基于改进ORB算法的移动机器人视觉SLAM方法研究
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摘要
以移动机器人视觉导航为应用背景,针对传统ORB算法在视觉SLAM中存在特征点分布不均匀、重叠特征点较多的问题,提出一种改进ORB算法。首先,对每层图像的尺度空间金字塔进行网格划分,增加空间尺度信息;其次,在特征点检测时,采用改进FAST角点自适应阈值提取,设置感兴趣区域;然后,采用非极大值抑制的方法,抑制低阈值特征点的输出;最后,使用基于区域图像特征点分布的方差数值评价待检测图像中特征点的分布情况。实验结果表明,改进ORB算法特征点的分布较为均匀,输出特征点重叠数量较少,执行时间较短。
Taking mobile robot visual navigation as the application background, an improved ORB algorithm is proposed to solve the problems of feature points unevenly distributing and too many redundant features in visual SLAM. Firstly, the scale-space pyramid of each image is meshed to increase the scale information. Secondly, feature points are detected, using improved FAST corner points adaptive extraction and setting region of interest. Thirdly, non-maximum suppression method is used to suppress the output low threshold feature points. Finally, feature points variance values based on region image is used to evaluate of distribution feature points in images. Experiments verify that the improved ORB algorithm has more uniform distribution, fewer output overlapping fea-ture points and shorter run time.
Taking mobile robot visual navigation as the application background, an improved ORB algorithm is proposed to solve the problems of feature points unevenly distributing and too many redundant features in visual SLAM. Firstly, the scale-space pyramid of each image is meshed to increase the scale information. Secondly, feature points are detected, using improved FAST corner points adaptive extraction and setting region of interest. Thirdly, non-maximum suppression method is used to suppress the output low threshold feature points. Finally, feature points variance values based on region image is used to evaluate of distribution feature points in images. Experiments verify that the improved ORB algorithm has more uniform distribution, fewer output overlapping fea-ture points and shorter run time.
引文
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[7]毛星云.OpenCV3编程入门[M].北京:电子工业出版社,2015.
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[9]PRIVITERA C M,STARK L W.Algorithms for defining visual regions-of-interest:comparison with eye fixations[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,2000,22(9):970-982.
[10]HOSANG J,BENENSON R,SCHIELE B.A convnet for non-maximum suppression[M].Pattern Recognition.Springer International Publishing,2016:192-204.
[11]KEI O.ROS(robot operating system)[J].Journal of the Robotics Society of Japan,2012,30(9):830-835.
[12]宋艳.基于图像特征的RGB-D视觉SLAM算法[D].青岛:中国海洋大学,2015.
[2]党宏社,候金良,强华,等.基于视觉引导的SCARA机器人自动装配系统[J].电子技术应用,2017,43(5):21-24.
[3]LOWE D G.Distinctive image features from scale-invariant keypoints[J].International Journal of Computer Vision,2004,60(2):91-110.
[4]BAY H,ESS A,TUYTELAARS T,et al.Speeded-up robust features(SURF)[J].Computer Vision&Image Understanding,2008,110(3):346-359.
[5]RUBLEE E,RABAUD V,KONOLIGE K,et al.ORB:an efficient alternative to SIFT or SURF[C].International Conference on Computer Vision.IEEE,2012:2564-2571.
[6]刘宏伟,余辉亮,梁艳阳.ORB特征四叉树均匀分布算法[J].自动化仪表,2018,39(5):52-54,59.
[7]毛星云.OpenCV3编程入门[M].北京:电子工业出版社,2015.
[8]XU J,CHANG H W,YANG S,et al.Fast feature-based video stabilization without accumulative global motion estimation[J].IEEE Transactions on Consumer Electronics,2012,58(3):993-999.
[9]PRIVITERA C M,STARK L W.Algorithms for defining visual regions-of-interest:comparison with eye fixations[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,2000,22(9):970-982.
[10]HOSANG J,BENENSON R,SCHIELE B.A convnet for non-maximum suppression[M].Pattern Recognition.Springer International Publishing,2016:192-204.
[11]KEI O.ROS(robot operating system)[J].Journal of the Robotics Society of Japan,2012,30(9):830-835.
[12]宋艳.基于图像特征的RGB-D视觉SLAM算法[D].青岛:中国海洋大学,2015.