DeRO: Dead Reckoning Based on Radar Odometry With Accelerometers Aided for Robot Localization

TL;DR

This paper introduces DeRO, a radar odometry method that combines radar velocity measurements with inertial data to improve robot localization accuracy, significantly reducing position and rotation errors.

Contribution

The paper presents a novel radar odometry framework that directly uses radar Doppler velocities with inertial data within a Kalman filter, enhancing localization accuracy and robustness.

Findings

Reduces position error by 62%

Reduces rotation error by 66%

Validated on five real-world datasets

Abstract

In this paper, we propose a radar odometry structure that directly utilizes radar velocity measurements for dead reckoning while maintaining its ability to update estimations within the Kalman filter framework. Specifically, we employ the Doppler velocity obtained by a 4D Frequency Modulated Continuous Wave (FMCW) radar in conjunction with gyroscope data to calculate poses. This approach helps mitigate high drift resulting from accelerometer biases and double integration. Instead, tilt angles measured by gravitational force are utilized alongside relative distance measurements from radar scan matching for the filter's measurement update. Additionally, to further enhance the system's accuracy, we estimate and compensate for the radar velocity scale factor. The performance of the proposed method is verified through five real-world open-source datasets. The results demonstrate that our approach reduces position error by 62% and rotation error by 66% on average compared to the state-of-the-art radar-inertial fusion method in terms of absolute trajectory error.