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相关概念视频

Relative Motion Analysis using Rotating Axes-Problem Solving01:29

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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
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Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
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一种基于转移学习的立体匹配算法的应用,用于多个场景中的机器人.

Yuanwei Bi1, Chuanbiao Li2, Xiangrong Tong1

  • 1School of Computer Control and Engineering, Yantai University, Yantai, 264005, China.

Scientific reports
|August 6, 2023
PubMed
概括

这项研究介绍了Ct-Net,这是一种用于机器人视觉的全新跨域立体匹配算法. Ct-Net提高了差异地图的可靠性,并降低了3D场景重建和自动驾驶应用的成本.

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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
  • 机器学习 机器学习

背景情况:

  • 机器人技术中的双筒视觉方法面临着诸如高成本,复杂的算法和不可靠的差异地图等挑战.
  • 现有的方法在各种机器人场景中难以跨域概括.

研究的目的:

  • 提出Ct-Net,一个跨域立体匹配算法,利用转移学习来增强机器人视力.
  • 在各种机器人应用中提高立体声匹配的可靠性和效率.

主要方法:

  • Ct-Net使用通用特征提取器和特征适配器进行域调整.
  • 一个域自适应性成本优化模块和差异得分预测完善匹配成本和搜索范围.
  • 该框架采用分阶段培训策略和废弃实验进行验证.

主要成果:

  • 在KITTI 2015基准中,Ct-Net显著降低了3PE-fg误差 (总体为19.3%,不包含21.1%).
  • 在Middlebury数据集上的楼梯样本的样本错误率至少提高了28.4%.
  • 在Middlebury,Apollo和现实世界数据集中展示了卓越的跨域性能.

结论:

  • Ct-Net有效地提高了机器人视觉的跨域立体声匹配性能.
  • 该算法解决了当前双眼视觉方法的局限性,提供了更高的可靠性和效率.
  • Ct-Net在各种现实世界的机器人视觉任务中显示出实际的应用性.