移动机器人中视觉里程计技术综述

doi:10.19678/j.issn.1000-3428.0060829

摘要/Abstract

摘要： 视觉里程计（VO）是实现移动机器人自主导航的主要技术之一，不同类型的VO技术在不同应用场景中受环境和硬件计算能力的影响，导致其性能各有优劣。概述VO技术的发展历程，对基于传统几何和基于深度学习的两类VO技术的性能进行对比与分析，重点介绍传统VO技术中特征点法的原理及其改进方法。在此基础上，归纳VO领域常用的公共数据集并对部分现有方法进行对比评测，为VO技术的实际应用提供参考和借鉴，并展望该领域未来的发展方向。

关键词: 视觉里程计, 特征点法, 深度学习, 位姿估计, 机器视觉

Abstract: Visual Odometry(VO)is one of the key technologies to realize autonomous navigation of mobile robots. Due to the influence of environment and hardware computing power,different VO technologies display varying performance in different application scenarios. This paper describes the development history of VO technologies, and compares the performance of traditional geometry-based VO technologies with deep learning-based technologies for analysis. Among all the traditional VO technologies,this paper focuses on the feature point method,describing its principles and improvement approaches.On this basis, the paper summarizes the commonly used public datasets for VO studies, and evaluates part of the existing methods in comparison to provide reference for VO applications.Finally, the paper discusses the future development directions of VO.

Key words: Visual Odometry (VO), feature point method, deep learning, pose estimation, machine vision

中图分类号:

TP391

马科伟, 张锲石, 康宇航, 任子良, 程俊. 移动机器人中视觉里程计技术综述[J]. 计算机工程, 2021, 47(11): 1-10.

MA Kewei, ZHANG Qieshi, KANG Yuhang, REN Ziliang, CHENG Jun. Overview of Visual Odometry Technology in Mobile Robots[J]. Computer Engineering, 2021, 47(11): 1-10.

