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隐藏空间表示用于没有标记器的实时手眼校准
Juan Camilo Martínez-Franco1, Ariel Rojas-Álvarez1, Alejandra Tabares1
1Department of Industrial Engineering, Universidad de los Andes, Bogota 111711, Colombia.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
这项研究介绍了一种使用机器人自编码神经网络的无标记手眼校准方法. 该技术准确地实时校准机器人系统,即使在具有挑战性的环境中.
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:
- 没有标记器的手眼校准对于机器人与非结构化的环境互动至关重要.
- 单眼相机具有成本效益,但在坐标投影的准确性方面存在困难.
- 现有的基于学习的方法经常在空间转换的方向预测中失败.
研究的目的:
- 引入一种新的手眼校准程序,用于使用无标记技术的机器人.
- 在基于学习的空间变换回归中克服定向领域的限制.
- 为了在多样化和具有挑战性的条件下实现精确的机器人传感器转换.
主要方法:
- 使用增强自编码神经网络推断旋转表示.
- 从自动编码过程中分析了潜在空间向量,以提高定向精度.
- 采用色彩深度摄像头进行数据采集和点云注册进行评估.
主要成果:
- 拟议的方法实现了计算成本低廉的实时手眼校准.
- 在各种照明和遮蔽条件下表现出强性.
- 与传统的基于标记的方法相比,获得了可比或更高的准确性.
结论:
- 基于自动编码器的方法有效地解决了无标记手眼校准的定向挑战.
- 这种技术为非结构化的环境中机器人传感器校准提供了可行的,高效的解决方案.
- 该方法对需要精确机器人操纵的实时应用具有前景.
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