Radar4VoxMap: Accurate Odometry from Blurred Radar Observations
Jiwon Seok, Soyeong Kim, Jaeyoung Jo, Jaehwan Lee, Minseo Jung, Kichun Jo
Abstract
Compared to conventional 3D radar, the 4D imaging radar provides additional height data and finer resolution measurements. Moreover, compared to LiDAR sensors, 4D imaging radar is more cost-effective and offers enhanced durability against challenging weather conditions. Despite these advantages, radar-based localization systems face several challenges, including limited resolution, leading to scattered object recognition and less precise localization. Additionally, existing methods that form submaps from filtered results can accumulate errors, leading to blurred submaps and reducing the accuracy of the SLAM and odometry. To address these challenges, this paper introduces Radar4VoxMap, a novel approach designed to enhance radar-only odometry. The method includes an RCS-weighted voxel distribution map that improves registration accuracy. Furthermore, fixed-lag optimization with the graph is used to optimize both the submap and pose, effectively reducing cumulative errors. The proposed method has shown strong performance on open datasets. The code is available at: https://github.com/ailab-hanyang/Radar4VoxMap
BibTeX
@inproceedings{icra2025_radar4voxmapaccu,
title = {Radar4VoxMap: Accurate Odometry from Blurred Radar Observations},
author = {Jiwon Seok and Soyeong Kim and Jaeyoung Jo and Jaehwan Lee and Minseo Jung and Kichun Jo},
booktitle = {ICRA 2025},
year = {2025}
}