.png){kind=icon}
返回
A Novel Radar Point Cloud Generation Method for Robot Environment Perception
.png){kind=icon}
PRE
AI
DOI：10.1109/TRO.2022.3185831.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Millimeter-wave (mmWave) radar has been widely used in autonomous driving due to its good performance under harsh weather conditions. In recent years, with the development of mmWave radar hardware performance, radar point clouds, as an important data format of mmWave radar, have been widely used in high-level perception tasks of mobile robots and autonomous driving. However, at present, compared to LiDAR point clouds, in common application scenes of mobile robots, mmWave radar point clouds have shortcomings such as sparsity and containing many ghost targets. Therefore, in this article, we analyze the reasons that cause these problems and propose a new method for point cloud generation as well as a new evaluation metric. After building a new dataset and carrying out experiments in real-world scenes, our method shows better performance on the quality of radar point clouds compared to other methods. In addition, by evaluating the performance of applying the high-quality radar point clouds to object detection tasks as well as localization and mapping tasks, the result shows that radar point clouds generated using our method can significantly improve the environment perception ability of mobile robots.
Keyword:
Radar
Point cloud compression
Radar applications
Radar antennas
Radar detection
Mobile robots
Doppler radar
Millimeter-wave (mmWave) radar
point cloud
radar detector
robot environment perception
期刊
IF:
10.5
论文数:
3.3K
被引数:
2.8W
机构
引用论文
.png){kind=icon}
分享收藏
查看更多内容