[1] |
柴天佑.自动化科学与技术发展方向.自动化学报, 2018, 44(11):1923-1930 doi: 10.16383/j.aas.2018.c180252
Chai Tian-You. Development directions of automation science and technology. Acta Automatica Sinica, 2018, 44(11): 1923-1930 doi: 10.16383/j.aas.2018.c180252 |
[2] |
Barfoot T, Kelly J, Sibley G. Special issue on long-term autonomy. The International Journal of Robotics Research, 2013, 32(14): 1609-1610 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=10.1177/0278364913511182 |
[3] |
Kunze L, Hawes N, Duckett T, Hanheide M, Krajník T. Artificial intelligence for long-term robot autonomy: a survey. IEEE Robotics and Automation Letters, 2018, 3(4): 4023-4030 |
[4] |
Garg S, Jacobson A, Kumar S, Milford M. Improving condition- and environment-invariant place recognition with semantic place categorization. In: Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems IROS). Vancouver, Canada: IEEE, 2017. 6863-6870 |
[5] |
Garg S, Suenderhauf N, Milford M. Don't look back: robustifying place categorization for viewpoint- and condition-invariant place recognition. In: Proceedings of the 2018 IEEE International Conference on Robotics and Automation ICRA). Brisbane, Australia: IEEE, 2018. 3645-3652 |
[6] |
Naseer T, Burgard W, Stachniss C. Robust visual localization across seasons. IEEE Transactions on Robotics, 2018, 34(2): 289-302 |
[7] |
Saarinen J P, Andreasson H, Stoyanov T, Lilienthal A J. 3D normal distributions transform occupancy maps: an efficient representation for mapping in dynamic environments. The International Journal of Robotics Research, 2013, 32(14): 1627-1644 |
[8] |
Lázaro M T, Capobianco R, Grisetti G. Efficient long-term mapping in dynamic environments. In: Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems IROS). Madrid, Spain: IEEE, 2018. 153-160 |
[9] |
Siva S, Zhang H. Omnidirectional multisensory perception fusion for long-term place recognition. In: Proceedings of the 2018 IEEE International Conference on Robotics and Automation ICRA). Brisbane, Australia: IEEE, 2018. 5175-5181 |
[10] |
Zhu J L, Ai Y F, Tian B, Cao D P, Scherer S. Visual place recognition in long-term and large-scale environment based on CNN feature. In: Proceedings of the 2018 IEEE Intelligent Vehicles Symposium IV). Changshu, China: IEEE, 2018. 1679-1685 |
[11] |
Se S, Lowe D G, Little J J. Vision-based global localization and mapping for mobile robots. IEEE Transactions on Robotics, 2005, 21(3): 364-375 |
[12] |
Pitzer B, Stiller C. Probabilistic mapping for mobile robots using spatial correlation models. In: Proceedings of the 2010 IEEE International Conference on Robotics and Automation. Anchorage, USA: IEEE, 2010. 5402-5409 |
[13] |
de la Puente P, Rodriguez-Losada D, Valero A, Matia F. 3D feature based mapping towards mobile robots' enhanced performance in rescue missions. In: Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. St. Louis, USA: IEEE, 2009. 1138-1143 |
[14] |
辛菁, 苟蛟龙, 马晓敏, 黄凯, 刘丁, 张友民.基于Kinect的移动机器人大视角3维V-SLAM.机器人, 2014, 36(5):560-568 http://d.old.wanfangdata.com.cn/Periodical/jqr201405007
Xin Jing, Gou Jiao-Long, Ma Xiao-Min, Huang Kai, Liu Ding, Zhang You-Min. A large viewing angle 3-dimensional V-SLAM algorithm with a Kinect-based mobile robot system. Robot, 2014, 36(5): 560-568 http://d.old.wanfangdata.com.cn/Periodical/jqr201405007 |
[15] |
杨鸿, 钱堃, 戴先中, 马旭东, 房芳.基于Kinect传感器的移动机器人室内环境三维地图创建.东南大学学报(自然科学版), 2013, 43(S1): 183-187 http://d.old.wanfangdata.com.cn/Periodical/dndxxb2013z1038
Yang Hong, Qian Kun, Dai Xian-Zhong, Ma Xu-Dong, Fang Fang. Kinect-based 3D indoor environment map building for mobile robot. Journal of Southeast University Natural Science Edition), 2013, 43(S1): 183-187 http://d.old.wanfangdata.com.cn/Periodical/dndxxb2013z1038 |
[16] |
丁文东, 徐德, 刘希龙, 张大朋, 陈天.移动机器人视觉里程计综述.自动化学报, 2018, 44(3): 385-400 doi: 10.16383/j.aas.2018.c170107
Ding Wen-Dong, Xu De, Liu Xi-Long, Zhang Da-Peng, Chen Tian. Review on visual odometry for mobile robots. Acta Automatica Sinica, 2018, 44(3): 385-400 doi: 10.16383/j.aas.2018.c170107 |
