摘要
为解决室内视觉定位中由于图像模糊所带来的挑战,提出基于模糊图像的室内视觉定位系统研究了模糊图像的定位。首先,通过模糊程度模型判断图像的模糊程度,并利用改进的图像恢复模型对图像进行去模糊处理,得到清晰图像。其次,通过基于深度学习的图像检索模型提高图像检索性能,以提高定位系统的精度。进一步,通过可适用于多种特征的基于本质矩阵的视觉定位算法,结合SuperPoint+SuperGlue特征提取算法以提高系统在光照和视点变化下的鲁棒性并提高处理效率;结合LoFTR(local feature transformers)算法以应对某些场景下无纹理或弱纹理的情形。该系统根据原始图像和检索图像对提取特征和匹配信息,并使用5点法和随机采样一致性算法估计本质矩阵,最终得到查询图像的相机绝对位姿。实验结果表明,该系统获得了精确的定位结果,平均中值定位误差为0.067 m,相机角度误差1.826°。可见,通过提出的方法,解决了室内视觉定位中图像模糊带来的挑战,显著提高了定位精度。
To address the challenges posed by image blurring in indoor visual localization,a study on the localization of blurred images was conducted by proposing an indoor visual localization system based on blurry images.Firstly,the blurriness level of images was assessed using a blurriness model,and then the images were deblurred using an improved image restoration model to obtain clear images.Secondly,the performance of image retrieval was enhanced using a deep learning-based image retrieval model to improve the accuracy of the localization system.Furthermore,a visual localization algorithm based on the essential matrix,adaptable to multiple features,was employed in conjunction with the SuperPoint+SuperGlue feature extraction algorithm to enhance the system's robustness to changes in illumination and viewpoint,and to improve processing efficiency.The local feature transformers(LoFTR)algorithm was integrated to address scenarios with no or weak texture.The system extracted features and matching information from pairs of original and retrieval images,and estimated the essential matrix using the 5-point method and the random sample consensus algorithm.Finally,the camera's absolute pose of the query image was obtained.Experimental results demonstrate that the system achieves precise localization results,with an average median localization error of 0.067 m and a camera angle error of 1.826°.It can be seen that the proposed method effectively addresses the challenges of image blurring in indoor visual localization,significantly improving localization accuracy.
作者
和法旭
吕梦妍
张坤鹏
张立晔
HE Fa-xu;L Meng-yan;ZHANG Kun-peng;ZHANG Li-ye(School of Computer Science and Technology,Shandong University of Technology,Zibo 255000,China)
出处
《科学技术与工程》
北大核心
2024年第32期13911-13924,共14页
Science Technology and Engineering
基金
国家自然科学基金(62001272)
山东省自然科学基金(ZR2023MF015)。
关键词
室内视觉定位
图像去模糊
图像检索
本质矩阵
五点法
indoor visual localization
image deblur
image retrieval
essential matrix
5-points method
