立体在线自我校准通过混合成本函数与共享特征的组合,考虑到成本不确定性
Wonju Lee1,2
1The School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea.
Sensors (Basel, Switzerland)
|April 26, 2025
概括
这项研究引入了一种新的无标记方法,用于使用非线性优化进行立体相机外部校准. 这种方法通过稳定特征匹配并减少过拟合来提高3D数据的准确性,从而实现强大的立体声数字图像相关性.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 三维重建的3D重建
背景情况:
- 立体数字图像相关性 (DIC) 需要准确的3D数据,严重依赖于精确的立体相机外部校准.
- 现有的校准方法通常依赖于标记器或对环境条件敏感,限制了强度.
研究的目的:
- 开发一种无标记方法,用于稳固的立体相机外部校准.
- 提高立体DIC3D数据采集的准确性和稳定性.
主要方法:
- 一种新的无标记立体外在校准技术,采用在变频器上的非线性优化.
- 利用固有的可观测性质并将成本函数与不确定性相结合,以实现具有自然特征的稳定优化.
- 使用贝叶斯选与基于零射击的语义细分的逻辑对时空扩展的空间时空扩展.
主要成果:
- 拟议的方法通过最小化每空间稀疏自然特征错误来实现稳定的优化和对异常值的稳定性.
- 优化与最小的代差异顺利融合,减少过拟合.
- 对公共数据的实验验证表明稳定性和性能与最先进的方法可比.
结论:
- 无标记的,基于分流器的非线性优化为立体相机外部校准提供了强大而准确的解决方案.
- 这种方法提高了在不同的环境中在立体DIC中获得3D数据的可靠性.
- 与贝叶斯选集成扩展了空间时间分析的能力.
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