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相关实验视频

Updated: Jun 4, 2025

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
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使用对象同时定位和映射来识别和重建服装.

Yilin Zhang1, Koichi Hashimoto1

  • 1Department of System Information Sciences, Graduate School of Information Sciences, Tohoku University, Aoba-ku, Sendai 980-8579, Japan.

Sensors (Basel, Switzerland)
|December 17, 2024
PubMed
概括
此摘要是机器生成的。

机器人现在可以使用新的视觉系统更好地处理服装. 该系统使机器人能够识别衣服并绘制周围环境的地图,改善服装行业的自动化.

关键词:
服装制造业 制造业 服装制造业计算机视觉 计算机视觉服装重建 重建 服装重建实例细分 实例细分 实例细分对象 SLAM 的 SLAM 的 SLAM机器人技术 机器人工程 机器人工程

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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
  • 服装加工 服装加工 服装加工

背景情况:

  • 机器人在服装加工中的集成是有限的,因为织物可变形.
  • 自动化服装处理需要先进的识别和空间理解.

研究的目的:

  • 开发一种基于视觉的模型,用于服装识别和环境重建.
  • 为了促进机器人在服装加工任务中的应用.

主要方法:

  • 对象SLAM (同时定位和映射) 用于实时跟踪和映射.
  • 用于服装检测的2D实例细分和用于DeepSDF (签名距离函数) 模型增强的合成3D网状数据集.
  • CAPE (圆柱和平面提取) 模型与SLAM集成,用于环境平面重建.

主要成果:

  • 使用英特尔RealSense摄像头同时检测/重建服装和平面的可行性.
  • 通过2D实例细分模型改进了服装识别.
  • 通过DeepSDF模型增强了对服装形状的理解.
  • 强大的环境重建与CAPE平面检测集成到SLAM.

结论:

  • 开发的系统显示出在服装加工中提高自动化和效率的潜力.
  • 服装识别和环境重建的结合是这个行业中机器人应用的关键.