从像素到精度:在数字双胞胎应用中对单眼视觉口径的调查
Arman Neyestani1, Francesco Picariello1, Imran Ahmed1
1Department of Engineering, University of Sannio, 82100 Benevento, Italy.
Sensors (Basel, Switzerland)
|February 24, 2024
概括
本次调查回顾了单眼视觉测距 (VO) 的传统和深度学习方法,这是测量位移的关键技术. 它强调了规模估计等挑战,并讨论了VO的未来研究方向.
科学领域:
- 机器人和计算机视觉 机器人和计算机视觉
- 自主导航系统 自主导航系统
背景情况:
- 单眼视觉测距 (VO) 对于使用单个摄像头估计摄像头运动和轨迹至关重要.
- 传统的VO方法在规模估计和地面平面解释方面面临挑战.
- 深度学习为提高VO性能提供了新的途径.
研究的目的:
- 为传统和基于深度学习的单眼VO技术提供全面的调查.
- 确定和讨论VO的关键研究挑战,特别是规模估计.
- 探索单眼视觉测距术的未来研究机会.
主要方法:
- 审查基本的VO概念:特征检测,跟踪,运动估计,三角化和轨迹估计.
- 分析传统的VO方法,使用地面平面信息进行规模估计.
- 对单眼VO.应用的深度学习方法的探索.
主要成果:
- 确定尺度估计是单眼VO的首要挑战.
- 强调了传统方法中使用摄像头高度和地面平面特征.
- 他注意到了辅助传感器的潜力,如LiDAR和深度摄像头.
- 总结了VO的各种深度学习策略.
结论:
- 单眼VO是对移位测量的重要技术,面临着持续的挑战.
- 深度学习为提高VO准确性和稳定性提供了有希望的解决方案.
- 未来的研究应该专注于先进的规模估计和深度学习的整合.
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