[1] Bulusu N, Heidemann J, Estrin D. GPS-less low-cost outdoor localization for very small devices. IEEE Personal Communications, 2000, 7(5): 28-34 doi: 10.1109/98.878533
[2] Doherty L, Pister K S J, El Ghaoui L. Convex position estimation in wireless sensor networks. In: IEEE INFOCOM, Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies. Anchorage, United States: IEEE, 2001. 1655-1663 https://www.researchgate.net/publication/3893900_Convex_position_estimation_in_wireless_sensor_networks
[3] Hightower J, Borriello G. Location systems for ubiquitous computing. Computer, 2001, 34(8): 57-66 doi: 10.1109/2.940014
[4] Savvides A, Han C C, Strivastava M B. Dynamic fine-grained localization in Ad-Hoc networks of sensors. In: Proceedings of the 7th Annual International Conference on Mobile Computing and Networking. Rome, Italy: ACM, 2001. 166-179 https://www.researchgate.net/publication/220926176_Srivastava_Dynamic_Fine-grained_Localization_in_Ad-Hoc_Networks_of_Sensors
[5] Savarese C, Rabaey J M, Langendoen K. Robust positioning algorithms for distributed Ad-Hoc wireless sensor networks. In: Proceedings of the General Track of the Annual Conference on USENIX Annual Technical Conference. Monterey, USA: USENIX Association, 2002. 317-327 https://www.researchgate.net/publication/2529202_Robust_Positioning_Algorithms_for_Distributed_Ad-Hoc_Wireless_Sensor_Networks
[6] Hays P A. Proton nuclear magnetic resonance spectroscopy (NMR) methods for determining the purity of reference drug standards and illicit forensic drug seizures. Journal of Forensic Sciences, 2005, 50(6): 1342-1360 http://cn.bing.com/academic/profile?id=3517aaf1c734242ab4ed9eeb65736e5c&encoded=0&v=paper_preview&mkt=zh-cn
[7] Kaufmann K W, Lemmon G H, DeLuca S L, Sheehan J H, Meiler J. Practically useful: what the Rosetta protein modeling suite can do for you. Biochemistry, 2010, 49(14): 2987-2998 doi: 10.1021/bi902153g
[8] Sit A, Wu Z J, Yuan Y X. A geometric buildup algorithm for the solution of the distance geometry problem using least-squares approximation. Bulletin of Mathematical Biology, 2009, 71(8): 1914-1933 doi: 10.1007/s11538-009-9431-9
[9] Sit A, Wu Z J. Solving a generalized distance geometry problem for protein structure determination. Bulletin of Mathematical Biology, 2011, 73(12): 2809-2836 doi: 10.1007/s11538-011-9644-6
[10] 刘勘, 周晓峥, 周洞汝.数据可视化的研究与发展.计算机工程, 2002, 28(8): 1-2, 63 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jsjgc200208001

Liu Kan, Zhou Xiao-Zheng, Zhou Dong-Ru. Data visualization research and development. Computer Engineering, 2002, 28(8): 1-2, 63 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jsjgc200208001
[11] Wilkinson L. SYSTAT: the System for Statistics. Evanston, IL: SYSTAT, Inc., 1986.
[12] Kümmerle R, Grisetti G, Strasdat H, Konolige K, Burgard W. $G.2o$: a general framework for graph optimization. In: IEEE International Conference on Robotics and Automation. Shanghai, China: IEEE, 2011. 3607-3613
[13] Li Z, Trappe W, Zhang Y, Nath B. Robust statistical methods for securing wireless localization in sensor networks. In: Proceedings of the 4th International Symposium on Information Processing in Sensor Networks. Boise, USA: IEEE, 2005. 91-98 https://www.researchgate.net/publication/4149846_Robust_statistical_methods_for_securing_wireless_localization_in_sensor_networks
[14] Saxe J B. Embeddability of Weighted Graphs in K-Space is Strongly NP-Hard. Qatar: Carnegie-Mellon University, 1980.
