摘要
物体位姿信息在航空、航海、室内机器人定位等领域都有着十分重要的地位,越来越多的研究表明偏振是某些生物进行导航以及在低光照下拥有视觉的关键。本文提出了一种基于偏振成像的低光照强背景噪声环境下的物体位姿求解方法。该方法通过在普通光源前设置偏振片,使非偏振光转变为偏振光源。通过偏振相机对偏振光源进行偏振成像,利用计算机视觉技术识别偏振光源并求解光源相对偏振相机的位姿。结果表明,本文提出的偏振方法相比光强具有更高的精度和鲁棒性,在40 m距离上,位姿误差为2.99%。该方法利用偏振成像技术,能够在低光照强背景噪声环境下求解物体相对于光源的位姿,克服了低光照下利用计算机视觉无法计算物体位姿信息的问题。
Pose information plays an important role in aviation,navigation,indoor robot positioning,and other fields.An increasing number of studies have shown that polarization is the key for some of the biometric navigation and vision applications in low light.In this paper,a method based on polarization imaging was proposed to understand the object pose under the environment of low light and strong background noise.Non-polarized light was converted to polarized light by setting the polarization film in front of an ordinary light source.A polarization camera was used to obtain the images of a polarized light source.Further,computer vision technology was used to identify the polarized light source and determine the pose of the light source relative to the camera.The results show that the polarization method proposed in this paper has better performance and robustness than light intensity.At a distance of 40 m,the pose error is 2.99%.This method uses polarization imaging to determine the pose of an object relative to the light source under the environment of low light and strong background noise.Thus,this study overcomes the problem that computer vision cannot estimate pose information of objects under low light environment.
作者
张然
桂心远
成昊远
褚金奎
ZHANG Ran;GUI Xin-yuan;CHENG Hao-yuan;CHU Jin-kui(Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education,The Key Laboratory for Micro/Nano Technology and System of Liaoning Province,Dalian University of Technology,Dalian 116024,China)
出处
《光学精密工程》
EI
CAS
CSCD
北大核心
2021年第4期647-655,共9页
Optics and Precision Engineering
基金
国家自然科学基金资助项目(No.51675076,No.51505062)
国家自然科学基金创新研究群体项目(No.51621064)
预研基金资助项目(No.61405180102)。
关键词
偏振成像
位姿估计
低光照
计算机视觉
polarization imaging
pose estimation
low light
computer vision
