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E-POSE:一个大规模的事件摄像头数据集用于对象位置估计.

Oussama Abdul Hay1, Xiaoqian Huang1, Abdulla Ayyad1

  • 1Advanced Research and Innovation Center (ARIC), Khalifa University of Science and Technology, Abu Dhabi, UAE.

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概括
此摘要是机器生成的。

本研究介绍了基于事件的最大数据集,用于6D对象的姿势估计,这对于机器人掌握至关重要. 新的数据集,包括耶鲁-CMU-伯克利对象,将推进机器人操纵研究.

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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
  • 传感器融合式传感器

背景情况:

  • 机器人自动化依赖于精确的6D对象姿势估计来进行操纵.
  • 基于事件的摄像机比传统摄像机具有优势,但缺乏用于姿势估计的专用数据集.

研究的目的:

  • 为基于事件的6D对象姿势估计引入一个全面的数据集.
  • 促进对机器人新型姿势估计算法的研究和开发.

主要方法:

  • 使用耶鲁-CMU-伯克利 (YCB) 对象开发了一个广泛的数据集.
  • 捕获的事件包与相关的地面真相姿势,尖峰图像,面具和3D边界框.
  • 包括各种不同的场景,不同的杂乱,物体类型,速度和照明条件.

主要成果:

  • 该数据集包括306个序列,超过一个小时的数据和15亿次事件.
  • 在18个场景中设有13个YCB对象,涵盖杂乱和无杂乱的场景.
  • 提供细分事件和三通道事件图像,以进行强大的验证.

结论:

  • 这个数据集是基于事件的6D对象姿势估计的最大和最多样化的资源.
  • 它解决了对数据的关键需求,以推进机器人操纵和掌握.
  • 旨在加快先进的对象构成估计算法的开发和测试.