基于概率运动统计特征匹配的单目视觉SLAM

doi:10.19678/j.issn.1000-3428.0052746

摘要/Abstract

摘要： 在单目视觉同步定位与建图（SLAM）过程中，由于特征匹配阶段存在误匹配且耗时长，使得机器人初始化速度慢、定位精度不高。针对此问题，基于概率运动统计特征匹配，提出一种单目视觉SLAM算法。通过设置自适应的阈值提取ORB特征点并使用四叉树进行保存，根据运动的平滑性与特征匹配的一致性估计特定区域内特征匹配的概率模型，得到正确的特征匹配点进行匹配，完成系统自动初始化与机器人位姿跟踪。在TUM数据集上的实验结果表明，该算法在特征匹配阶段耗时仅为1.4 ms，机器人初始化时间和定位精度分别为1.7 s和0.54 cm，且具有良好的实时性。

关键词: 单目视觉同步定位与建图, 概率运动统计特征匹配, ORB特征, 初始化, 定位精度

Abstract: In the current monocular vision Simultaneous Localization and Mapping(SLAM) process,the feature matching phase is time-consuming,and mismatches tend to occur,so the robot often has a slow initialization speed and low positioning accuracy.To address these problems,we propose a monocular vision SLAM algorithm based on probabilistic motion statistical feature matching.Firstly,we set the adaptive threshold to extract the ORB feature points,which are simultaneously saved by the quadtree.Then,according to the smoothness of motion and the consistency of feature matching,we estimate the probability model of feature matching in a specific region.Thereby,the correct feature matching points are obtained and the system automatically initialization and robot pose tracking are achieved subsequently.The experimental results on TUM dataset show that the algorithm only takes 1.4 ms in the feature matching phase.Besides,the robot initialization time and positioning accuracy are 1.7 s and 0.54 cm respectively,which represents a good real-time performance.

Key words: monocular vision Simultaneous Localization and Mapping(SLAM), probabilistic motion statistics feature matching, ORB features, initialization, positioning accuracy

中图分类号:

TP391

曾维林, 刘桂华, 陈豪. 基于概率运动统计特征匹配的单目视觉SLAM[J]. 计算机工程, 2019, 45(12): 222-231,236.

ZENG Weilin, LIU Guihua, CHEN Hao. Monocular Vision SLAM Based on Probabilistic Motion Statistics Feature Matching[J]. Computer Engineering, 2019, 45(12): 222-231,236.

