一种基于模糊图像评价与特征匹配改进的视觉SLAM方法  

Visual SLAM Method Based on Fuzzy Image Evaluation and Feature Matching Improvement

作　　者：刘毓 焦宇航 任超锋 Liu Yu;Jiao Yuhang;Ren Chaofeng(School of Geology Engineering and Geomatics,Chang’an University,Xi’an 710054,Shaanxi,China)

机构地区：[1]长安大学地质工程与测绘学院,陕西西安710054

出　　处：《激光与光电子学进展》2024年第24期160-170,共11页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(41801383)。

摘　　要：针对运动模糊会降低视觉同步定位与制图(SLAM)运行精度的问题,提出了一种基于模糊图像评价与特征匹配改进的视觉SLAM方法。首先,通过分析图像运动模糊的产生原理,基于再模糊理论设计了一种模糊参数,用于表示图像的模糊程度;然后,使用自适应阈值对模糊图像进行删除;最后,在特征匹配环节改进了基于网格的运动统计算法,代替了SLAM常用的特征匹配方法。通过对不同环境的两种开源数据集进行实验与分析,结果表明:1)所设计的模糊参数能够有效表征图像的模糊程度,该方法在标准图像库中的预测图像质量评价分数,与其他算法相比,均方根误差减小了9.3%~12.3%;在KITTI数据集上使用自适应阈值进行模糊图像分类,与其他算法相比,准确率提升了11.0%~17.2%、F1分数提升了22.9%~30.9%。2)改进的特征匹配算法提升了特征匹配的质量,在KITTI数据集上,与常规特征匹配算法相比,内点率提升了11.6%~33.1%、匹配精度提升了30.4%~38.9%、匹配耗时减少了52.8%~55.8%。3)整体方法能减轻运动模糊对视觉SLAM定位的负面影响,与常规视觉SLAM方法相比,该方法在处理长距离复杂线路的图像序列时,平均绝对误差下降了10.4%~26.0%,均方根误差下降了10.0%~27.3%。To solve the problem wherein motion blur reduces the operational accuracy of visual simultaneous localization and mapping(SLAM),this study proposes an improved visual SLAM method based on blurred image evaluation and feature matching.First,following analysis of the generation principle of image motion blur,a blur parameter is designed based on the re-blurring theory to express the blur degree of the image.Then,adaptive thresholding is used to remove the blurred image.Finally,the grid-based motion statistics algorithm is improved for the feature matching process,replacing the commonly used feature matching method in SLAM.Experiments and analysis of two open source datasets are conducted under different environments.The results show that:1)the designed blur parameters effectively represent the blur degree of the image.In the prediction of image quality evaluation scores on a standard image library,the root mean square error is reduced by 9.3%-12.3%as compared with other algorithms.Compared with other algorithms when using adaptive thresholds for fuzzy image classification on the KITTI dataset,the accuracy rate and F1 score under the proposed method are increased by 11.0%-17.2%and 22.9%-30.9%,respectively.2)The improved feature matching algorithm improves the quality of feature matching.Compared with the conventional feature matching algorithm on the KITTI dataset,the interior point rate and matching accuracy under the proposed method are increased by 11.6%-33.1%and 30.4%-38.9%,respectively,and the matching time is reduced by 52.8%-55.8%.3)In general,the proposed method can reduce the negative effects of motion blur on visual SLAM positioning.Compared with conventional visual SLAM,when processing image sequences of long-distance complex lines,the proposed method reduces the average absolute error and RMS error by 10.4%-26.0%and 10.0%-27.3%,respectively.

关 键 词：同步定位与制图 运动模糊 图像质量评价 基于网格的运动统计算法 

分 类 号：P258[天文地球—测绘科学与技术]