引入一个室内物体分类数据集,包括毫米波雷达的稀疏点云
Panagiotis Kasnesis1,2, Christos Chatzigeorgiou3, Vasileios Doulgerakis3
1University of West Attica, Department of Electrical and Electronic Engineering, Egaleo, 12241, Greece. pkasnesis@uniwa.gr.
Scientific data
|August 3, 2024
概括
本研究介绍了RadIOCD数据集,其中包括用于室内物体识别的毫米波 (mmWave) 雷达数据. 这一数据集使机器学习能够用于视力有限的应用,如搜索和救援.
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
- 传感器数据 传感器数据
背景情况:
- 对象识别对于自主系统至关重要.
- 基于视觉的方法在封闭或烟雾的环境中是有限的.
- 毫米波 (mmWave) 雷达提供了另一种传感方式.
研究的目的:
- 介绍基于毫米波雷达的室内物体识别的RadioCD数据集.
- 为开发和测试机器学习模型提供全面的数据集.
- 在视力失败的具有挑战性的环境中启用对象识别.
主要方法:
- 使用商用毫米波雷达收集稀疏的3D点云数据.
- 记录了10名志愿者与3个环境中的5种对象类型进行交互.
- 捕获的数据包括每个点云的多普勒速度和强度.
主要成果:
- RadIOCD数据集包括5776个记录,每个记录大约为8秒长.
- 数据集被细分为适合机器学习的76,821个样本.
- 证明了数据集在训练可概括的机器学习模型方面的潜力.
结论:
- RadIOCD数据集促进了基于毫米波雷达的对象识别.
- 该数据集支持机器学习模型开发,以实现强大的对象检测.
- 在需要在非视觉条件下传感的应用中,RadioCD非常有价值.
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