计算机科学 ›› 2021, Vol. 48 ›› Issue (9): 223-234.doi: 10.11896/jsjkx.200700152
田野, 陈宏巍, 王法胜, 陈兴文
TIAN Ye, CHEN Hong-wei, WANG Fa-sheng, CHEN Xing-wen
摘要: SLAM(Simultaneous Localization and Mapping),即同时定位与地图构建,目前被广泛应用于机器人领域。SLAM算法使得机器人处于陌生环境时,能够通过自身搭载的传感器来感知环境信息并建立环境地图,并完成对自身位姿的计算,从而能够在未知环境中进行移动。随着研究者们对SLAM问题的深入研究,SLAM领域相关成果已非常丰富,但是有关室内场景SLAM的论述还不够系统。通过对现有的关于SLAM算法发展成果的总结和对比,对室内SLAM进行了综合性的阐述。首先介绍了SLAM的技术现状和室内场景SLAM在不同传感器下的分类问题;其次介绍了SLAM的经典框架;然后根据相关传感器种类的不同,简要介绍了不同传感器下常见的SLAM算法的原理,同时讨论了传统室内SLAM算法中存在的诸多局限性问题,引出了基于多传感器融合技术的SLAM和基于深度学习技术的SLAM两个研究方向;最后介绍了SLAM的未来发展趋势和应用领域。
中图分类号:
[1]LIU Z.Research status and future of mobile robot technology[J].Digital Technology and Application,2019,37(10):214-215. [2]MA Z G,ZHAO Y G,LIU C Y,et al.Review of Research on Laser and Vision Fusion SLAM Method[J].Computer Measurement and Control,2019,27(3):1-6. [3]SMITH R C,CHEESEMAN P.On the representation and estimation of spatial uncertainty[J].The International Journal of Robotics Research,1986,5(4):56-68. [4]SCARAMUZZA D,CADENA C,LEONARD J,et al.Past,Pre-sent,and Future of Simultaneous Localization and Mapping:Toward the Robust-Perception Age[J].IEEE Transactions on Robotics,2016,32(6):1309-1332. [5]LI Y T,MU R J,SHAN Y Z.A brief analysis of the development status of unmanned system vision SLAM technology[J/OL].Control and Decision,2020:1-10.http://kzyjc.alljournals.cn/ch/reader/download_new_edit_content.aspx?file_no=201908110000001&edit_id=20200106145046001&flag=2&year_id=&quarter_id=. [6]LENAC K,KITANO A,CUPEC R,et al.Fast Planar Surface 3D SLAM Using LIDAR[J].Robotics and Autonomous Systems,2017,92:197-220. [7]PIERZCHALA M,GIGUERE P,ASTRUP R.Mapping forests using an unmanned ground vehicle with 3D Lidar and graph-SLAM[J].Computer and Electronics in Agriculture,2018,145:217-225. [8]DROSECHEL D,BEHNKE S.Efficient Continuous-TimeSLAM for 3D Lidar-Based Online Mapping[C]//2018 IEEE International Conference on Robotics and Automation(ICRA).IEEE,2018:1-9. [9]BELBACHIR N,NOORI N,AKDEMIR B.Real-Time VehicleLocalization using on-Board Visual SLAM for Detection and Tracking[C]//2019 IEEE/CVF International Conference on Computer Vision Workshop(ICCVW).IEEE,2019:4291-4295. [10]SCHNEIDER H,DYMCZYK M,FEHR M,et al.Maplab:An Open Framework for Research in Visual-Inertial Mapping and Localization[J].IEEE Robotics and Automation Letters,2019,8(29):1418-1425. [11]DENG S Y,GUO C J.Research on real-time location and map construction method based on multi-sensor fusion[C]//Procee-dings of the 11th China Satellite Navigation Annual Conference-S13 Autonomous Navigation,Academic Exchange Center of China Satellite Navigation System Management Office.2020:6. [12]ZHAO Y,LIU G L,TIAN G H,et al.A Summary of VisualSLAM Based on Deep Learning[J].Robot,2017,39(6):889-896. [13]CHEN J Y,SHI Y C.Research progress of solid-state lidar[J].Opto-Electronic Engineering,2019,46(7):1-11. [14]GAO X,ZHANG T.The fourteen lectures on visual SLAM[M].Beijing:Publishing House of Electronics Industry,2017:97-107. [15]ZHAO L S,FENG Y,CAO Y.Optimization of SURF algorithm parameters in monocular visual positioning[J].Computer Technology and Development,2012,22(6):6-9. [16]WANG X W,HE L L,ZHAO T.Research on Mobile Robot SLAM Based on Lidar and Binocular Vision[J].Journal of Transduction Technology,2018,31(3):394-399. [17]PAN L H,TIAN F Q,YING W J,et al.Monocular camera-IMU external parameter automatic calibration and online estimation of visual-inertial navigation SLAM[J].Chinese Journal of Scientific Instrument,2019,40(6):56-67. [18]FAN J B,SUN J,FAN H H,et al.Autonomous positioningmethod based on magnetometer,IMU and monocular vision[J].Overall Aerospace Technology,2019,3(6):39-45. [19]LIU R J,WANG X S,ZHANG C,et al.A Summary of Visual SLAM Based on Deep Learning[J].Journal of System Simulation,2020,32(7):1244-1256. [20]WU Z Y,ZHANG C,LIN