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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

概括

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.

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.

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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