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机器人室内语义建图中的场所感知方法综述

朱博 高翔 赵燕喃

朱博, 高翔, 赵燕喃. 机器人室内语义建图中的场所感知方法综述. 自动化学报, 2017, 43(4): 493-508. doi: 10.16383/j.aas.2017.c160350
引用本文: 朱博, 高翔, 赵燕喃. 机器人室内语义建图中的场所感知方法综述. 自动化学报, 2017, 43(4): 493-508. doi: 10.16383/j.aas.2017.c160350
ZHU Bo, GAO Xiang, ZHAO Yan-Nan. Place Perception for Robot Indoor Semantic Mapping: A Survey. ACTA AUTOMATICA SINICA, 2017, 43(4): 493-508. doi: 10.16383/j.aas.2017.c160350
Citation: ZHU Bo, GAO Xiang, ZHAO Yan-Nan. Place Perception for Robot Indoor Semantic Mapping: A Survey. ACTA AUTOMATICA SINICA, 2017, 43(4): 493-508. doi: 10.16383/j.aas.2017.c160350

机器人室内语义建图中的场所感知方法综述

doi: 10.16383/j.aas.2017.c160350
基金项目: 

国家自然科学基金 61603195

江苏省自然科学基金 BK20140878

南京邮电大学引进人才科研启动基金资助项目 NY214018

南京邮电大学国家自然科学基金孵化项目 NY215131

详细信息
    作者简介:

    高翔 南京邮电大学自动化学院教授.主要研究方向为机器人传感技术.E-mail:gaoxnj@126.com

    赵燕喃 南京邮电大学自动化学院本科生.E-mail:840898594@qq.com

    通讯作者:

    朱博 南京邮电大学自动化学院讲师.2014年获得东南大学控制理论与控制工程博士学位.主要研究方向为机器人环境感知, 机器人视觉, 语义建图.E-mail:zhuboseu@163.com

Place Perception for Robot Indoor Semantic Mapping: A Survey

Funds: 

National Natural Science Foundation of China 61603195

Natural Science Foundation of Jiangsu Province BK20140878

Introduction of talent research start-up fund of NUPT NY214018

Incubation Project for National Natural Science Foundation Project of Nanjing University of Posts and Telecommunications NY215131

More Information
    Author Bio:

    Professor at the School of Automation, Nanjing University of Posts and Telecommunications. Her main research interest is robot sensing technology

    Bachelor student at the School of Automation, Nanjing University of Posts and Telecommunications

    Corresponding author: ZHU Bo Lecturer at the School of Automation, Nanjing University of Posts and Telecommunications. He received his Ph. D. degree from Southeast University in 2014. His research interest covers robot environment perception, robot vision, and semantic mapping. Corresponding author of this paper
  • 摘要: 场所感知问题是机器人语义地图研究的关键问题之一,本文对室内语义地图相关的场所感知方法进行全面综述.首先,根据近年的文献给出场所概念的描述性定义,对研究中涉及的相近术语和概念进行辨析,澄清研究对象和研究主题.然后,根据实现场所感知目标所采用的线索对已有方法进行分类介绍.主要分成3个大类:基于环境布局几何信息的方法、基于环境布局视觉信息的方法、基于用户指导信息的方法,其中各类又根据所用信息特点细分为若干子类.除此之外,将一些特殊研究方法单独归类进行补充说明.阐述各类别方法对场所感知问题的解决思路和工作原理,并指出各种方法特点和局限性.最后,分析了该领域存在的主要问题,并对未来研究方向进行了讨论和展望.
    1)  本文责任编委 徐昕
  • 图  1  场所感知方法分类

    Fig.  1  The category of place perception methods

    图  2  位于房间、门口、走廊时距离传感器数据实例[17]

    Fig.  2  The data instance of range sensors in room, doorway, and corridor[17]

    图  3  3D空间特征向量构成基础[32]

    Fig.  3  The construction base of 3D space feature vector[32]

    图  4  厨房全景图像 (上图为深度图, 下图为反射图)[51]

    Fig.  4  The panoramic images of a kitchen (depth image above and reflectance image below)[51]

    图  5  检测出特征点的3D图像[54]

    Fig.  5  The 3D image including the detected feature points[54]

    图  6  基于NBC的场所感知效果[73]

    Fig.  6  The place perception effect based on NBC[73]

    图  7  人机交互获取环境知识[85]

