基于Voxel特征提取模块的LiDAR到雷达翻译用于雷达数据增强
Jinho Lee1, Geonkyu Bang1, Takaya Shimizu2
1Emerging Design and Informatics Course, Graduate School of Interdisciplinary Information Studies, The University of Tokyo, 4 Chome-6-1 Komaba, Meguro City, Tokyo 153-0041, Japan.
Sensors (Basel, Switzerland)
|January 23, 2024
概括
这项研究引入了一种新的LiDAR-to-Radar转换方法,以提高自动驾驶汽车的雷达传感器数据质量. 该技术通过转换高质量的LiDAR数据来增强雷达数据增强,减少噪音和提高性能.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 传感器融合式传感器
背景情况:
- 自动驾驶汽车依赖LiDAR和雷达传感器来测量物体距离.
- 对于LiDAR的深度学习是先进的,但雷达传感器的深度学习落后.
- 激光雷达提供精确性,但易受天气影响;雷达强大,但噪音大.
研究的目的:
- 解决自动驾驶雷达传感器中的噪音和数据增强挑战.
- 提出一种新的LiDAR-to-Radar数据翻译方法.
主要方法:
- 开发了一个LiDAR-to-Radar翻译技术.
- 包含一个voxel功能提取模块.
- 利用LiDAR和雷达之间的基于点的数据采集相似性.
主要成果:
- 成功将高质量的LiDAR数据转化为雷达数据.
- 使用现实世界,同位置的LiDAR和雷达数据证明了该方法的优越性.
- 展示了增强的雷达数据质量和增强能力.
结论:
- 拟议的LiDAR-to-Radar转换方法有效地减少了雷达数据中的噪声.
- 这种方法显著提高了用于自动驾驶汽车感知的雷达数据增强.
- 该方法为改善不同环境条件下的雷达传感器性能提供了可行的解决方案.
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