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基于对象识别和使用RGB-D数据的准确姿势估计的巧妙操纵.

Udaka A Manawadu1, Naruse Keitaro1

  • 1Graduate School of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu, Fukushima 965-0006, Japan.

Sensors (Basel, Switzerland)
|November 9, 2024
PubMed
概括

本研究引入了一种用于工业门操纵的自动化系统,增强对象识别和姿势估计准确度. 一种基于区域的新策略可以提高机器人手臂的灵敏度,即使在具有挑战性的方向上也是如此.

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
  • 工业自动化 工业自动化

背景情况:

  • 自动化工业门操纵需要精确的物体识别和姿势估计.
  • 现有的系统面临着不同物体定向和距离的挑战.

研究的目的:

  • 开发一个用于对象识别,六度自由度姿势估计和灵巧操纵的综合系统.
  • 用多视角点云来提高工业门的位置估计的准确性.
  • 为具有挑战性的场景创建一个强大的操纵策略.

主要方法:

  • 他们使用了英特尔RealSense D435摄像头和JACO机器人臂.
  • 对象识别包括场景细分,几何和模型识别以及动态集群合并.
  • 姿势估计使用随机抽样共识算法.
  • 开发了一个基于区域的灵巧操纵策略来调整摄像头的定位.

主要成果:

  • 该系统在可接受的错误值内显示出可靠的性能,可用于在±15°视距范围内的物体.
  • 在极端的方向和距离上观察到更多的错误,特别是在球上.
  • 基于区域的操纵策略有效地减轻了困难场景中的错误,提高了可靠性.
关键词:
3D对象识别 3D对象识别3D姿势估计 3D姿势估计巧妙的操纵技巧 巧妙的操作技巧一个点云,一个点云.

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结论:

  • 集成系统为机器人操纵提供了改进的对象识别和姿势估计.
  • 基于区域的战略提高了工业环境中的灵巧操纵可靠性.
  • 这项研究为工业自动化提供了一个新的机器人运动模型.