Radar-Inertial Odometry with Online Spatio-Temporal Calibration via Continuous-Time IMU Modeling

2026-03-20

AI Summary

This work proposes a radar-inertial odometry (RIO) framework that addresses the common limitations of existing methods, which typically assume known extrinsic parameters or rely on sufficient motion excitation for online calibration while often neglecting temporal offsets between sensors. By modeling IMU measurements in continuous time using uniform cubic B-splines to represent acceleration and angular velocity, the proposed approach jointly estimates both spatial extrinsics and temporal offsets within a factor graph optimization framework. Notably, this method achieves online spatiotemporal calibration without requiring scan matching, object tracking, or prior environmental assumptions. To the best of our knowledge, it is the first RIO system capable of performing such joint calibration without specific motion excitation or environmental constraints, thereby significantly enhancing localization accuracy and robustness under adverse conditions such as low light or fog.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Motion & Tracking

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
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Radar-Inertial Odometry (RIO) has emerged as a robust alternative to vision- and LiDAR-based odometry in challenging conditions such as low light, fog, featureless environments, or in adverse weather. However, many existing RIO approaches assume known radar-IMU extrinsic calibration or rely on sufficient motion excitation for online extrinsic estimation, while temporal misalignment between sensors is often neglected or treated independently. In this work, we present a RIO framework that performs joint online spatial and temporal calibration within a factor-graph optimization formulation, based on continuous-time modeling of inertial measurements using uniform cubic B-splines. The proposed continuous-time representation of acceleration and angular velocity accurately captures the asynchronous nature of radar-IMU measurements, enabling reliable convergence of both the temporal offset and extrinsic calibration parameters, without relying on scan matching, target tracking, or environment-specific assumptions.

Problem

• Radar-Inertial Odometry
• extrinsic calibration
• temporal misalignment
• sensor synchronization
• online calibration

Innovation

• Radar-Inertial Odometry
• Online Calibration
• Continuous-Time IMU Modeling
• Spatio-Temporal Calibration
• B-spline