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一个强大的和集成的视觉计量框架,利用光流和特征点方法
Haiyang Qiu1, Xu Zhang2, Hui Wang1
1School of Naval Architecture and Ocean Engineering, Guangzhou Maritime University, Guangzhou 510725, China.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
本研究介绍了一种视觉测距框架,使用光流和特征点来更快,更准确地估计姿势. 它整合了两种方法,可以显著减少计算时间,同时保持稳定性.
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:
- 视觉测距 (VO) 对于机器人导航和增强现实至关重要.
- 现有的VO方法在实时性能和稳定性方面面临挑战,特别是在动态环境中.
研究的目的:
- 开发一个强大的和综合的视觉测距框架.
- 为了加快姿势估计,提高准确性和稳定性.
主要方法:
- 使用光学流量跟踪来加快功能点匹配.
- 将全球和本地特征点方法与过机制集成在一起.
- 双对光流和特征点方法用于姿势估计.
主要成果:
- 将相对姿势估计计算时间缩短到ORB_SLAM3前端测距的40%.
- 实现与ORB_SLAM3相比的准确性和稳定性.
- 在ORB_SLAM3框架内在EUROC数据集上验证.
结论:
- 拟议的视觉测距框架提供了显著的速度改进.
- 综合方法保持了高精度和稳定性.
- 展示了对实时视觉测距应用的有希望的解决方案.
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