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

TL;DR

This work presents a tightly coupled, online GNSS-aided IMU-odometer calibration method that ingests raw GNSS measurements (pseudo-range, carrier-phase, Doppler) within a factor-graph optimization to jointly estimate navigation states and IMU-odometer extrinsics and odometer intrinsics. It provides a formal observability analysis showing that two horizontal translations and three rotation axes between IMU and odometer are observable under general motion, while vertical translation is unobservable, and demonstrates strong calibration and localization gains in both simulation and field tests, including open-source data. The approach, which includes outlier mitigation and ambiguity resolution via the LAMBDA method, achieves up to 71.14% improvement over loosely coupled baselines in horizontal localization and delivers centimeter-level lever-arm estimation capability, even under GNSS degradation. The work contributes a practical, real-time capable framework and a public dataset combining IMU, 2D odometer, and raw GNSS measurements for rover and base stations, advancing robust AGV localization in challenging environments.

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.

Figures (11)

• Figure 1: The proposed factor graph with a sliding window overview, where the red box denotes the marginalization factor, the orange, blue, and green boxes denote GNSS, IMU, odometer measuring and relative state constraints, and the orange, blue, and green circles denote GNSS, IMU, and odometer states.
• Figure 2: Simulated trajectory and odometer measurements.
• Figure 3: Calibration errors in the Simulation, where the pink line denotes the TC method with constant GNSS-IMU lever-arm error (TC-LE-C), the blue line denotes the TC method with online estimated GNSS-IMU lever-arm (TC-LE-OL), the green one denotes the TC method without GNSS lever-arm disturbance (TC).
• Figure 4: Statistics for calibration results and uncertainty in 100 Monte-Carlo simulation with random initial value. "Err 0s", "Err 60s" and "Err 120s" mean the calibration error at corresponding epochs. "Std 0s", "Std 60s" and "Std 120s" mean the standard deviation of these calibrated results.
• Figure 5: AGV platform for field data collection. Blue boxes denote the evaluating sensor and the Red box denotes the ground-truth system.