[15] Yemini Y. Some theoretical aspects of position-location problems. In: Proceedings of the 20th Annual Symposium on Foundations of Computer Science. Washington D. C., USA: IEEE, 1979. 1-8 http://www.sciencedirect.com/science/article/pii/001457938680151X
[16] Jackson B, Jordán T. Connected rigidity matroids and unique realizations of graphs. Journal of Combinatorial Theory, Series B, 2005, 94(1): 1-29 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=593b97de641132c7f040a84dc72841fa
[17] Thomas F, Ros L. Revisiting trilateration for robot localization. IEEE Transactions on Robotics, 2005, 21(1): 93-101 doi: 10.1109/TRO.2004.833793
[18] Čapkun S, Buttyán L, Hubaux J P. SECTOR: secure tracking of node encounters in multi-hop wireless networks. In: Proceedings of the 1st ACM Workshop on Security of Ad Hoc and Sensor Networks. Fairfax, Virginia: ACM, 2003. 21-32 https://www.researchgate.net/publication/37402492_SECTOR_Secure_Tracking_of_Node_Encounters_in_Multi-hop_Wireless_Networks
[19] Lazos L, Poovendran R. SeRLoc: secure range-independent localization for wireless sensor networks. In: Proceedings of the 3rd ACM Workshop on Wireless Security. Philadelphia, PA, USA: ACM, 2004. 21-30 https://www.researchgate.net/publication/221005650_SeRLoc_Secure_Range-Independent_Localization_for_Wireless_Sensor_Networks
[20] Kusy B, Ledeczi A, Maroti M, Meertens L. Node-density independent localization. In: Proceedings of the 5th International Conference on Information Processing in Sensor Networks. Nashville, USA: IEEE, 2006. 441-448 https://www.researchgate.net/publication/224642430_Node-density_independent_localization
[21] Hendrickson B. The molecule problem: exploiting structure in global optimization. Siam Journal on Optimization, 1995, 5(4): 835-857 doi: 10.1137/0805040
[22] Shang Y, Ruml W, Zhang Y, Fromherz M P J. Localization from mere connectivity. In: Proceedings of the 4th ACM International Symposium on Mobile Ad Hoc Networking and Computing. Annapolis, Maryland, USA: ACM, 2003. 201-212 https://www.researchgate.net/publication/2892241_Localization_from_Mere_Connectivity
[23] Shang Y, Ruml W. Improved MDS-based localization. In: EEE INFOCOM 2004. Hong Kong, China: IEEE, 2004. 2640-2651 https://www.researchgate.net/publication/4102894_Improved_MDS-based_localization
[24] Shang Y, Ruml W, Zhang Y, Fromherz M. Localization from connectivity in sensor networks. IEEE Transactions on Parallel and Distributed Systems, 2004, 15(11): 961-974 doi: 10.1109/TPDS.2004.67
[25] Biswas P, Lian T C, Wang T C, Ye Y Y. Semidefinite programming based algorithms for sensor network localization. ACM Transactions on Sensor Networks, 2006, 2(2): 188-220 doi: 10.1145/1149283.1149286
[26] Biswas P, Aghajan H, Ye Y Y. Semidefinite programming algorithms for sensor network localization using angle information. In: Proceedings of the Conference Record of the Thirty-Ninth Asilomar Conference on Signals, Systems and Computers. Pacific Grove, CA, USA: IEEE, 2005. 220-224 http://www.researchgate.net/publication/4225543_Semidefinite_Programming_Algorithms_for_Sensor_Network_Localization_using_Angle_Information
[27] Biswas P, Liang T C, Toh K C, Ye Y, Wang T C. Semidefinite programming approaches for sensor network localization with noisy distance measurements. IEEE Transactions on Automation Science and Engineering, 2006, 3(4): 360-371 doi: 10.1109/TASE.2006.877401
[28] Zhu Z S, So A M C, Ye Y Y. Universal rigidity: towards accurate and efficient localization of wireless networks. In: Proceedings IEEE INFOCOM. San Diego, USA: IEEE, 2010. 1-9
[29] Boyd S, El Ghaoui L, Feron E, Balakrishnan V. Linear Matrix Inequalities in System and Control Theory. Philadelphia: Society for Industrial and Applied Mathematics, 1994. http://library.wur.nl/WebQuery/clc/1669445
[30] Liang T C, Wang T C, Ye Y Y. A Gradient Search Method to Round the Semidefinite Programming Relaxation Solution for Ad Hoc Wireless Sensor Network Localization, Technical Report, Department of Management Science and Engineering, Stanford University, 2004.
