Continuous-time Radar-inertial Odometry for Automotive Radars

TL;DR

This paper introduces a novel continuous-time radar-inertial odometry framework that fuses data from multiple radars and an IMU, outperforming previous discrete-time methods and enhancing ego-motion estimation robustness in adverse weather.

Contribution

It is the first application of a continuous-time framework to radar-inertial odometry, enabling efficient multi-sensor fusion and improved accuracy.

Findings

Continuous-time method outperforms discrete-time approach.

Framework effectively fuses asynchronous radar and IMU data.

Enhanced robustness in adverse weather conditions.

Abstract

We present an approach for radar-inertial odometry which uses a continuous-time framework to fuse measurements from multiple automotive radars and an inertial measurement unit (IMU). Adverse weather conditions do not have a significant impact on the operating performance of radar sensors unlike that of camera and LiDAR sensors. Radar's robustness in such conditions and the increasing prevalence of radars on passenger vehicles motivate us to look at the use of radar for ego-motion estimation. A continuous-time trajectory representation is applied not only as a framework to enable heterogeneous and asynchronous multi-sensor fusion, but also, to facilitate efficient optimization by being able to compute poses and their derivatives in closed-form and at any given time along the trajectory. We compare our continuous-time estimates to those from a discrete-time radar-inertial odometry approach and show that our continuous-time method outperforms the discrete-time method. To the best of our knowledge, this is the first time a continuous-time framework has been applied to radar-inertial odometry.