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

TL;DR

This paper introduces a radar-inertial odometry framework that jointly performs online spatial and temporal calibration using continuous-time IMU modeling, improving robustness in challenging environments without relying on scan matching or target tracking.

Contribution

It presents a novel continuous-time IMU modeling approach within a factor-graph framework for joint online calibration of radar-IMU systems, addressing temporal misalignment and extrinsic calibration simultaneously.

Findings

Reliable convergence of calibration parameters without environment-specific assumptions

Accurate modeling of asynchronous radar-IMU measurements

Improved odometry robustness in adverse conditions

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.