FD-RIO: Fast Dense Radar Inertial Odometry
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913832882929664 |
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| author | Abu-Alrub, Nader J. Rawashdeh, Nathir A. |
| author_facet | Abu-Alrub, Nader J. Rawashdeh, Nathir A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_07694 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FD-RIO: Fast Dense Radar Inertial Odometry Abu-Alrub, Nader J. Rawashdeh, Nathir A. Robotics 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. |
| title | FD-RIO: Fast Dense Radar Inertial Odometry |
| topic | Robotics |
| url | https://arxiv.org/abs/2505.07694 |