三重混合器:在不利的天气条件下,用于点云排斥的三重域混合模型
概括
恶劣的天气会降低LiDAR数据的质量. TripleMixer是一个新的点云消噪网络,有效地消除噪声,同时保持结构,增强自动驾驶感知系统.
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:
- 恶劣的天气 (雪,雾,雨) 显著降低了LiDAR传感器的性能.
- 噪音和损坏的测量阻碍了自动驾驶感知模型的准确性.
研究的目的:
- 为LiDAR数据开发一个强大而高效的点云消毒网络.
- 在恶劣的天气条件下评估无声化对下游感知任务的影响.
主要方法:
- 拟议的TripleMixer是一个集空间,频率和通道处理的网络.
- 创建了天气-KITTI和天气-NuScenes数据集,提供各种天气场景和注释.
- 已建立的Denoising,语义细分 (SS),位置识别 (PR) 和对象检测 (OD) 基准.
主要成果:
- TripleMixer实现了最先进的消噪性能,在保持几何结构的同时抑制噪声.
- 在没有再培训的情况下,在下游任务 (SS,PR,OD) 中取得了显著的改进.
- 展示了拒绝作为一个任务无关的战略,以提高LiDAR的稳定性.
结论:
- TripleMixer提供了一个有效的解决方案,用于在恶劣天气中消除LiDAR点云的阴影.
- 拟议的基准标准有助于系统评估消毒方法.
- 激光雷达检测显示了提高自动驾驶系统可靠性的巨大潜力.
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