http://www.ecice06.com/CN/Y2021/V47/I11/1

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参考文献

[1] NISTER D,NARODITSKY O,BERGEN J,et al.Visual odometry[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2004:625-659.
[2] MATTHIES L,SHAFER S.Error modeling in stereo navigation[J].IEEE Journal on Robotics and Automation,1987,3(3):239-248.
[3] DURRANT-WHYTE H,BAILEY T.Simultaneous localization and mapping:part I[J].IEEE Robotics & Automation Magazine,2006,13(2):99-110.
[4] BAILEY T,DURRANT-WHYTE H.Simultaneous localization and mapping:part II[J].IEEE Robotics & Automation Magazine,2006,13(3):108-117.
[5] KARLSSON N,DI BERNARDO E,OSTROWSKI J,et al.The SLAM algorithm for robust localization and mapping[C]//Proceedings of IEEE International Conference on Robotics and Automation.Washington D.C.,USA:IEEE Press,2005:24-29.
[6] 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,2007:225-234.
[7] FORSTER C,PIZZOLI M,SCARAMUZZA D.SVO:fast semi-direct monocular visual odometry[C]//Proceedings of IEEE International Conference on Robotics and Automation.Washington D.C.,USA:IEEE Press,2014:15-22.
[8] ENGEL J,KOLTUN V,CREMERS D.Direct sparse odometry[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,40(3):611-625.
[9] 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.
[10] LABBE M,MICHAUD F.Online global loop closure detection for large-scale multi-session graph-based SLAM[C]//Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems.Washington D.C.,USA:IEEE Press,2014:2661-2666.
[11] KERL C,STURM J,CREMERS D.Dense visual SLAM for RGB-D cameras[C]//Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems.Washington D.C.,USA:IEEE Press,2013:2100-2106.
[12] ENDRES F,HESS J,STURM J,et al.3-D mapping with an RGB-D camera[J].IEEE Transactions on Robotics,2013,30(1):177-187.
[13] LOWE D G.Distinctive image features from scale-invariant keypoints[J].International Journal of Computer Vision,2004,60(2):91-110.
[14] 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.
[15] RUBLEE E,RABAUD V,KONOLIGE K,et al.ORB:an efficient alternative to SIFT or SURF[C]//Proceedings of International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2011:2564-2571.
[16] DAVISON A J,REID I D,MOLTON N D,et al.MonoSLAM:real-time single camera SLAM[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(6):1052-1067.
[17] 杨冬冬,张晓林,李嘉茂.基于局部与全局优化的双目视觉里程计算法[J].计算机工程,2018,44(1):1-8. YANG D D,ZHANG X L,LI J M.Binocular visual odometry algorithm based on local and global optimization[J].Computer Engineering,2018,44(1):1-8.(in Chinese)
[18] NEWCOMBE R A,LOVEGROVE S J,DAVISON A J.DTAM:dense tracking and mapping in real-time[C]//Proceedings of International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2011:2320-2327.
[19] LEUTENEGGER S,LYNEN S,BOSSE M,et al.Keyframe-based visual-inertial odometry using nonlinear optimization[J].The International Journal of Robotics Research,2015,34(3):314-334.
[20] QIN T,LI P,SHEN S.Vins-mono:a robust and versatile monocular visual-inertial state estimator[J].IEEE Transactions on Robotics,2018,34(4):1004-1020.
[21] 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.
[22] CAMPOS C,ELVIRA R,RODRíGUEZ J J G,et al.ORB-SLAM3:an accurate open-source library for visual,visual-inertial and multi-map SLAM[EB/OL].[2021-01-01].https://arxiv.org/pdf/2007.11898.pdf.
[23] IZADI S,KIM D,HILLIGES O,et al.Kinect fusion:real-time 3D reconstruction and interaction using a moving depth camera[C]//Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology.New York,USA:ACM Press,2011:559-568.
[24] MORAVEC H.Obstacle avoidance and navigation in the real world by a seeing robot rover[EB/OL].[2021-01-01].https://www.ri.cmu.edu/pub_files/pub4/moravec_hans_1980_1/moravec_hans_1980_1.pdf.
[25] HARRIS C G,STEPHENS M.A combined corner and edge detector[EB/OL].[2021-01-01].https://home.cis.rit.edu/~cnspci/references/dip/feature_extraction/harris1988.pdf.
[26] SHI J.Good features to track[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,1994:593-600.
[27] 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.
[28] 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.
[29] 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.
[30] 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.
[31] ZHAO J,MA J,TIAN J,et al.A robust method for vector field learning with application to mismatch removing[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2011:2977-2984.
[32] LI H,HARTLEY R.Five-point motion estimation made easy[C]//Proceedings of the 18th International Conference on Pattern Recognition.Washington D.C.,USA:IEEE Press,2006:630-633.
[33] NISTÉR D.An efficient solution to the five-point relative pose problem[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(6):756-770.
[34] PIZARRO O,EUSTICE R M,SINGH H.Relative pose estimation for instrumented,calibrated imaging platforms[C]//Proceedings of DICTA'03.Washington D.C.,USA:IEEE Press,2003:601-612.
[35] HARTLEY R I.In defense of the eight-point algorithm[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(6):580-593.
[36] LONGUET-HIGGINS H C.A computer algorithm for reconstructing a scene from two projections[J].Nature,1981,293(5828):133-135.
[37] FRAUNDORFER F,TANSKANEN P,POLLEFEYS M.A minimal case solution to the calibrated relative pose problem for the case of two known orientation angles[C]//Proceedings of European Conference on Computer Vision.Berlin,Germany:Springer,2010:269-282.
[38] BESL P J,MCKAY H D.A method for registration of 3D shapes[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1992,14(2):239-256.