[17] |
Montemerlo M, Thrun S, Whittaker W. Conditional particle filters for simultaneous mobile robot localization and people-tracking. In: Proceedings of the 2002 International conference on Robotics and Automation. Washington, USA: IEEE, 2002. 695-701 |
[18] |
Wolf D F, Sukhatme G S. Mobile robot simultaneous localization and mapping in dynamic environments. Autonomous Robots, 2005, 19(1): 53-65 doi: 10.1007-s10514-005-0606-4/ |
[19] |
Montesano L, Minguez J, Montano L. Modeling dynamic scenarios for local sensor-based motion planning. Autonomous Robots, 2008, 25(3): 231-251 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=578b332117cc1fba3aa17d98a66ee403 |
[20] |
Wang C C, Thorpe C, Thrun S, Hebert M, Durrant-Whyte H. Simultaneous localization, mapping and moving object tracking. The International Journal of Robotics Research, 2007, 26(9): 889-916 http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ028484490/ |
[21] |
Henriques J F, Vedaldi A. Mapnet: an allocentric spatial memory for mapping environments. In: Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE, 2018. 8476-8484 |
[22] |
Bürki M, Dymczyk M, Gilitschenski I, Cadena C, Siegwart R, Nieto J. Map management for efficient long-term visual localization in outdoor environments. In: Proceedings of the 2018 IEEE Intelligent Vehicles Symposium IV). Changshu, China: IEEE, 2018. 682-688 |
[23] |
Tipaldi G D, Meyer-Delius D, Burgard W. Lifelong localization in changing environments. The International Journal of Robotics Research, 2013, 32(14): 1662-1678 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=10.1177/0278364913502830 |
[24] |
Meyer-Delius D, Hess J, Grisetti G, Burgard W. Temporary maps for robust localization in semi-static environments. In: Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems. Taipei, China: IEEE, 2010. 5750-5755 |
[25] |
Saarinen J, Andreasson H, Lilienthal A J. Independent Markov chain occupancy grid maps for representation of dynamic environment. In: Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. Vilamoura, Portugal: IEEE, 2012. 3489-3495 |
[26] |
Biswas J, Veloso M. Episodic non-Markov localization: reasoning about short-term and long-term features. In: Proceedings of the 2014 IEEE International Conference on Robotics and Automation ICRA). Hong Kong, China: IEEE, 2014. 3969-3974 |
[27] |
Biber P, Duckett T. Dynamic maps for long-term operation of mobile service robots. In: Proceedings of the 2005 Robotics: Science and Systems Conference. Cambridge, USA: Massachusetts Institute of Technology, 2005. 17-24 |
[28] |
Dayoub F, Cielniak G, Duckett T. Long-term experiments with an adaptive spherical view representation for navigation in changing environments. Robotics and Autonomous Systems, 2011, 59(5): 285-295 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=41aa241ec4c3d77da06633964020821e |
[29] |
Morris T, Dayoub F, Corke P, Wyeth G, Upcroft B. Multiple map hypotheses for planning and navigating in non-stationary environments. In: Proceedings of the 2014 IEEE International Conference on Robotics and Automation ICRA). Hong Kong, China: IEEE, 2014. 2765-2770 |
[30] |
Krajnik T, Fentanes J P, Cielniak G, Dondrup C, Duckett T. Spectral analysis for long-term robotic mapping. In: Proceedings of the 2014 IEEE International Conference on Robotics and Automation ICRA). Hong Kong, China: IEEE, 2014. 3706-3711 |
[31] |
Zhang J, Singh S. Low-drift and real-time lidar odometry and mapping. Autonomous Robots, 2017, 41(2): 401-416 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=96d086fad7e673afa04089e3f0bd785e |
[32] |
Wang D Z, Posner I, Newman P. What could move? Finding cars, pedestrians and bicyclists in 3D laser data. In: Proceedings of the 2012 IEEE International Conference on Robotics and Automation. Saint Paul, USA: IEEE, 2012. 4038-4044 |
[33] |
