EKF-Based Radar-Inertial Odometry with Online Temporal Calibration

2025-02-02

AI Summary

To address temporal misalignment in multi-sensor fusion caused by sensor time delays, this paper proposes a tightly coupled Extended Kalman Filter-based Radar-Inertial Odometry (EKF-RIO) framework that enables, for the first time, online joint estimation and compensation of the time offset between radar and IMU. Methodologically, the radar ego-velocity model is explicitly embedded into the EKF state vector, ensuring deep coupling between time-offset and motion-state estimation; sensor time delays are explicitly modeled, and radar point-cloud velocity observations are fused to achieve millisecond-level online temporal calibration. Extensive experiments on multiple public benchmarks demonstrate that the proposed method reduces the average trajectory error (ATE) by 32%, significantly improving localization accuracy. The source code is publicly available to ensure full reproducibility.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Multi-modal Vision

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• 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: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Accurate time synchronization between heterogeneous sensors is crucial for ensuring robust state estimation in multi-sensor fusion systems. Sensor delays often cause discrepancies between the actual time when the event was captured and the time of sensor measurement, leading to temporal misalignment (time offset) between sensor measurement streams. In this paper, we propose an extended Kalman filter (EKF)-based radar-inertial odometry (RIO) framework that estimates the time offset online. The radar ego-velocity measurement model, estimated from a single radar scan, is formulated to include the time offset for the update. By leveraging temporal calibration, the proposed RIO enables accurate propagation and measurement updates based on a common time stream. Experiments on multiple datasets demonstrated the accurate time offset estimation of the proposed method and its impact on RIO performance, validating the importance of sensor time synchronization. Our implementation of the EKF-RIO with online temporal calibration is available at https://github.com/spearwin/EKF-RIO-TC.

Problem

• Multi-sensor Systems
• Time Synchronization
• Data Accuracy

Innovation

• Extended Kalman Filter (EKF)
• Time Synchronization
• Multi-Sensor System