[31] Cox T F, Cox M A A. Multidimensional Scaling (Second Edition). London: Chapman Hall/CRC, 2000.
[32] Savvides A, Park H, Srivastava M B. The bits and flops of the n-hop multilateration primitive for node localization problems. In: Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications. Atlanta, USA: ACM, 2002. 112-121
[33] Costa J A, Patwari N, Hero A O Ⅲ. Distributed weighted-multidimensional scaling for node localization in sensor networks. ACM Transactions on Sensor Networks, 2006, 2(1): 39-64 http://d.old.wanfangdata.com.cn/OAPaper/oai_doaj-articles_1757e346002855db37f97718c6d6e3b1
[34] Groenen P J F. The Majorization Approach to Multidimensional Scaling: Some Problems and Extensions[Ph.D. dissertation], Leiden University, Leiden, The Netherlands, 1993 https://www.researchgate.net/publication/243785955_The_majorization_approach_to_multidimensional_scaling_some_problems_and_extensions
[35] Press W H, Teukolsky S A, Vetterling W T, Flannery B P. Numerical Recipes (Second Edition). Cambridge: Cambridge University Press, 1992.
[36] Lourakis M I A, Argyros A A. SBA: a software package for generic sparse bundle adjustment. ACM Transactions on Mathematical Software, 2009, 36(1): Article No. 2 http://d.old.wanfangdata.com.cn/Periodical/wjclxb200304024
[37] Endres F, Hess J, Engelhard N, Sturm J, Cremers D, Burgard W. An evaluation of the RGB-D SLAM system. In: Proceedings of the 2012 IEEE International Conference on Robotics and Automation. Saint Paul, MN, USA: IEEE, 2012. 1691-1696 https://www.researchgate.net/publication/254041427_An_evaluation_of_the_RGB-D_SLAM_system
[38] Sturm J, Engelhard N, Endres F, Burgard W, Cremers D. A benchmark for the evaluation of RGB-D SLAM systems. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. Vilamoura, Portugal: IEEE, 2012. 573-580 https://www.researchgate.net/publication/261353760_A_benchmark_for_the_evaluation_of_RGB-D_SLAM_systems?ev=auth_pub
[39] Engel J, Schōps T, Cremers D. LSD-SLAM: large-scale direct monocular SLAM. In: European Conference on Computer Vision. Zurich, Switzerland: Springer, 2014. 834-849 http://www.researchgate.net/publication/290620817_LSD-SLAM_Large-Scale_Direct_Monocular_SLAM
[40] Forster C, Pizzoli M, Scaramuzza D. SVO: fast semi-direct monocular visual odometry. In: Proceedings of the 2014 IEEE International Conference on Robotics and Automation. Hong Kong, China: IEEE, 2014. 15-22 https://www.researchgate.net/publication/262378002_SVO_Fast_Semi-Direct_Monocular_Visual_Odometry
[41] Fraundorfer F, Heng L, Honegger D, Lee G H, Meier L, Tanskanen P, et al. Vision-based autonomous mapping and exploration using a quadrotor MAV. In: Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. Vilamoura, Portugal: IEEE, 2012. 4557-4564 http://www.researchgate.net/publication/261353707_Vision-based_autonomous_mapping_and_exploration_using_a_quadrotor_MAV
[42] De Leeuw J. Applications of convex analysis to multidimensional scaling. Department of Statistics, UCLA, 2005. http://www.researchgate.net/publication/239417876_APPLICATIONS_OF_CONVEX_ANALYSIS_TO_MULTIDIMENSIONAL_SCALING
[43] Borg I, Groenen P J F. Modern Multidimensional Scaling: Theory and Applications (Second Edition). New York, NY: Springer, 2005.