Tanzmeister G, Thomas J, Wollherr D, Buss M. Grid-based mapping and tracking in dynamic environments using a uniform evidential environment representation. In: Proceedings of the 2014 IEEE International Conference on Robotics and Automation ICRA). Hong Kong, China: IEEE, 2014. 6090-6095 |
[34] |
Ott L, Ramos F. Unsupervised online learning for long-term autonomy. The International Journal of Robotics Research, 2013, 32(14): 1724-1741 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=10.1177/0278364913505657 |
[35] |
Pomerleau F, Krüsi P, Colas F, Furgale P, Siegwart R. Long-term 3D map maintenance in dynamic environments. In: Proceedings of the 2014 IEEE International Conference on Robotics and Automation ICRA). Hong Kong, China: IEEE, 2014. 3712-3719 |
[36] |
Dymczyk M, Lynen S, Cieslewski T, Bosse M, Siegwart R, Furgale P. The gist of maps -- summarizing experience for lifelong localization. In: Proceedings of the 2015 IEEE International Conference on Robotics and Automation ICRA). Seattle, USA: IEEE, 2015. 2767-2773 |
[37] |
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 doi: 10.3724/SP.J.1004.2011.01232
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 doi: 10.3724/SP.J.1004.2011.01232 |
[38] |
闫飞, 庄严, 王伟.移动机器人基于多传感器信息融合的室外场景理解.控制理论与应用, 2011, 28(8): 1093-1098
Yan Fei, Zhuang Yan, Wang Wei. Outdoor scene comprehension of mobile robot based on multi-sensor information fusion. Control Theory & Applications, 2011, 28(8): 1093-1098 |
[39] |
余淼, 胡占义.高阶马尔科夫随机场及其在场景理解中的应用.自动化学报, 2015, 41(7): 1213-1234 doi: 10.16383/j.aas.2015.c140684
Yu Miao, Hu Zhan-Yi. Higher-order markov random fields and their applications in scene understanding. Acta Automatica Sinica, 2015, 41(7): 1213-1234 doi: 10.16383/j.aas.2015.c140684 |
[40] |
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 |
[41] |
Sun L, Yan Z, Zaganidis A, Zhao C, Duckett T. Recurrent-OctoMap: learning state-based map refinement for long-term semantic mapping with 3-D-lidar data. IEEE Robotics and Automation Letters, 2018, 3(4): 3749-3756 |
[42] |
Drouilly R, Rives P, Morisset B. Semantic representation for navigation in large-scale environments. In: Proceedings of the 2015 IEEE International Conference on Robotics and Automation ICRA). Seattle, USA: IEEE, 2015. 1106-1111 |
[43] |
Wang S, Clark R., Wen H K, Trigoni N. End-to-end, sequence-to-sequence probabilistic visual odometry through deep neural networks. The International Journal of Robotics Research, 2018, 37(4-5): 513-542 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=10.1177/0278364917734298 |
[44] |
Kendall A, Grimes M, Cipolla R. PoseNet: a convolutional network for real-time 6-dof camera relocalization. In: Proceedings of the 2015 IEEE international conference on computer vision ICCV). Santiago, Chile: IEEE, 2015. 2938-2946 |
[45] |
Lowry S, Sünderhauf N, Newman P, Leonard J J, Cox D, Corke P, et al. Visual place recognition: a survey. IEEE Transactions on Robotics, 2016, 32(1): 1-19 http://d.old.wanfangdata.com.cn/Periodical/zhlxbx200807004 |
[46] |
Kim G, Kim A. Scan context: egocentric spatial descriptor for place recognition within 3D point cloud map. In: Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems IROS). Madrid, Spain: IEEE, 2018. 4802-4809 |
[47] |
Cummins M J, Newman P M. FAB-MAP: appearance-based place recognition and mapping using a learned visual vocabulary model. In: Proceedings of the 27th International Conference on Machine Learning. Haifa, Israel: Omnipress, 2010. 3-10 |
[48] |
Galvez-López D, Tardos J D. Bags of binary words for fast place recognition in image sequences. IEEE Transactions on Robotics, 2012, 28(5): 1188-1197 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=67749a17e9b82a5f605904c28b2ebb2f |
[49] |
Churchill W, Newman P. Experience-based navigation for long-term localisation. The International Journal of Robotics Research, 2013, 32(14): 1645-1661 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=10.1177/0278364913499193 |
[50] |
Milford M J, Wyeth G F. SeqSLAM: visual route-based navigation for sunny summer days and stormy winter nights. In: Proceedings of the 2012 IEEE International Conference on Robotics and Automation. Saint Paul, USA: IEEE, 2012. 1643-1649 |
