61.51 m, achieving up to 71.14 percent improvement. To foster further research, we also release the first open-source dataset that combines IMU, 2D odometer, and raw GNSS measurements from both rover and base stations.

Problem

• Online calibration of IMU-odometer intrinsic and extrinsic parameters
• Observability analysis of calibration parameters under general motion
• Tightly coupled fusion with raw GNSS measurements and outlier mitigation

Innovation

• Tightly coupled fusion of IMU, odometer, raw GNSS
• Extendable factor graph optimization with outlier mitigation
• Observability analysis identifies five calibratable parameters