DenserRadar: A 4D millimeter-wave radar point cloud detector based on dense LiDAR point clouds
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911870729846784 |
|---|---|
| author | Han, Zeyu Jiang, Junkai Ding, Xiaokang Meng, Qingwen Xu, Shaobing He, Lei Wang, Jianqiang |
| author_facet | Han, Zeyu Jiang, Junkai Ding, Xiaokang Meng, Qingwen Xu, Shaobing He, Lei Wang, Jianqiang |
| contents | The 4D millimeter-wave (mmWave) radar, with its robustness in extreme environments, extensive detection range, and capabilities for measuring velocity and elevation, has demonstrated significant potential for enhancing the perception abilities of autonomous driving systems in corner-case scenarios. Nevertheless, the inherent sparsity and noise of 4D mmWave radar point clouds restrict its further development and practical application. In this paper, we introduce a novel 4D mmWave radar point cloud detector, which leverages high-resolution dense LiDAR point clouds. Our approach constructs dense 3D occupancy ground truth from stitched LiDAR point clouds, and employs a specially designed network named DenserRadar. The proposed method surpasses existing probability-based and learning-based radar point cloud detectors in terms of both point cloud density and accuracy on the K-Radar dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_05131 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DenserRadar: A 4D millimeter-wave radar point cloud detector based on dense LiDAR point clouds Han, Zeyu Jiang, Junkai Ding, Xiaokang Meng, Qingwen Xu, Shaobing He, Lei Wang, Jianqiang Robotics The 4D millimeter-wave (mmWave) radar, with its robustness in extreme environments, extensive detection range, and capabilities for measuring velocity and elevation, has demonstrated significant potential for enhancing the perception abilities of autonomous driving systems in corner-case scenarios. Nevertheless, the inherent sparsity and noise of 4D mmWave radar point clouds restrict its further development and practical application. In this paper, we introduce a novel 4D mmWave radar point cloud detector, which leverages high-resolution dense LiDAR point clouds. Our approach constructs dense 3D occupancy ground truth from stitched LiDAR point clouds, and employs a specially designed network named DenserRadar. The proposed method surpasses existing probability-based and learning-based radar point cloud detectors in terms of both point cloud density and accuracy on the K-Radar dataset. |
| title | DenserRadar: A 4D millimeter-wave radar point cloud detector based on dense LiDAR point clouds |
| topic | Robotics |
| url | https://arxiv.org/abs/2405.05131 |