[51] |
Tang L, Wang Y, Ding X Q, Yin H, Xiong R, Huang S D. Topological local-metric framework for mobile robots navigation: a long term perspective. Autonomous Robots, 2019, 43(1): 197-211 |
[52] |
Rublee E, Rabaud V, Konolige K, Bradski G. ORB: an efficient alternative to SIFT or SURF. In: Proceedings of the 2011 IEEE International Conference on Computer Vision. Barcelona, Spain: IEEE, 2011. 2564-2571 |
[53] |
Milford M, Lowry S, Sunderhauf N, Shirazi S, Pepperell E, Upcroft B, et al. Sequence searching with deep-learnt depth for condition- and viewpoint-invariant route-based place recognition. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops CVPRW). Boston, USA: IEEE, 2015. 18-25 |
[54] |
McManus C, Churchill W, Maddern W, Stewart A D, Newman P. Shady dealings: robust, long-term visual localisation using illumination invariance. In: Proceedings of the 2014 IEEE International Conference on Robotics and Automation ICRA). Hong Kong, China: IEEE, 2014. 901-906 |
[55] |
Ratnasingam S, McGinnity T M. Chromaticity space for illuminant invariant recognition. IEEE Transactions on Image Processing, 2012, 21(8): 3612-3623 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=79cf7cfaef24ccac564aee125992328e |
[56] |
Arroyo R, Alcantarilla P F, Bergasa L M, Romera E. Are you ABLE to perform a life-long visual topological localization. Autonomous Robots, 2018, 42(3): 665-685 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=8f3c8ff51defba77bc2d7c0ccc9a5df8 |
[57] |
Neubert P, Sünderhauf N, Protzel P. Appearance change prediction for long-term navigation across seasons. In: Proceedings of the 2013 European Conference on Mobile Robots. Barcelona, Spain: IEEE, 2013. 198-203 |
[58] |
Cummins M, Newman P M. Appearance-only SLAM at large scale with FAB-MAP 2.0. The International Journal of Robotics Research, 2011, 30(9): 1100-1123 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=10.1177/0278364910385483 |
[59] |
Sünderhauf N, Protzel P. BRIEF-Gist - closing the loop by simple means. In: Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems. San Francisco, USA: IEEE, 2011. 1234-1241 |
[60] |
Johns E, Yang G Z. Dynamic scene models for incremental, long-term, appearance-based localisation. In: Proceedings of the 2013 IEEE International Conference on Robotics and Automation. Karlsruhe, Germany: IEEE, 2013. 2731-2736 |
[61] |
Han F, Wang H, Huang G Q, Zhang H. Sequence-based sparse optimization methods for long-term loop closure detection in visual SLAM. Autonomous Robots, 2018, 42(7): 1323-1335 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=7b0e7ff1905f0527bf750d954e7776ae |
[62] |
McManus C, Upcroft B, Newman P. Learning place-dependant features for long-term vision-based localisation. Autonomous Robots, 2015, 39(3): 363-387 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=31f1d3f30d5eb5242d977cf0617c6e41 |
[63] |
Linegar C, Churchill W, Newman P. Made to measure: bespoke landmarks for 24-hour, all-weather localisation with a camera. In: Proceedings of the 2016 IEEE International Conference on Robotics and Automation ICRA). Stockholm, Sweden: IEEE, 2016. 787-794 |
[64] |
张慧, 王坤峰, 王飞跃.深度学习在目标视觉检测中的应用进展与展望.自动化学报, 2017, 43(8): 1289-1305 doi: 10.16383/j.aas.2017.c160822
Zhang Hui, Wang Kun-Feng, Wang Fei-Yue. Advances and perspectives on applications of deep learning in visual object detection. Acta Automatica Sinica, 2017, 43(8): 1289-1305 doi: 10.16383/j.aas.2017.c160822 |
[65] |
Chen L C, Papandreou G, Kokkinos I, Murphy K, Yuille A L. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834-848 http://d.old.wanfangdata.com.cn/Periodical/zgtxtxxb-a201911017 |
[66] |
Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4): 640-651 http://d.old.wanfangdata.com.cn/Periodical/nygcxb201918019 |
[67] |