Y.Optimal design of laser SLAMalgorithm based on RBPF[J].Computer Engineering,2020,46(7):294-299. [21]WANG H,WANG P.Research and Implementation Based on RBPF-SLAM Algorithm[J].Computer Application System,2019,28(7):169-173. [22]MAO S L,DI W,HAO Y D.Indoor 2D Laser SLAM on a Raspberry Pi-Based Mobile Robot[C]//2019 11th International Conference on Intelligent Human-Machine Systems and Cybernetics(IHMSC).2019. [23]WONGSUWAN K,SUKVICHAI K.Generalizing correctivegradient refinement in RBPF for occupancy grid LIDAR SLAM[C]//2017 IEEE International Conference on Robotics and Bio-mimetics(ROBIO).IEEE,2017. [24]ZHANG L,LIU Z Y,CAO J Y,et al.Cartographer algorithm and system realization based on enhanced pose fusion of swee-ping robot[J].Journal of Software,2020,31(9):1-13. [25]WU C D,YAO J M,HU H L.Cartographer 2D SLAM algorithmresearch[J].Cable TV Technology,2018(4):20-22. [26]SANTOS J M,PORTUGAL D,RUI P R.An evaluation of 2D SLAM techniques available in Robot Operating System[C]//IEEE International Symposium on Safety,Security and Rescue Robotics.IEEE,2014:1-6. [27]HESS W,KOHLER D,RAPP H,et al.Real-time loop closure in2D lidar SLAM [C]//Proc of IEEE International Conference on Robotics and Automation.Piscataway,NJ:IEEE,2016:1271-1278. [28]JIAN J Z,LI X.Design and Implementation of ROS-Based Autonomous Mobile Robot Positioning and Navigation System[C]//2019 18th International Symposium on Distributed Computing and Applications for Business Engineering and Science(DCABES).2019. [29]KOHLBRECHER S,VON S O,MEYER J,et al,A flexible and scalable slam system with full 3d motion estimation[C]//2011 IEEE International Symposium on Safety,Security and Rescue Rootics.IEEE,2011:155-160. [30]YU N,ZHANG B.An Improved Hector SLAM Algorithmbased on Information Fusion for Mobile Robot[C]//2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems(CCIS).IEEE,2019:279-284. [31]QUAN M X,PU S H,LI G.Visual SLAM Review[J].Journal of Intelligent Systems,2016,11(6):768-776. [32]LV L H.Summary of Vision-based Real-time Location and Map Reconstruction(V-SLAM)[J].New Technology,2019:67-70. [33]DAVISON A J,REID I D,MOLTON N D,et al.Mono-SLAM:real-time single camera SLAM[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(6):1052-1067. [34]KLEIN G,MURRAY D.Parallel Tracking and Mapping forSmall AR Workspaces[C]//IEEE and ACM International Symposium on Mixed and Augmented Reality.2007:225-234. [35]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. [36]LIU H M,ZHANG G F,BAO H J.Overview of simultaneouspositioning and map construction methods based on monocular vision[J].Journal of Computer Aided Design and Graphics,2016,28(6):855-868. [37]MUR-ARTAL R,TARDOS J D.ORB-SLAM2:An Open-Source Slam System for Monocular,Stereo and RGB-D Came-ras[J].IEEE Transactions on Robotics,2017,33(5):1255-1262. [38]ZHANG Y,LI H S,MA L,et al.Research on real-time positioning of underwater robot based on ORB-SLAM2 algorithm[J].Bulletin of Surveying and Mapping,2019(12):1-7. [39]LU J W,WANG Y L.Construction of real-time grid map based on ORB-SLAM2[J].Application Research of Computers,2019,36(10):3124-3131. [40]HADDADI S J,CASTELAN E B.Visual-Inertial Fusionfor Indoor Autonomous Navigation of a Quadrotor Using ORB-SLAM[C]//2018 Latin American Robotic Symposium,2018 Brazilian Symposium on Robotics(SBR) and 2018 Workshop on Robotics in Education(WRE).2018. [41]SANTOS M,SOTO O,BUSTAMANTE V P.ORB-SLAMBased Active Disturbance Rejection Control for Quadrotor Autonomous Flight[C]//2018 XX Congreso Mexicano de Robótica(COMRob).2018. [42]FORSTER C,PIZZOLI M,SCARAMUZZA D.SVO:Fast semi-direct monocular visual odometry[C]//IEEE International Conference on Robotics and Automation.IEEE,2014:15-22. [43]XIAO Y,RUAN X G,ZHU X Q,et al.A Micro-UAV Monocular Vision SVO/INS Integrated Navigation Method[J].Journal of Chinese Inertial