一种基于改进ORB和PROSAC特征点匹配的V-SLAM算法

TP391; 随着机器视觉的高速发展,视觉同步定位与地图构建(visual simultaneous localization and mapping,V-SLAM)成为室内定位、导航应用的研究热点.针对传统ORB算法提取特征点分布不均匀的问题,在前端采用四叉树算法管理特征点,实现特征点均匀化分布,并结合渐进抽样一致性(progressive sample consensus,PROSAC)算法剔除误匹配特征点;在后端,采用构建词袋(bag of words,BoW)法对关键帧进行回环检测,判断帧与帧之间是否存在回环,并采用光束平差(bundle adjustment,BA)法进行相机位姿优化...

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Published in河南理工大学学报(自然科学版) Vol. 42; no. 1; pp. 152 - 159
Main Authors 王红星, 徐婉琳, 张勃阳
Format Journal Article
LanguageChinese
Published 河南理工大学 物理与电子信息学院,河南 焦作 454000%河南理工大学 土木工程学院,河南 焦作 454000 2023
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ISSN1673-9787
DOI10.16186/j.cnki.1673-9787.2021040137

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Abstract TP391; 随着机器视觉的高速发展,视觉同步定位与地图构建(visual simultaneous localization and mapping,V-SLAM)成为室内定位、导航应用的研究热点.针对传统ORB算法提取特征点分布不均匀的问题,在前端采用四叉树算法管理特征点,实现特征点均匀化分布,并结合渐进抽样一致性(progressive sample consensus,PROSAC)算法剔除误匹配特征点;在后端,采用构建词袋(bag of words,BoW)法对关键帧进行回环检测,判断帧与帧之间是否存在回环,并采用光束平差(bundle adjustment,BA)法进行相机位姿优化,修正相机位姿.在图像特征点提取和匹配实验中,通过与传统ORB算法及其他方法对比,证明本文算法具有较好的运算效率.与ORB_SLAM-modified算法进行轨迹对比实验,分析生成的点云图,结果表明,本文算法具有较高的可靠性和精确度.
AbstractList TP391; 随着机器视觉的高速发展,视觉同步定位与地图构建(visual simultaneous localization and mapping,V-SLAM)成为室内定位、导航应用的研究热点.针对传统ORB算法提取特征点分布不均匀的问题,在前端采用四叉树算法管理特征点,实现特征点均匀化分布,并结合渐进抽样一致性(progressive sample consensus,PROSAC)算法剔除误匹配特征点;在后端,采用构建词袋(bag of words,BoW)法对关键帧进行回环检测,判断帧与帧之间是否存在回环,并采用光束平差(bundle adjustment,BA)法进行相机位姿优化,修正相机位姿.在图像特征点提取和匹配实验中,通过与传统ORB算法及其他方法对比,证明本文算法具有较好的运算效率.与ORB_SLAM-modified算法进行轨迹对比实验,分析生成的点云图,结果表明,本文算法具有较高的可靠性和精确度.
Author 徐婉琳
王红星
张勃阳
AuthorAffiliation 河南理工大学 物理与电子信息学院,河南 焦作 454000%河南理工大学 土木工程学院,河南 焦作 454000
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Author_FL XU Wanlin
WANG Hongxing
ZHANG Boyang
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Keywords 特征点匹配
改进ORB特征
渐进抽样一致性
3D点云构图
V-SLAM
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Title 一种基于改进ORB和PROSAC特征点匹配的V-SLAM算法
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