    Fig.  7  Surrounding knowledge obtaining based on human-robot interaction[85]

    图  8  根据运动传感器推理家具类型[88]

    Fig.  8  Furniture type inferring based on motion sensors[88]

  • [1] Cognitive Systems for Cognitive Assistants-CoSy[Online], available:http://www.cognitivesystems.org/, January 1, 2016
    [2] COGNIRON The Cognitive Robot Companion[Online], available:http://www.cogniron.org, January 1, 2016
    [3] CogX:Cognitive Systems that Self-Understand and Self-Extend[Online], available:http://www.cs.bham.ac.uk/research/groupings/robotics/projects/cogx, January 1, 2016
    [4] Kostavelis I, Gasteratos A. Semantic mapping for mobile robotics tasks:a survey. Robotics and Autonomous Systems, 2015, 66:86-103 doi: 10.1016/j.robot.2014.12.006
    [5] 李学龙, 史建华, 董永生, 陶大程.场景图像分类技术综述.中国科学:信息科学, 2015, 45(7):827-848 http://www.cnki.com.cn/Article/CJFDTOTAL-PZKX201507001.htm

    Li Xue-Long, Shi Jian-Hua, Dong Yong-Sheng, Tao Da-Cheng. A survey on scene image classification. Scientia Sinica:Informationis, 2015, 45(7):827-848 http://www.cnki.com.cn/Article/CJFDTOTAL-PZKX201507001.htm
    [6] 顾广华, 韩晰瑛, 陈春霞, 赵耀.图像场景语义分类研究进展综述.系统工程与电子技术, 2016, 38(4):936-948 http://www.cnki.com.cn/Article/CJFDTOTAL-XTYD201604032.htm

    Gu Guang-Hua, Han Xi-Ying, Chen Chun-Xia, Zhao Yao. Survey on semantic scene classification research. Systems Engineering and Electronics, 2016, 38(4):936-948 http://www.cnki.com.cn/Article/CJFDTOTAL-XTYD201604032.htm
    [7] 庄严, 陈东, 王伟, 韩建达, 王越超.移动机器人基于视觉室外自然场景理解的研究与进展.自动化学报, 2010, 36(1):1-11 http://www.aas.net.cn/CN/abstract/abstract13622.shtml