Technology,2019,27(2):211-219. [44]MENG Y,WANG W,HAN H,et al.A visual/inertial integra-ted landing guidance method for UAV landing on the ship[J].Aerospace Science and Technology,2019,85:474-480. [45]FORSTER C,GASSNER M,WERLBERGER M,et al.SVO:Semi-Direct Visual Odometry for Monocular and Multi-Camera Systems[J].IEEE Transactions on Robotics and Automation,2017,33(2):249-265. [46]HUANG T.Augmented reality system based on monocularvisual odometer[D].Guangzhou:South China University of Technology,2018. [47]LI X N,XU Z Y,ZHANG Y H.Determination of weight factors for multi-sensor data fusion[J].Journal of Spacecraft TT&C Technology,2005,24(1):65-67. [48]AYABAKAN T,KERESTECIOGLU F.Indoor positioningusing federated Kalman Filter[C]//26th Signal Processing and Communications Applications Conference(SIU).2018. [49]KAYA S B,ALKAR A Z.Indoor localization and tracking bymulti sensor fusion in Kalman filter[C]//26th Signal Processing and Communications Applications Conference(SIU).2018. [50]CHEN X L,WANG J L,SUN L,et al.A Method of Date Fusion for Multi-sensor Based on Bayesian Estimation[J].Journal of Electronic Measurement and Instrument,2009. [51]ZHANG P,DONG W H,GAO D D.An Optimal Method of Data Fusion for Multi-Sensors Based on Bayesian Estimation[J].Chinese Journal of Sensors and Actuators,2014,27(5):643-648. [52]KE L H.Research and Implementation of SLAM Based on Deep Learning[D].Xi’an:Xi’an Polytechnic University,2019. [53]LI S P,ZHANG T.Overview of the application of deep learning in visual SLAM[J].Space Control Technology and Application,2019,45(2):1-10. [54]KONDA K,MEMISEVIC R.Learning visual odometry with a convolutional network[C]//Proceedings of the 10th International Conference on Computer Vision Theory and Applications.Lisbon,Portugal:SCITCC Press,2015:486-490. [55]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,2016,1(1):18-25. [56]HE Y L,CHEN J T,ZENG B.Fast closed loop detection me-thod based on simplification convolutional neural network[J].Computer Engineering,2018,44(6):182-187. [57]YI H,HONG Z,ZHOU S.BoCNF:Efficient image matchingwith bag of Conv-Net features for scalable and robust visual place recognition[J].Autonomous Robots (S0929-5593),2017,42(9):1-17. [58]HOU Y,ZHANG H,ZHOU S L.Convolutional neural network based on image representation for visual loop closure detection[C]//IEEE International Conference on Information and Automation.Piscataway,USA:IEEE,2015:2238-2245. [59]SUNDERHAUF N,SHIRAZI S,DAYOUB F,et al.On the per- formance of Conv-Net features for place recognition[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems.Piscataway,USA:IEEE,2015:4297-4304. [60]MERRILL N,HUANG G.CALC2.0:Combining Appearance,Semantic and Geometric Information for Robust and Efficient Visual Loop Closure[C]//2019 IEEE/RSJInternational Confe-rence on Intelligent Robots and Systems(IROS).2019. [61]LUO S X,ZHANG S J.Research on Closed Loop DetectionAlgorithm Based on Deep Learning[J].Computer and Digital Engineering,2019,47(3):494-502. [62]GAO X,ZHANG T.Unsupervised learning to detect loops using deep neural networks for visual SLAM system[J].Autonomous Robots,2017,41(1):1-18. [63]VASUDEVAN S,GACHTER S,NGUYEN V,et al.Cogni-tive maps for mobile robots-an object based approach[J].Robo-tics and Automatic Systems,2007,55(5):359-371. [64]YAO E L,ZHANG H X,SONG H T,et al.Robust SLAM algorithm based on semantic information and edge consistency[J].Robot,2019,41(6):751-760. [65]SUNDERHAUF N,PHAM T T,LATIF Y,et al.MeaningfulMaps with Object-Oriented Semantic Mapping[C]//2017 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS).2017. [66]MA L,STUCKLER J,KARL C,et al.Multi-View Deep Lear-ning for Consistent Semantic Mapping with RGB-D Cameras[C]//2017 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS).2017. |
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