基于联合双边过器扩展稀疏雷达深度,用于雷达引导单眼深度估计.
Chen-Chou Lo1, Patrick Vandewalle1
1Processing Speech and Images (PSI), Department of Electrical Engineering (ESAT), KU Leuven, 3001 Leuven, Belgium.
Sensors (Basel, Switzerland)
|March 28, 2024
概括
这项研究引入了一种新的雷达扩展技术,用于单眼深度估计,显著增加雷达点密度并减少错误. 该方法在雷达引导的深度估计中实现了最先进的性能.
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:
- 单眼深度估计从雷达数据中获益,以提高准确性和稳定性.
- 现有的雷达扩展方法与稀疏的数据作斗争,需要激光雷达监督.
研究的目的:
- 开发一种新的雷达扩展技术,用于雷达引导的单眼深度估计.
- 为了提高雷达数据的密度和质量,用于深度估计任务.
主要方法:
- 一种新的雷达扩展技术,灵感来自于联合双边过器.
- 使用空间和范围内核来计算雷达点扩展的信心分数.
- 实施一个距离感知窗口大小用于雷达扩展.
主要成果:
- 成功地将雷达点从平均每的39个增加到10万个.
- 与原始雷达和以前的方法相比,扩展雷达数据中的内在错误减少.
- 在nuScenes数据集上展示了最先进的性能.
结论:
- 拟议的雷达扩展方法显著改善了单眼深度估计的雷达数据实用性.
- 该技术为在各种条件下进行深度估计提供了具有成本效益和强大的解决方案.
- 该方法推进了自主系统的传感器融合领域.
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