[44] Moore D C. Robust distribution sensor network localization with noisy range measurements. Massachusetts Institute of Technology, 2005. 50-61 https://www.researchgate.net/publication/35943813_Robust_distribution_sensor_network_localization_with_noisy_range_measurements
[45] Koren Y, Gotsman C, Ben-Chen M. PATCHWORK: efficient localization for sensor networks by distributed global optimization. Technical Report, 2005. https://www.researchgate.net/publication/254039773_PATCHWORK_Efficient_Localization_for_Sensor_Networks_by_Distributed_Global_Optimization
[46] Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding. Science, 2000, 290(5500): 2323-2326 doi: 10.1126/science.290.5500.2323
[47] Shi J B, Malik J. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 888-905 doi: 10.1109/34.868688
[48] Zhang L, Liu L G, Gotsman C, Gortler S J. An as-rigid-as-possible approach to sensor network localization. ACM Transactions on Sensor Networks, 2010, 6(4): Article No. 35 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=fa857a82ca6af6ecf09b9cba6458a2e5
[49] Sorkine O, Alexa M. As-rigid-as-possible surface modeling. In: Proceedings of the 5th Eurographics Symposium on Geometry Processing. Barcelona, Spain, 2007. 109-116 https://www.researchgate.net/publication/221316589_As-Rigid-As-Possible_Surface_Modeling
[50] Alexa M, Cohen-Or D, Levin D. As-rigid-as-possible shape interpolation. In: Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques. New Orleans, USA: ACM, 2000. 157-164 https://www.researchgate.net/publication/2538876_As-Rigid-As-Possible_Shape_Interpolation
[51] Press W H, Teukolsky S A, Vetterling W T, Flannery B P. Numerical Recipes: The Art of Scientific Computing (3rd Edition). Cambridge: Cambridge University Press, 2007.
[52] Cornilescu G, Marquardt J L, Ottiger M, Bax A. Validation of protein structure from anisotropic carbonyl chemical shifts in a dilute liquid crystalline phase. Journal of the American Chemical Society, 1998, 120(27): 6836-6837 doi: 10.1021/ja9812610
[53] Hendrickson B. Conditions for unique graph realizations. SIAM Journal on Computing, 1992, 21(1): 65-84 doi: 10.1137/0221008
[54] Gower J C, Dijksterhuis G B. Procrustes Problems. Oxford: Oxford University Press, 2004.
[55] Golub G H, Van Loan C F. Matrix Computations (4th Edition). Baltimore, MD: Johns Hopkins University Press, 2012.
