OHO:一个多模式,多用途的数据集,用于人机对象的交换
Benedict Stephan1, Mona Köhler1, Steffen Müller1
1Neuroinformatics and Cognitive Robotics Lab, Technische Universität Ilmenau, 98693 Ilmenau, Germany.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
创建对象交换 (OHO) 数据集使机器学习能够实现安全的人机器人协作. 这一数据集有助于区分手和物体,用于关键的机器人抓取任务.
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
- 机器学习 机器学习
背景情况:
- 安全的人机协作需要在物体交付期间将手与物体区分开来.
- 目前的方法缺乏对现实世界交付场景的强有力的解决方案.
研究的目的:
- 介绍对象交换 (OHO) 数据集,用于开发机器学习模型.
- 在安全关键的机器人任务中实现强大的手和物体区分.
主要方法:
- 收集了用颜色,深度和热成像来使用手持物体的数据集.
- 开发了用于点云和图像数据的自动标签生成,例如细分.
- 训练并评估了一个实例分段模型,用于每像素手对象区分.
主要成果:
- 该OHO数据集支持实例细分,3D姿势估计和形状估计.
- 自动化标签被证明适用于训练有效的实例细分模型.
- 基线实验证明了成功的手对象区分.
结论:
- OHO数据集是促进人机交互和安全研究的宝贵资源.
- 开发的实例细分管道显示了对现实世界机器人应用的前景.
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