2025/05/12 by Nader J. Abu-Alrub, Abu-Alrub, Nader J., Nathir A. Rawashdeh +1
Engineering · #Advanced SAR Imaging Techniques #FOS: Computer and information sciences #Robotic Locomotion and Control #Robotics (cs.RO) #Robotics and Sensor-Based Localization
paper · pdf · doi:10.48550/arxiv.2505.07694
openalex publication_date 2025/05/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.