[39] GAO X S,HOU X R,TANG J,et al.Complete solution classification for the perspective-three-point problem[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(8):930-943.
[40] 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.
[41] WANG S,CLARK R,WEN H,et al.DeepVO:towards end-to-end visual odometry with deep recurrent convolutional neural networks[C]//Proceedings of IEEE International Conference on Robotics and Automation.Washington D.C.,USA:IEEE Press,2017:2043-2050.
[42] YIN Z,SHI J.GeoNet:unsupervised learning of dense depth,optical flow and camera pose[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2018:1983-1992.
[43] TATENO K,TOMBARI F,LAINA I,et al.CNN-SLAM:real-time dense monocular SLAM with learned depth prediction[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2017:6243-6252.
[44] ROBERTS R,NGUYEN H,KRISHNAMURTHI N,et al.Memory-based learning for visual odometry[C]//Proceedings of IEEE International Conference on Robotics and Automation.Washington D.C.,USA:IEEE Press,2008:47-52.
[45] KONDA K R,MEMISEVIC R.Learning visual odometry with a convolutional network[C]//Proceedings of International Conference on Computer Vision Theory and Applications.Washington D.C.,USA:IEEE Press,2015:486-490.
[46] COSTANTE G,MANCINI M,VALIGI P,et al.Exploring representation learning with CNNs for frame-to-frame ego-motion estimation[J].IEEE Robotics and Automation Letters,2015,1(1):18-25.
[47] MULLER P,SAVAKIS A.Flowdometry:an optical flow and deep learning based approach to visual odometry[C]//Proceedings of IEEE Winter Conference on Applications of Computer Vision.Washington D.C.,USA:IEEE Press,2017:624-631.
[48] LIN Y,LIU Z,HUANG J,et al.Deep global-relative networks for end-to-end 6-dof visual localization and odometry[C]//Proceedings of Pacific Rim International Conference on Artificial Intelligence.Berlin,Germany:Springer,2019:454-467.
[49] JIAO J,JIAO J,MO Y,et al.MagicVO:end-to-end monocular visual odometry through deep bi-directional recurrent convolutional neural network[EB/OL].[2021-01-01].https://arxiv.org/ftp/arxiv/papers/1811/1811.10964.pdf.
[50] YU C,LIU Z,LIU X J,et al.DS-SLAM:a semantic visual SLAM towards dynamic environments[C]//Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems.Washington D.C.,USA:IEEE Press,2018:1168-1174.
[51] ALMALIOGLU Y,SAPUTRA M R U,DE GUSMAO P P,et al.GANVO:unsupervised deep monocular visual odometry and depth estimation with generative adversarial networks[C]//Proceedings of International Conference on Robotics and Automation.Washington D.C.,USA:IEEE Press,2019:5474-5480.
[52] PANG S,MORRIS D,RADHA H.CLOCs:camera-LiDAR object candidates fusion for 3D object detection[EB/OL].[2021-01-01].https://arxiv.org/pdf/2009.00784.pdf.
[53] YANG N,STUMBERG L V,WANG R,et al.D3VO:deep depth,deep pose and deep uncertainty for monocular visual odometry[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2020:1281-1292.
[54] LOO S Y,AMIRI A J,MASHOHOR S,et al.CNN-SVO:improving the mapping in semi-direct visual odometry using single-image depth prediction[C]//Proceedings of International Conference on Robotics and Automation.Washington D.C.,USA:IEEE Press,2019:5218-5223.
[55] LI R,WANG S,LONG Z,et al.UndeepVO:monocular visual odometry through unsupervised deep learning[C]//Proceedings of IEEE International Conference on Robotics and Automation.Washington D.C.,USA:IEEE Press,2018:7286-7291.
[56] COSTANTE G,MANCINI M.Uncertainty estimation for data-driven visual odometry[J].IEEE Transactions on Robotics,2020,36(6):1738-1757.
[57] ZHAN H,WEERASEKERA C S,BIAN J W,et al.Visual odometry revisited:what should be learnt?[C]//Proceedings of IEEE International Conference on Robotics and Automation.Washington D.C.,USA:IEEE Press,2020:4203-4210.
[58] GEIGER A,LENZ P,URTASUN R.Are we ready for autonomous driving? The KITTI vision benchmark suite[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2012:3354-3361.
[59] STURM J,ENGELHARD N,ENDRES F,et al.A benchmark for the evaluation of RGB-D SLAM systems[C]//Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems.Washington D.C.,USA:IEEE Press,2012:573-580.
[60] BURRI M,NIKOLIC J,GOHL P,et al.The EuRoC micro aerial vehicle datasets[J].The International Journal of Robotics Research,2016,35(10):1157-1163.
[61] MADDERN W,PASCOE G,LINEGAR C,et al.1 year,1000 km:the Oxford robotcar dataset[J].The International Journal of Robotics Research,2017,36(1):3-15.
[62] HODAN T,HALUZA P,OBDRŽÁLEK Š,et al.T-LESS:an RGB-D dataset for 6D pose estimation of texture-less objects[C]//Proceedings of IEEE Winter Conference on Applications of Computer Vision.Washington D.C.,USA:IEEE Press,2017:880-888.
[63] GASPAR A R,NUNES A,PINTO A M,et al.Urban@CRAS dataset:benchmarking of visual odometry and SLAM techniques[J].Robotics and Autonomous Systems,2018,109:59-67.
[64] WENZEL P,WANG R,YANG N,et al.4Seasons:a cross-season dataset for multi-weather SLAM in autonomous driving[EB/OL].[2021-01-01].https://vision.in.tum.de/_media/spezial/bib/wenzel2020fourseasons.pdf.
[65] WANG W,ZHU D,WANG X,et al.TartanAir:a dataset to push the limits of visual SLAM[EB/OL].[2021-01-01].https://arxiv.org/pdf/2003.14338.pdf.
[66] ZUÑIGA-NOËL D,JAENAL A,GOMEZ-OJEDA R,et al.The UMA-VI dataset:visual-inertial odometry in low-textured and dynamic illumination environments[J].The International Journal of Robotics Research,2020,39(9):1052-1060.
[67] PIRE T,FISCHER T,CIVERA J,et al.Stereo parallel tracking and mapping for robot localization[C]//Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems.Washington D.C.,USA:IEEE Press,2015:1373-1378.

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