    Zhuang Yan, Chen Dong, Wang Wei, Han Jian-Da, Wang Yue-Chao. Status and development of natural scene understanding for vision-based outdoor moblie robot. Acta Automatica Sinica, 2010, 36(1):1-11 http://www.aas.net.cn/CN/abstract/abstract13622.shtml
    [8] Henderson J M, Hollingworth A. High-level scene perception. Annual review of psychology, 1999, 50(1):243-271 doi: 10.1146/annurev.psych.50.1.243
    [9] Pronobis A, Sjoo K, Aydemir A, Bishop A N, Jensfelt P. A framework for robust cognitive spatial mapping. In:Proceedings of the 14th International Conference on Advanced Robotics. Munich, Germany:IEEE, 2009. 1-8
    [10] Vasudevan S. Spatial Cognition for Mobile Robots:A Hierarchical Probabilistic Concept-oriented Representation of Space[Ph.D. dissertation], Swiss Federal Institute of Technology in Zurich, 2008.
    [11] Pronobis A, Jensfelt P. Hierarchical multi-modal place categorization. In:Proceedings of the 5th European Conference on Mobile Robots. Örebro, Sweden:ECMR, 2011. 159-164
    [12] Swadzba A, Wachsmuth S. Indoor scene classification using combined 3D and gist features. In:Proceedings of the 10th Asian Conference on Computer Vision. Queenstown, New Zealand:Springer, 2010. 201-215
    [13] Fazl-Ersi E, Tsotsos J K. Histogram of oriented uniform patterns for robust place recognition and categorization. The International Journal of Robotics Research, 2012, 31(4):468-483 doi: 10.1177/0278364911434936
    [14] Pronobis A. Semantic mapping with mobile robots[Ph.D. dissertation], KTH Royal Institute of Technology, Sweden, 2011.
    [15] Ulrich I, Nourbakhsh I. Appearance-based place recognition for topological localization. In:Proceedings of the 2000 IEEE International Conference on Robotics and Automation. San Francisco, CA, USA:IEEE, 2000. 1023-1029
    [16] Laumond J P. Model structuring and concept recognition:two aspects of learning for a mobile robot. In:Proceedings of the 8th International Joint Conference on Artificial Intelligence. Karlsruhe, West Germany:Morgan Kaufmann Publishers Inc., 1983. 839-841
    [17] Mozos O M, Stachniss C, Burgard W. Supervised learning of places from range data using AdaBoost. In:Proceedings of the 2005 IEEE International Conference on Robotics and Automation. Barcelona, Spain:IEEE, 2005. 1742-1747
    [18] Mozos O M, Burgard W. Supervised learning of topological maps using semantic information extracted from range data. In:Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. Beijing, China:IEEE, 2006. 2772-2777
    [19] Shi L, Kodagoda S, Dissanayake G. Multi-class classification for semantic labeling of places. In:Proceedings of the 11th International Conference on Control Automation, Robotics and Vision. Singapore:IEEE, 2010. 2307-2312
    [20] Shi L, Kodagoda S, Dissanayake G. Application of semi-supervised learning with voronoi graph for place classification. In:Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. Vilamoura-Algarve, Portugal:IEEE, 2012. 2991-2996
    [21] Sousa P, Araiijo R, Nunes U. Real-time labeling of places using support vector machines. In:Proceedings of the 2007 IEEE International Symposium on Industrial Electronics. Vigo, Spain:IEEE, 2007. 2022-2027
    [22] Premebida C, Faria D R, Souza F A, Nunes U. Applying probabilistic mixture models to semantic place classification in mobile robotics. In:Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems. Hamburg, Germany:IEEE, 2015. 4265-4270
    [23] Uršič P, Leonardis A, Skočaj D, Kristan M. Laser range data based room categorization using a compositional hierarchical model of space. In:Proceedings of the 2015 Austrian Robotics Workshop. Klagenfurt, Austria, 2015. 30-31
    [24] Soares S G, Araújo R. Semantic place labeling using a probabilistic decision list of AdaBoost classifiers. International Journal of Computer Information Systems and Industrial Management Applications, 2014, 6:548-559
    [25] Kaleci B, Šenler C M, Dutağaci H, Parlaktuna O. A probabilistic approach for semantic classification using laser range data in indoor environments. In:Proceedings of the 2015 International Conference on Advanced Robotics. Istanbul, Turkey:IEEE, 2015. 375-381
    [26] Friedman S, Pasula H, Fox D. Voronoi random fields:extracting the topological structure of indoor environments via place labeling. In:Proceedings of the 20th International Joint Conference on Artificial Intelligence. Hyderabad, India:Morgan Kaufmann Publishers Inc., 2007. 2109-2114
    [27] Luperto M, Li A Q, Amigoni F. A system for building semantic maps of indoor environments exploiting the concept of building typology. In:Proceedings of the 17th Annual RoboCup International Symposium. Eindhoven, Holland:Springer, 2013. 504-515
    [28] Liao Y Y, Kodagoda S, Wang Y, Shi L, Liu Y. Place classification with a graph regularized deep neural network model. IEEE Transactions on Cognitive and Developmental Systems, to be published.
    [29] Buschka P, Saffiotti A. A virtual sensor for room detection. In:Proceedings of the 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems. Lausanne, Switzerland:IEEE, 2002. 637-642