[56] Gortler S J, Healy A D, Thurston D P. Characterizing generic global rigidity. American Journal of Mathematics, 2010, 132(4): 897-939 doi: 10.1353/ajm.0.0132
[57] Cucuringu M, Lipman Y, Singer A. Sensor network localization by eigenvector synchronization over the euclidean group. ACM Transactions on Sensor Networks, 2012, 8(3): Article No. 19 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=51a43c69302e35adee96e330ee6450b8
[58] Sobeih A, Hack M, Liu Z, Zhang L. Almost peer-to-peer clock synchronization. In: Proceedings of the 2007 IEEE International Parallel and Distributed Processing Symposium. Rome: IEEE, 2007. 1-10 https://www.researchgate.net/publication/220951841_Almost_Peer-to-Peer_Clock_Synchronization?ev=prf_cit
[59] Mainwaring A, Culler D, Polastre J, Szewczyk R, Anderson J. Wireless sensor networks for habitat monitoring. In: Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications. Atlanta, USA: ACM, 2002. 88-97
[60] Brust M R, Akbaș M I, Turgut D. Multi-hop localization system for environmental monitoring in wireless sensor and actor networks. Concurrency and Computation: Practice and Experience, 2013, 25(5): 701-717 doi: 10.1002/cpe.1812
[61] Wang X P, Liu Y H, Yang Z, Lu K, Luo J. Robust component-based localizationin sparse networks. IEEE Transactions on Parallel and Distributed Systems, 2014, 25(5): 1317-1327 doi: 10.1109/TPDS.2013.85
[62] Korniienko A, Khan U A, Scorletti G. Robust sensor localization with locally-computed, global $H_\infty$-design. In: Proceedings of the 2016 American Control Conference. Boston, USA: IEEE, 2016. 6375-6380
[63] Khan U A, Kar S, Moura J M F. DILAND: an algorithm for distributed sensor localization with noisy distance measurements. IEEE Transactions on Signal Processing, 2010, 58(3): 1940-1947 doi: 10.1109/TSP.2009.2038423
[64] Lin Z Y, Fu M Y, Diao Y F. Distributed self localization for relative position sensing networks in 2D space. IEEE Transactions on Signal Processing, 2015, 63(14): 3751-3761 doi: 10.1109/TSP.2015.2432739
[65] Hubbell W L, Cafiso D S, Altenbach C. Identifying conformational changes with site-directed spin labeling. Nature Structural and Molecular Biology, 2000, 7(9): 735-739 doi: 10.1038/78956
[66] Oh K J, Altenbach C, Collier R J, Hubbell W L. Site-directed spin labeling of proteins. Bacterial Toxins: Methods and Protocols. New York: Humana Press, 2000. 147-169
[67] Dong Q F, Wu Z J. A linear-time algorithm for solving the molecular distance geometry problem with exact inter-atomic distances. Journal of Global Optimization, 2002, 22(1-4): 365-375 https://www.researchgate.net/publication/227120408_A_linear-time_algorithm_for_solving_the_molecular_distance_geometry_problem_with_exact_inter-atomic_distances
[68] Wu D, Wu Z J. An updated geometric build-up algorithm for solving the molecular distance geometry problems with sparse distance data. Journal of Global Optimization, 2007, 37(4): 661-673 doi: 10.1007/s10898-006-9080-6
[69] Lavor C, Liberti L, Donald B, Worley B, Bardiaux B, Malliavin T E, Nilges M. Minimal NMR distance information for rigidity of protein graphs. Discrete Applied Mathematics, 2019, 256: 91-104 doi: 10.1016/j.dam.2018.03.071
[70] Blumenthal L M. Theory and Applications of Distance Geometry. Oxford: Clarendon Press, 1953.
[71] Moré J J, Wu Z J. Distance geometry optimization for protein structures. Journal of Global Optimization, 1999, 15(3): 219-234 doi: 10.1023/A:1008380219900
[72] Cleveland W S, McGill M E. Dynamic Graphics for Statistics. Belmont, California: Wadsworth, 1988.
[73] Littlefield R J. Using the glyph concept to create user-definable display formats. Pacific Northwest Lab, Richland, USA, 1983.
