CMHT自主数据集:一个多传感器数据集,包括用于自主驾驶的雷达和内射传感器
Howard Zhang1, Ash Liu1, Saied Habibi2
1Department of Computing and Software, McMaster University 1280 Main St. W, Hamilton, ON, L8S 4L8, Canada.
Data in brief
|June 19, 2025
概括
这项研究介绍了一个全面的自动驾驶数据集,包括来自LiDAR,GPS/IMU,雷达和摄像机的同步数据. 它支持在各种条件下对感知和传感器融合的研究.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 传感器技术 传感器技术
背景情况:
- 标准化数据集对于推进自动驾驶 (AD) 算法至关重要.
- 随着传感器多样性的增加,数据集需要对齐,多模式的传感器数据.
- 现有的数据集可能缺乏全面的传感器套件或不同的环境条件.
研究的目的:
- 为自动驾驶研究提供全新的,全面的驾驶数据集.
- 促进感知和传感器融合算法的开发和评估.
- 提供同步的多传感器数据,涵盖各种驾驶场景.
主要方法:
- 使用完整的传感器套件,以10-20Hz记录了超过9000的数据.
- 集成的Velodyne LiDAR,GPS/IMU,毫米波雷达和彩色/红外摄像头.
- 在各种条件下捕获数据,包括恶劣天气 (雨/夜) 和多样化的交通 (高速公路/城市).
主要成果:
- 数据集包括完全同步的数据和原始的ROS2袋.
- 为捕捉到的场景中的单个对象提供3D tracklet标签.
- 详细说明驾驶平台的技术规格,数据格式和相关公用事业.
结论:
- 这一数据集为自动驾驶社区提供了宝贵的资源.
- 能够在传感器融合,感知和算法开发方面进行强有力的研究.
- 支持创建更可靠和更适应的自动驾驶系统.
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