一个基于感知子的多层球形视觉指南针,使用全球特征
Yao Du1, Carlos Mateo2, Omar Tahri2
1Université Bourgogne, 21000 Dijon, France.
Sensors (Basel, Switzerland)
|April 13, 2024
概括
这项研究引入了使用球形时刻和图像面具的视觉指南针,以克服机器人导航中的挑战. 该方法准确地实时估计旋转,适合嵌入式系统.
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
- 导航系统 导航系统
背景情况:
- 视觉指南针的光度方法面临的挑战是图像区域的变化和对全球特征的翻译运动效应的建模.
- 场景深度的变化,特别是在非平面环境中,使翻译运动对全球特征的影响复杂化.
研究的目的:
- 开发一种强大的视觉指南针方法,使用全球特征来处理图像区域变化和转移运动的变化.
- 为了实现嵌入式机器人应用程序的实时旋转估计.
主要方法:
- 利用球形时刻作为全球特征进行视觉对比.
- 采用图像掩盖来缓解由区域出现/消失引起的全球特征值的突然变化.
- 应用神经网络来建模翻译运动对全球特征的影响.
主要成果:
- 拟议的方法证明了准确和可靠的旋转估计.
- 实现实时推断速度,使其适合嵌入式机器人应用程序.
- 在真实世界和合成数据集上的实验结果验证了该方法的性能与最先进的技术相比.
结论:
- 视觉指南针方法有效地结合了图像掩盖和神经网络,以克服光度导航中的关键挑战.
- 该技术为机器人系统的实时旋转估计提供了快速有效的解决方案.
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