Arroyo R, Alcantarilla P F, Bergasa L M, Romera E. Fusion and binarization of CNN features for robust topological localization across seasons. In: Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems IROS). Daejeon, South Korea: IEEE, 2016. 4656-4663 |
[68] |
Hou Y, Zhang H, Zhou S L. BoCNF: efficient image matching with Bag of ConvNet features for scalable and robust visual place recognition. Autonomous Robots, 2018, 42(6): 1169-1185 |
[69] |
Sünderhauf N, Shirazi S, Dayoub F, Upcroft B, Milford M. On the performance of ConvNet features for place recognition. In: Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems IROS). Hamburg, Germany: IEEE, 2015. 4297-4304 |
[70] |
Bosse M, Zlot R. Place recognition using keypoint voting in large 3D lidar datasets. In: Proceedings of the 2013 IEEE International Conference on Robotics and Automation. Karlsruhe, Germany: IEEE, 2013. 2677-2684 |
[71] |
Magnusson M, Andreasson H, Nuchter A, Lilienthal A J. Appearance-based loop detection from 3D laser data using the normal distributions transform. In: Proceedings of the 2009 IEEE International Conference on Robotics and Automation. Kobe, Japan: IEEE, 2009. 23-28 |
[72] |
Zhuang Y, Jiang N, Hu H S, Yan F. 3-D-laser-based scene measurement and place recognition for mobile robots in dynamic indoor environments. IEEE Transactions on Instrumentation and Measurement, 2013, 62(2): 438-450 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=d0e502be8383632ba1735f10900c3417 |
[73] |
Cao F K, Zhuang Y, Zhang H, Wang W. Robust place recognition and loop closing in laser-based SLAM for UGVs in urban environments. IEEE Sensors Journal, 2018, 18(10): 4242-4252 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=9075705a40bec6d3065347a2892bc30b |
[74] |
Kim G, Park B, Kim A. 1-day learning, 1-year localization: long-term LiDAR localization using scan context image. IEEE Robotics and Automation Letters, 2019, 4(2): 1948-1955 |
[75] |
Dubé R, Dugas D, Stumm E, Nieto J, Siegwart R, Cadena C. SegMatch: segment based place recognition in 3D point clouds. In: Proceedings of the 2017 IEEE International Conference on Robotics and Automation ICRA). Singapore, Singapore: IEEE, 2017. 5266-5272 |
[76] |
He L, Wang X L, Zhang H. M2DP: a novel 3D point cloud descriptor and its application in loop closure detection. In: Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems. Daejeon, South Korea: IEEE, 2016. 231-237 |
[77] |
Maddern W, Pascoe G, Newman P. Leveraging experience for large-scale LIDAR localisation in changing cities. In: Proceedings of the 2015 IEEE International Conference on Robotics and Automation ICRA). Seattle, USA: IEEE, 2015. 1684-1691 |
[78] |
Latif Y, Cadena C, Neira J. Robust loop closing over time for pose graph SLAM. The International Journal of Robotics Research, 2013, 32(14): 1611-1626 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=10.1177/0278364913498910 |
[79] |
Uy M A, Lee G H. PointNetVLAD: deep point cloud based retrieval for large-scale place recognition. In: Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: 2018. 4470-4479 |
[80] |
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 doi: 10.3724/SP.J.1004.2010.00001
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 doi: 10.3724/SP.J.1004.2010.00001 |
[81] |
Li L J, Socher R, Li F F. Towards total scene understanding: classification, annotation and segmentation in an automatic framework. In: Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA: IEEE, 2009. 2036-2043 |
[82] |
Kumar M P, Koller D. Efficiently selecting regions for scene understanding. In: Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, USA: IEEE, 2010. 3217-3224 |
[83] |
Kim B S, Kohli P, Savarese S. 3D scene understanding by voxel-CRF. In: Proceedings of the 2013 IEEE International Conference on Computer Vision. Sydney, Australia: IEEE, 2013. 1425-1432 |
[84] |
Su H, Maji S, Kalogerakis E, Learned-Miller E. Multi-view convolutional neural networks for 3D shape recognition. In: Proceedings of the 2015 IEEE International Conference on Computer Vision ICCV). Santiago, Chile: IEEE, 2015. 945-953 |