    [30] Liu Z Y, von Wichert G. Extracting semantic indoor maps from occupancy grids. Robotics and Autonomous Systems, 2014, 62(5):663-674 doi: 10.1016/j.robot.2012.10.004
    [31] Hellbach S, Himstedt M, Bahrmann F, Riedel M, Villmann T, Böhme H J. Some room for GLVQ:semantic labeling of occupancy grid maps. In:Proceedings of the 10th International Workshop on Self-Organizing Maps. Mittweida, Germany:Springer, 2014. 133-143
    [32] Swadzba A, Wachsmuth S. A detailed analysis of a new 3D spatial feature vector for indoor scene classification. Robotics and Autonomous Systems, 2014, 62(5):646-662 doi: 10.1016/j.robot.2012.10.006
    [33] Torralba A, Murphy K P, Freeman W T, Rubin M A. Context-based vision system for place and object recognition. In:Proceedings of the 2003 IEEE International Conference on Computer Vision. Nice, France:IEEE, 2003. 273-280
    [34] Quattoni A, Torralba A. Recognizing indoor scenes. In:Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, Florida, USA:IEEE, 2009. 413-420
    [35] Madokoro H, Utsumi Y, Sato K. Scene classification using unsupervised neural networks for mobile robot vision. In:Proceedings of the 2012 SICE Annual Conference. Akita, Japan:IEEE, 2012. 1568-1573
    [36] Wu J, Rehg J M. Where am Ⅰ:place instance and category recognition using spatial PACT. In:Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, Alaska, USA:IEEE, 2008. 1-8
    [37] Pronobis A, Caputo B, Jensfelt P, Christensen H. A discriminative approach to robust visual place recognition. In:Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. Beijing, China:IEEE, 2006. 3829-3836
    [38] Pronobis A, Caputo B. Confidence-based cue integration for visual place recognition. In:Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems. San Diego, California, USA:IEEE, 2007. 2394-2401
    [39] Luo J, Pronobis A, Caputo B, Jensfelt P. Incremental learning for place recognition in dynamic environments. In:Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems. San Diego, California, USA:IEEE, 2007. 721-728
    [40] Pronobis A, Mozos O M, Caputo B, Jensfelt P. Multi-modal semantic place classification. The International Journal of Robotics Research, 2010, 29(2-3):298-320 doi: 10.1177/0278364909356483
    [41] Li F F, Pietro P. A Bayesian hierarchical model for learning natural scene categories. In:Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA:IEEE, 2005. 524-531
    [42] Ranganathan A. Pliss:Detecting and labeling places using online change-point detection. In:Proceedings of the 2010 Robotics:Science and Systems. Zaragoza, Spain:MIT Press, 2010. 185-192
    [43] Wu J X, Christensen H I, Rehg J M. Visual place categorization:problem, dataset, and algorithm. In:Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. St. Louis, MO, USA:IEEE, 2009. 4763-4770
    [44] Mozos O M, Mizutani H, Kurazume R, Hasegawa T. Categorization of indoor places using the Kinect sensor. Sensors, 2012, 12(5):6695-6711 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.396.5663
    [45] Choi W, Chao Y W, Pantofaru C, Savarese S. Understanding indoor scenes using 3D geometric phrases. In:Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, OR, USA:IEEE, 2013. 33-40
    [46] Costante G, Ciarfuglia T A, Valigi P, Ricci E. A transfer learning approach for multi-cue semantic place recognition. In:Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. Tokyo, Japan:IEEE, 2013. 2122-2129
    [47] Ali S Y, Marhaban M H, Ahmad S A, Ramli A R. Improved SIFT algorithm for place categorization. In:Proceedings of the 10th Asian Control Conference. Kota Kinabalu, Malaysia:IEEE, 2015. 1-3
    [48] Sünderhauf N, Dayoub F, McMahon S, Talbot B, Schulz R, Corke P, Wyeth G, Upcroft B, Milford M. Place categorization and semantic mapping on a mobile robot[Online], available:http://arxiv.org/abs/1507.02428, January 1, 2016
    [49] Carrillo H, Latif Y, Neira J, Castellanos J A. Place categorization using sparse and redundant representations. In:Proceedings of the 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. Chicago, Illinois, USA:IEEE, 2014. 4950-4957
    [50] Kostavelis I, Charalampous K, Gasteratos A, Tsotsos J K. Robot navigation via spatial and temporal coherent semantic maps. Engineering Applications of Artificial Intelligence, 2016, 48:173-187 doi: 10.1016/j.engappai.2015.11.004
    [51] Jung H, Mozos O M, Iwashita Y, Kurazume R. Local N-ary Patterns:a local multi-modal descriptor for place categorization. Advanced Robotics, 2016, 30(6):402-415 doi: 10.1080/01691864.2015.1120242
    [52] Jung H, Mozos O M, Iwashita Y, Kurazume R. Indoor place categorization using co-occurrences of LBPs in gray and depth images from RGB-D sensors. In:Proceedings of the 5th International Conference on Emerging Security Technologies. Alcala de Henares, Spain:IEEE, 2014. 40-45
    [53] 牛杰, 卜雄洙, 钱堃, 李众.一种融合全局及显著性区域特征的室内场景识别方法.机器人, 2015, 37(1):122-128 http://www.cnki.com.cn/Article/CJFDTOTAL-JQRR201501014.htm

