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

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% improvement. T o 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. T o support future work, we release the first open-source dataset combining IMU, 2D odometer, and raw GNSS measurements from rover and base stations. Localization for autonomous ground vehicles: Localization is a fundamental requirement for AGV, supporting intelligent transportation applications such as delivery, patrolling, search, and rescue [1]. An IMU and an odometer are two common sensors to provide acceleration, velocity and angular velocity for navigation [2]. Generally, they are less susceptible to environmental changes and can be used as dead-reckoning sensors which can incorporate other external sensors (e.g., camera [3], light detection and ranging (LiDAR) [4] and GNSS [5]) to achieve driftless positioning. The problem is, these external sensors are sensitive to environmental conditions.