基于双眼视觉的湖泊地区的船舶距离方法.
Tengwen Zhang1,2, Xin Liu1,2,3, Mingzhi Shao1,2
1School of Ship and Port Engineering, Shandong Jiaotong University, Weihai 264209, China.
Sensors (Basel, Switzerland)
|October 29, 2025
概括
这项研究引入了一种新的测距方法,使用改进的定向快速和旋转简报 (ORB) 和立体视觉,用于在湖泊中准确的船舶导航. 该算法提高了速度和精度,弥补了立体相匹配的限制.
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
- 导航系统 导航系统
背景情况:
- 传统的测距算法与复杂的湖泊环境和船船体作斗争.
- 船舶附近不准确的深度信息会损害电动船的导航,并增加计算负载.
研究的目的:
- 开发一种改进的测距方法,用于湖泊环境中船舶的准确深度感知.
- 通过解决现有立体视觉技术的局限性,增强电动旅游船的航行能力.
主要方法:
- 整合了改进的定向快速和旋转简要 (ORB) 功能检测与立体视觉的集成.
- 使用局部特征加权方法和四树结构来精制特征点.
- 提高特征匹配精度,使用近似近邻 (FLANN) 和渐进样本共识 (PROSAC) 算法的快速库.
- 应用三角化原理来计算目标距离和角度.
主要成果:
- 与ORB-PROSAC相比,拟议的算法实现了6.5%的处理速度改进.
- 在理想条件下,距离错误在10米处为2.25%,在20米处为5.56%.
- 该方法在补偿特定湖泊场景中的立体声匹配缺陷方面表现出有效性.
结论:
- 这种新的测距方法为湖泊航行中的深度感知挑战提供了可行的解决方案.
- 改进的ORB和立体视觉集成提高了电动船的导航安全性和效率.
- 这种方法提供了一个有价值的替代方案,用于解决基于立体匹配的范围的"差距期".
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