    Niu Jie, Bu Xiong-Zhu, Qian Kun, Li Zhong. An indoor scene recognition method combining global and saliency region features. Robot, 2015, 37(1):122-128 http://www.cnki.com.cn/Article/CJFDTOTAL-JQRR201501014.htm
    [54] Romero-González C, Martínez-Gómez J, García-Varea I, Rodríguez-Ruiz L. 3D spatial pyramid:descriptors generation from point clouds for indoor scene classification. Machine Vision and Applications, 2016, 27(2):263-273 doi: 10.1007/s00138-015-0744-4
    [55] Martínez-Gómez J, Morell V, Cazorla M, García-Varea I. Semantic localization in the PCL library. Robotics and Autonomous Systems, 2016, 75:641-648 doi: 10.1016/j.robot.2015.09.006
    [56] Zivkovic Z, Booij O, Kröse B. From images to rooms. Robotics and Autonomous Systems, 2007, 55(5):411-418 doi: 10.1016/j.robot.2006.12.005
    [57] 吴皓, 田国会, 陈西博, 张涛涛, 周风余.基于机器人服务任务导向的室内未知环境地图构建.机器人, 2010, 32(2):196-203 http://www.cnki.com.cn/Article/CJFDTOTAL-JQRR201002009.htm

    Wu Hao, Tian Guo-Hui, Chen Xi-Bo, Zhang Tao-Tao, Zhou Feng-Yu. Map building of indoor unknown environment based on robot service mission direction. Robot, 2010, 32(2):196-203 http://www.cnki.com.cn/Article/CJFDTOTAL-JQRR201002009.htm
    [58] Rituerto A, Murillo A C, Guerrero J J. Semantic labeling for indoor topological mapping using a wearable catadioptric system. Robotics and Autonomous Systems, 2014, 62(5):685-695 doi: 10.1016/j.robot.2012.10.002
    [59] Zender H, Mozos O M, Jensfelt P, Kruijff G J M, Burgard W. Conceptual spatial representations for indoor mobile robots. Robotics and Autonomous Systems, 2008, 56(6):493-502 doi: 10.1016/j.robot.2008.03.007
    [60] Mozos Ó M, Triebel R, Jensfelt P, Rottmann A, Burgard W. Supervised semantic labeling of places using information extracted from sensor data. Robotics and Autonomous Systems, 2007, 55(5):391-402 doi: 10.1016/j.robot.2006.12.003
    [61] Rottmann A, Mozos O M, Stachniss C, Burgard W. Semantic place classification of indoor environments with mobile robots using boosting. In:Proceedings of the 20th National Conference on Artificial Intelligence. Pittsburgh, Pennsylvania, USA:AAAI, 2005. 1306-1311
    [62] Galindo C, Saffiotti A, Coradeschi S, Buschka P, Fernández-Madrigal J A, Gonzalez J. Multi-hierarchical semantic maps for mobile robotics. In:Proceedings of the 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems. Edmonton, Alberta, Canada:IEEE, 2005. 2278-2283
    [63] Rogers J G, Christensen H I. A conditional random field model for place and object classification. In:Proceedings of the 2012 IEEE International Conference on Robotics and Automation. St. Paul, Minnesota, USA:IEEE, 2012. 1766-1772
    [64] Viswanathan P, Meger D, Southey T, Little J J, Mackworth A. Automated spatial-semantic modeling with applications to place labeling and informed search. In:Proceedings of the 6th Canadian Conference on Computer and Robot Vision. Kelowna, British Columbia, Canada:IEEE, 2009. 284-291
    [65] Viswanathan P, Southey T, Little J, Mackworth A. Automated place classification using object detection. In:Proceedings of the 7th Canadian Conference on Computer and Robot Vision. Ottawa, Ontario, Canada:IEEE, 2010. 324-330
    [66] Viswanathan P, Southey T, Little J, Mackworth A. Place classification using visual object categorization and global information. In:Proceedings of the 8th Canadian Conference on Computer and Robot Vision. St. Johns, Newfoundland, Canada:IEEE, 2011. 1-7
    [67] Espinace P, Kollar T, Soto A, Roy N. Indoor scene recognition through object detection. In:Proceedings of the 2010 IEEE International Conference on Robotics and Automation. Anchorage, Alaska, USA:IEEE, 2010. 1406-1413
    [68] Espinace P, Kollar T, Roy N, Soto A. Indoor scene recognition by a mobile robot through adaptive object detection. Robotics and Autonomous Systems, 2013, 61(9):932-947 doi: 10.1016/j.robot.2013.05.002
    [69] Charalampous K, Kostavelis I, Chantzakou F E, Volanis E S, Emmanouilidis C, Tsalides P, Gasteratos A. Place categorization through object classification. In:Proceedings of the 2014 IEEE International Conference on Imaging Systems and Techniques. Santorini, Greece:IEEE, 2014. 320-324
    [70] Kostavelis I, Amanatiadis A, Gasteratos A. How do you help a robot to find a place? A supervised learning paradigm to semantically infer about places. HAIS 2013:Hybrid Artificial Intelligent Systems. Berlin:Springer, 2013, 8073:324-333