[74] 柳萌萌, 赵书良, 韩玉辉, 苏东海, 李晓超, 陈敏.多尺度数据挖掘方法.软件学报, 2016, 27(12): 3030-3050 http://d.old.wanfangdata.com.cn/Periodical/jsjkx201904009

Liu Meng-Meng, Zhao Shu-Liang, Han Yu-Hui, Su Dong-Hai, Li Xiao-Chao, Chen Min. Research on multi-scale data mining method. Journal of Software, 2016, 27(12): 3030-3050 http://d.old.wanfangdata.com.cn/Periodical/jsjkx201904009
[75] Levy S. A cross-cultural analysis of the structure and levels of attitudes towards acts of political protest. Social Indicators Research, 1983, 12(3): 281-309 doi: 10.1007/BF00319806
[76] Castellanos J A, Montiel J M M, Neira J, Tardos J D. The SPmap: a probabilistic framework for simultaneous localization and map building. IEEE Transactions on Robotics and Automation, 1999, 15(5): 948-952 doi: 10.1109/70.795798
[77] Paz L M, Tardos J D, Neira J. Divide and conquer: EKF SLAM in O($n$). IEEE Transactions on Robotics, 2008, 24(5): 1107-1120 doi: 10.1109/TRO.2008.2004639
[78] Montemerlo M, Thrun S, Koller D, Wegbreit B. FastSLAM: a factored solution to the simultaneous localization and mapping problem. In: Proceedings of the AAAI National Conference on Artificial Intelligence. San Francisco: AAAI, 2002. 593-598
[79] Hahnel D, Burgard W, Fox D, Thrun S. An efficient fastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. Las Vegas, USA: IEEE, 2003. 206-211
[80] Eustice R M, Singh H, Leonard J J. Exactly sparse delayed-state filters. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation. Barcelona, Spain: IEEE, 2005. 2417-2424 https://www.researchgate.net/publication/221077974_Exactly_Sparse_Delayed-State_Filters
[81] Thrun S, Liu Y F, Koller D, Ng A Y, Ghahramani Z, Durrant-Whyte H. Simultaneous localization and mapping with sparse extended information filters. International Journal of Robotics Research, 2004, 23(7-8): 693-716 doi: 10.1177/0278364904045479
[82] Grisetti G, Kummerle R, Stachniss C, Burgard W. A tutorial on graph-based SLAM. IEEE Intelligent Transportation Systems Magazine, 2010, 2(4): 31-43 doi: 10.1109/MITS.2010.939925
[83] Saez J M, Hogue A, Escolano F, Jenkin M. Underwater 3D SLAM through entropy minimization. In: Proceedings of the 2006 IEEE International Conference on Robotics and Automation. Orlando, USA: IEEE, 2006. 3562-3567 http://www.researchgate.net/publication/221069355_Underwater_3D_SLAM_through_Entropy_Minimization
[84] Kümmerle R, Steder B, Dornhege C, Kleiner A, Grisetti G, Burgard W. Large scale graph-based SLAM using aerial images as prior information. Autonomous Robots, 2011, 30(1): 25-39 doi: 10.1007/s10514-010-9204-1
[85] 张毅, 汪龙峰, 余佳航.基于深度信息的移动机器人室内环境三维地图创建.计算机应用, 2014, 34(12): 3438-3440 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jsjyy201412015

Zhang Yi, Wang Long-Feng, Yu Jia-Hang. Depth-image based 3D map reconstruction of indoor environment for mobile robots. Journal of Computer Applications, 2014, 34(12): 3438-3440 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jsjyy201412015
[86] Levi Z, Gotsman C. Smooth rotation enhanced As-Rigid-As-Possible mesh animation. IEEE Transactions on Visualization and Computer Graphics, 2015, 21(2): 264-277 doi: 10.1109/TVCG.2014.2359463
[87] Parashar S, Pizarro D, Bartoli A, Collins T. As-rigid-as-possible volumetric shape-from-template. In: Proceedings of the 2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015. 891-899 https://www.researchgate.net/publication/300408305_As-Rigid-as-Possible_Volumetric_Shape-from-Template?ev=auth_pub
[88] Jacobs D J, Hendrickson B. An algorithm for two-dimensional rigidity percolation: the pebble game. Journal of Computational Physics, 1997, 137(2): 346-365 doi: 10.1006/jcph.1997.5809
[89] Connelly R. Generic global rigidity. Discrete & Computational Geometry, 2005, 33(4): 549-563 http://www.researchgate.net/profile/R_Connelly/publication/220453469_Generic_Global_Rigidity/links/5447b0890cf2f14fb8121c28?ev=pub_ext_doc_dl_meta