[85] |
Zhuang Y, Lin X Q, Hu H S, Guo G. Using scale coordination and semantic information for robust 3-D object recognition by a service robot. IEEE Sensors Journal, 2015, 15(1): 37-47 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=d0235780d49ee238dbfe97d36ae136b4 |
[86] |
Eitel A, Springenberg J T, Spinello L, Riedmiller M, Burgard W. Multimodal deep learning for robust RGB-D object recognition. In: Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems. Hamburg, Germany: IEEE, 2015. 681-687 |
[87] |
Wang A R, Lu J W, Cai J F, Cham T J, Wang G. Large-margin multi-modal deep learning for RGB-D object recognition. IEEE Transactions on Multimedia, 2015, 17(11): 1887-1898 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=71e349bf5320c3caac8bb60f7142f0ee |
[88] |
Zhang X S, Zhuang Y, Wang W, Pedrycz W. Transfer boosting with synthetic instances for class imbalanced object recognition. IEEE Transactions on Cybernetics, 2018, 48(1): 357-370 |
[89] |
Zhang X S, Zhuang Y, Hu H S, Wang W. 3-D laser-based multiclass and multiview object detection in cluttered indoor scenes. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(1): 177-190 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=6a862dbd69f8c258307137e739b878d0 |
[90] |
Zhang X S, Zhuang Y, Wei W, Pedrycz W. Online feature transformation learning for cross-domain object category recognition. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(7): 2857-2871 |
[91] |
Zhuang Y, Liu Y S, He G J, Wang W. Contextual classification of 3D laser points with conditional random fields in urban environments. In: Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems IROS). Hamburg, Germany: IEEE, 2015. 3908-3913 |
[92] |
Krešo I, Čaušević D, Krapac J, šegvić S. Convolutional scale invariance for semantic segmentation. In: Proceedings of the 38th German Conference on Pattern Recognition. Hannover, Germany: Springer, 2016. 64-75 |
[93] |
Ansari M D, KraußS, Wasenmüller O, Stricker D. ScaleNet: scale invariant network for semantic segmentation in urban driving scenes. In: Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. Funchal, Madeira, Portugal: Scitepress, 2018. 399-404 |
[94] |
Kim D K, Maturana D, Uenoyama M, Scherer S. Season-invariant semantic segmentation with a deep multimodal network. Field and Service Robotics. Cham, Germany: Springer, 2018. 255-270 |
[95] |
熊丹, 卢惠民, 肖军浩, 郑志强.具有尺度和旋转适应性的长时间目标跟踪.自动化学报, 2019, 45(2): 289-304 doi: 10.16383/j.aas.2018.c170359
Xiong Dan, Lu Hui-Min, Xiao Jun-Hao, Zheng Zhi-Qiang. Robust long-term object tracking with adaptive scale and rotation estimation. Acta Automatica Sinica, 2019, 45(2): 289-304 doi: 10.16383/j.aas.2018.c170359 |
[96] |
Bansal A, Badino H, Huber D. Understanding how camera configuration and environmental conditions affect appearance-based localization. In: Proceedings of the 2014 IEEE Intelligent Vehicles Symposium. Dearborn, USA: IEEE, 2014. 800-807 |
[97] |
Maddern W, Pascoe G, Linegar C, Newman P. 1 year, 1000 km: The Oxford RobotCar dataset. The International Journal of Robotics Research, 2017, 36(1): 3-15 |
[98] |
Carlevaris-Bianco N, Ushani A K, Eustice R M. University of Michigan North Campus long-term vision and lidar dataset. The International Journal of Robotics Research, 2016, 35(9): 1023-1035 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=10.1177/0278364915614638 |
[99] |
Liu Y S, Wang F, Dobaie A M, He G J, Zhuang Y. Comparison of 2D image models in segmentation performance for 3D laser point clouds. Neurocomputing, 2017, 251: 136-144 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=30709a9943019170c59af0c88fa64f27 |
[100] |
Dosovitskiy A, Ros G, Codevilla F, Lopez A, Koltun V. CARLA: an open urban driving simulator. In: Proceedings of the 1st Annual Conference on Robot Learning. Mountain View, United States: PMLR, 2017. |
[101] |
Quiter C, Ernst M. deepdrive/deepdrive: 2.0 [Online]. available: https://doi.org/10.5281/zenodo.1248998, March 26, 2018. |