    [71] Ranganathan A, Dellaert F. Semantic modeling of places using objects. In:Proceedings of the 2007 Robotics:Science and Systems Conference. Atlanta, Georgia, USA:MIT Press, 2007.
    [72] Vasudevan S, Gächter S, Nguyen V, Siegwart R. Cognitive maps for mobile robots——an object based approach. Robotics and Autonomous Systems, 2007, 55(5):359-371 doi: 10.1016/j.robot.2006.12.008
    [73] Vasudevan S, Siegwart R. Bayesian space conceptualization and place classification for semantic maps in mobile robotics. Robotics and Autonomous Systems, 2008, 56(6):522-537 doi: 10.1016/j.robot.2008.03.005
    [74] Vasudevan S, Siegwart R. A Bayesian conceptualization of space for mobile robots. In:Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems. San Diego, California, USA:IEEE, 2007. 715-720
    [75] Klenk M, Hawes N, Lockwood K. Representing and reasoning about spatial regions defined by context. In:Proceedings of the 2011 AAAI Fall Symposium. El Segundo, CA, USA:AAAI, 2011. 154-161
    [76] Hawes N, Klenk M, Lockwood K, Horn G S, Kellecher J D. Towards a cognitive system that can recognize spatial regions based on context. In:Proceedings of the 26th AAAI Conference on Artificial Intelligence. Toronto, Ontario, Canada:AAAI, 2012. 200-206
    [77] Ruiz-Sarmiento J R, Galindo C, González-Jiménez J. Joint categorization of objects and rooms for mobile robots. In:Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems. Hamburg, Germany:IEEE, 2015. 2523-2528
    [78] Chen Y X, Pan D R, Pan Y F, Liu S Z, Gu A H, Wang M. Indoor scene understanding via monocular RGB-D images. Information Sciences, 2015, 320:361-371 doi: 10.1016/j.ins.2015.03.023
    [79] Booij O, Kröse B, Peltason J, Spexard T, Hanheide M. Moving from augmented to interactive mapping. In:Proceedings of the Interactive Robot Learning-Robotics:Science and Systems 2008 Workshop. Zurich, Switzerland:IRL, 2008.
    [80] Spexard T, Li S Y, Wrede B, Fritsch J, Sagerer G, Booij O, Zivkovic Z, Terwijn B, Krose B. BIRON, where are you? Enabling a robot to learn new places in a real home environment by integrating spoken dialog and visual localization. In:Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. Beijing, China:IEEE, 2006. 934-940
    [81] Diosi A, Taylor G, Kleeman L. Interactive SLAM using laser and advanced sonar. In:Proceedings of the 2005 IEEE International Conference on Robotics and Automation. Barcelona, Spain:IEEE, 2005. 1103-1108
    [82] Milford M, Schulz R, Prasser D, Wyeth G, Wiles J. Learning spatial concepts from RatSLAM representations. Robotics and Autonomous Systems, 2007, 55(5):403-410 doi: 10.1016/j.robot.2006.12.006
    [83] Nieto-Granda C, Rogers J G, Trevor A J B, Christensen H I. Semantic map partitioning in indoor environments using regional analysis. In:Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems. Taipei, China:IEEE, 2010. 1451-1456
    [84] Topp E A, Huettenrauch H, Christensen H I, Eklundh K S. Bringing together human and robotic environment representations-a pilot study. In:Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. Beijing, China:IEEE, 2006. 4946-4952
    [85] Gemignani G, Capobianco R, Bastianelli E, Bloisi D D, Iocchi L, Nardi D. Living with robots:interactive environmental knowledge acquisition. Robotics and Autonomous Systems, 2016, 78:1-16 doi: 10.1016/j.robot.2015.11.001
    [86] Gemignani G, Nardi D, Bloisi D D, Capobianco R, Iocchi L. Interactive semantic mapping:experimental evaluation. In:Proceedings of the 14th International Symposium on Experimental Robotics. Tokyo, Japan:Springer, 2016. 339-355
    [87] Luperto M, Amigoni F. Exploiting structural properties of buildings towards general semantic mapping systems. In:Proceedings of the 13th International Conference on Intelligent Autonomous Systems. Padova, Italy:Springer, 2016. 375-387
    [88] Sheng W H, Du J H, Cheng Q, Li G, Zhu C, Liu M Q, Xu G Q. Robot semantic mapping through human activity recognition:a wearable sensing and computing approach. Robotics and Autonomous Systems, 2015, 68:47-58 doi: 10.1016/j.robot.2015.02.002
    [89] Martinez-Gomez J, Garcia-Varea I, Caputo B. Baseline multimodal place classifier for the 2012 robot vision task. In:Proceedings of the 2012 Conference and Labs of the Evaluation Forum. Rome, Italy:Springer, 2012. 1-10
    [90] 朱博, 戴先中, 李新德.基于"原型"的机器人开放式室内场所感知算法.模式识别与人工智能, 2012, 25(1):1-10 http://www.cnki.com.cn/Article/CJFDTOTAL-MSSB201201002.htm