https://www.ecice06.com/CN/Y2019/V45/I12/222

图/表 23

20191214133835

20191214133837

20191214133840

20191214133842

20191214133845

20191214133848

20191214133851

20191214133853

20191214133856

20191214133858

20191214133901

20191214133904

20191214133906

20191214133909

20191214133912

20191214133915

20191214133919

20191214133922

20191214133925

20191214133929

20191214133932

20191214133936

20191214133941

参考文献

[1] CADENA C,CARLONE L,CARRILLO H,et al.Past,present,and future of simultaneous localization and mapping:toward the robust-perception age[J].IEEE Transactions on Robotics,2016,32(6):1309-1332.
[2] DUAN Huaxu,YAN Fei,ZHUANG Yan,et al.Simultaneous localization and mapping for UGVs with binocular camera[J].Journal of Huazhong University of Science and Technology(Nature Science Edition),2015,43(z1):319-323.(in Chinese) 段华旭,闫飞,庄严,等.无人车基于双目视觉的同时定位与地图构建[J].华中科技大学学报(自然科学版),2015,43(增刊):319-323.
[3] LIU Haomin,ZHANG Guofeng,BAO Hujun.A survey of monocular simultaneous localization and mapping[J].Journal of Computer-Aided Design & Computer Graphics,2016,28(6):855-868.(in Chinese) 刘浩敏,章国锋,鲍虎军.基于单目视觉的同时定位与地图构建方法综述[J].计算机辅助设计与图形学学报,2016,28(6):855-868.
[4] KLEIN G,MURRAY D.Parallel tracking and mapping for small AR workspaces[C]//Proceedings of the 6th IEEE and ACM International Symposium on Mixed and Augmented Reality.Washington D.C.,USA:IEEE Press,2007:1-10.
[5] TRIGGS B,MCLAUCHLAN P F,HARTLEY R I,et al.Bundle adjustment a modern synthesis[C]//Proceedings of International Workshop on Vision Algorithms.Berlin,Germany:Springer,1999:298-372.
[6] MUR-ARTAL R,MONTIEL J M M,TARDOS J D.ORB-SLAM:a versatile and accurate monocular SLAM system[J].IEEE Transactions on Robotics,2015,31(5):1147-1163.
[7] MUR-ARTAL R,TARDÓS J D.ORB-SLAM2:an open-source SLAM system for monocular,stereo,and RGB-D cameras[J].IEEE Transactions on Robotics,2017,33(5):1255-1262.
[8] ENGEL J,SCHÖPS T,CREMERS D.LSD-SLAM:large-scale direct monocular SLAM[C]//Proceedings of European Conference on Computer Vision.Berlin,Germany:Springer,2014:834-849.
[9] ENGEL J,KOLTUN V,CREMERS D.Direct sparse odometry[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,40(3):611-625.
[10] INDYK P,MOTWANI R.Approximate nearest neighbors:towards removing the curse of dimensionality[C]//Proceedings of the 30th Annual ACM Symposium on Theory of Computing.New York,USA:ACM Press,1998:604-613.
[11] MUJA M,LOWE D G.Fast approximate nearest neighbors with automatic algorithm configuration[C]//Proceedings of VISAPP'09.Lisboa,Portugal:[s.n.],2009:1-10.
[12] MUJA M,LOWE D G.Scalable nearest neighbor algorithms for high dimensional data[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2014,36(11):2227-2240.
[13] FISCHLER M A,BOLLES R C.Random sample consensus:a paradigm for model fitting with applications to image analysis and automated cartography[J].Communications of the ACM,1981,24(6):381-395.
[14] RAGURAM R,FRAHM J M,POLLEFEYS M.A comparative analysis of RANSAC techniques leading to adaptive real-time random sample consensus[C]//Proceedings of European Conference on Computer Vision.Berlin,Germany:Springer,2008:500-513.
[15] LIN Wenyan,WANG Fan,CHENG Mingming,et al.Code:coherence based decision boundaries for feature correspondence[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,40(1):34-47.
[16] LIN Wenyan,LIU Siying,JIANG Nianjuan,et al.Repmatch:robust feature matching and pose for reconstructing modern cities[C]//Proceedings of European Conference on Computer Vision.Berlin,Germany:Springer,2016:562-579.
[17] ROSTEN E,DRUMMOND T.Machine learning for high-speed corner detection[C]//Proceedings of European Conference on Computer Vision.Berlin,Germany:Springer,2006:430-443.
[18] CALONDER M,LEPETIT V,STRECHA C,et al.Brief:binary robust independent elementary features[C]//Proceedings of European Conference on Computer Vision.Berlin,Germany:Springer,2010:778-792.
[19] ZHAO Lina.Research and improvement of Fisher discriminant analysis method[D].Harbin:Northeast Forestry University,2013.(in Chinese) 赵丽娜.Fisher判别法的研究及应用[D].哈尔滨:东北林业大学,2013.
[20] LOWE D G.Distinctive image features from scale-invariant keypoints[J].International Journal of Computer Vision,2004,60(2):91-110.
[21] BAY H,TUYTELAARS T,VAN GOOL L.Surf:speeded up robust features[C]//Proceedings of European Conference on Computer Vision.Berlin,Germany:Springer,2006:404-417.
[22] RUBLEE E,RABAUD V,KONOLIGE K,et al.ORB:an efficient alternative to SIFT or SURF[C]//Proceedings of IEEE International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2011:1-8.
[23] HARTLEY R,ZISSERMAN A.Multiple view geometry in computer vision[M].Cambridge,UK:Cambridge University Press,2003.
[24] GÁLVEZ-LÓPEZ D,TARDOS J D.Bags of binary words for fast place recognition in image sequences[J].IEEE Transactions on Robotics,2012,28(5):1188-1197.
[25] STRASDAT H,MONTIEL J,DAVISON A J.Scale drift-aware large scale monocular SLAM[J].Robotics:Science and Systems,2010,2(3):73-80.
[26] STURM J,ENGELHARD N,ENDRES F,et al.A benchmark for the evaluation of RGB-D SLAM systems[C]//Proceedings of 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.Washington D.C.,USA:IEEE Press,2012:573-580.
[27] ENDRES F,HESS J,ENGELHARD N,et al.An evaluation of the RGB-D SLAM system[J].Proceedings of IEEE International Conference on Robotics and Automation.Washington D.C.,USA:IEEE Press,2012:1691-1696.
[28] KOHLBRECHER S,VON STRYK O,MEYER J,et al.A flexible and scalable SLAM system with full 3D motion estimation[C]//Proceedings of 2011 IEEE International Symposium on Safety,Security,and Rescue Robotics.Washington D.C,USA:IEEE Press,2011:155-160.
[29] SANTOS J M,PORTUGAL D,ROCHA R P.An evaluation of 2D SLAM techniques available in robot operating system[C]//Proceedings of 2013 IEEE International Symposium on Safety,Security,and Rescue Robotics.Washington D.C.,USA:IEEE Press,2013:1-6.

选择文件类型/文献管理软件名称

选择包含的内容