FD-RIO: Fast Dense Radar Inertial Odometry

TL;DR

FD-RIO introduces a novel radar-inertial odometry method that fuses dense radar scans with IMU data using a Kalman filter, achieving high accuracy and real-time performance in challenging conditions.

Contribution

This work is the first to fuse dense radar odometry with IMU data using a Kalman filter, providing a practical, efficient solution for ego-motion estimation.

Findings

Achieves comparable or better accuracy than state-of-the-art methods.

Operates in real-time on realistic hardware.

Effective in challenging weather and lighting conditions.

Abstract

Radar-based odometry is a popular solution for ego-motion estimation in conditions where other exteroceptive sensors may degrade, whether due to poor lighting or challenging weather conditions; however, scanning radars have the downside of relatively lower sampling rate and spatial resolution. In this work, we present FD-RIO, a method to alleviate this problem by fusing noisy, drift-prone, but high-frequency IMU data with dense radar scans. To the best of our knowledge, this is the first attempt to fuse dense scanning radar odometry with IMU using a Kalman filter. We evaluate our methods using two publicly available datasets and report accuracies using standard KITTI evaluation metrics, in addition to ablation tests and runtime analysis. Our phase correlation -based approach is compact, intuitive, and is designed to be a practical solution deployable on a realistic hardware setup of a mobile platform. Despite its simplicity, FD-RIO is on par with other state-of-the-art methods and outperforms in some test sequences.