    Zhu Bo, Dai Xian-Zhong, Li Xin-De. Open interior-places perception algorithm of robot based on prototype. Pattern Recognition and Artificial Intelligence, 2012, 25(1):1-10 http://www.cnki.com.cn/Article/CJFDTOTAL-MSSB201201002.htm
    [91] 朱博, 戴先中, 李新德, 杨伟, 陈芳园.基于"原型"的机器人开放式室内场所感知实验研究.机器人, 2013, 35(4):491-499, 512 http://www.cnki.com.cn/Article/CJFDTOTAL-JQRR201304015.htm

    Zhu Bo, Dai Xian-Zhong, Li Xin-De, Yang Wei, Chen Fang-Yuan. Experimental study on open interior-places perception of robot based on "prototype". Robot, 2013, 35(4):491-499, 512 http://www.cnki.com.cn/Article/CJFDTOTAL-JQRR201304015.htm
    [92] Mozos O M, Jensfelt P, Zender H, Kruijff G M, Burgard W. From labels to semantics:An integrated system for conceptual spatial representations of indoor environments for mobile robots. In:Proceedings of the ICRA-07 Workshop on Semantic Information in Robotics. Rome, Italy:IEEE, 2007. 33-40
    [93] 庄严, 卢希彬, 李云辉, 王伟.移动机器人基于三维激光测距的室内场景认知.自动化学报, 2011, 37(10):1232-1240 http://www.aas.net.cn/CN/abstract/abstract17612.shtml

    Zhuang Yan, Lu Xi-Bin, Li Yun-Hui, Wang Wei. Mobile robot indoor scene cognition using 3D laser scanning. Acta Automatica Sinica, 2011, 37(10):1232-1240 http://www.aas.net.cn/CN/abstract/abstract17612.shtml
    [94] Goertzel B, Lian R T, Arel I, de Garis H, Chen S. A world survey of artificial brain projects, Part Ⅱ:biologically inspired cognitive architectures. Neurocomputing, 2010, 74(1-3):30-49 doi: 10.1016/j.neucom.2010.08.012
    [95] 乔红, 尹沛劼, 李睿, 王鹏.机器人与神经科学交叉的意义——关于智能机器人未来发展的思考.中国科学院院刊, 2015, 30(6):762-771 http://www.cnki.com.cn/Article/CJFDTOTAL-KYYX201506007.htm

    Qiao Hong, Yin Pei-Jie, Li Rui, Wang Peng. What is the Meaning for the Interdisciplinary Research of Robot and Neuroscience? Thoughts on the future development of intelligent robots. Bulletin of Chinese Academy of Sciences, 2015, 30(6):762-771 http://www.cnki.com.cn/Article/CJFDTOTAL-KYYX201506007.htm
    [96] 曾毅, 刘成林, 谭铁牛.类脑智能研究的回顾与展望.计算机学报, 2016, 39(1):212-222 doi: 10.11897/SP.J.1016.2016.00212

    Zeng Yi, Liu Cheng-Lin, Tan Tie-Niu. Retrospect and outlook of brain-inspired intelligence research. Chinese Journal of Computers, 2016, 39(1):212-222 doi: 10.11897/SP.J.1016.2016.00212
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  • 收稿日期:  2016-04-19
  • 录用日期:  2016-11-08
  • 刊出日期:  2017-04-20

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