基于残差网络的图像序列闭环检测 下载: 1197次
占浩, 朱振才, 张永合, 郭明, 丁国鹏. 基于残差网络的图像序列闭环检测[J]. 激光与光电子学进展, 2021, 58(4): 0411003.
Hao Zhan, Zhencai Zhu, Yonghe Zhang, Ming Guo, Guopeng Ding. Loop-Closure Detection Using Image Sequencing Based on ResNet[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0411003.
[2] Konolige K, Agrawal M. FrameSLAM: from bundle adjustment to real-time visual mapping[J]. IEEE Transactions on Robotics, 2008, 24(5): 1066-1077.
[3] 林付春, 刘宇红, 周进凡, 等. 基于ORB特征的视觉里程计算法优化[J]. 激光与光电子学进展, 2019, 56(21): 211507.
[6] 陆世东, 涂美义, 罗小勇, 等. 基于图优化理论和GNSS激光SLAM位姿优化算法[J]. 激光与光电子学进展, 2020, 57(8): 081024.
[7] Mur-Artal R. Montiel J M M, Tardós J D. ORB-SLAM: a versatile and accurate monocular SLAM system[J]. IEEE Transactions on Robotics, 2015, 31(5): 1147-1163.
[9] Cummins M, Newman P. FAB-MAP: probabilistic localization and mapping in the space of appearance[J]. The International Journal of Robotics Research, 2008, 27(6): 647-665.
[10] BayH, Tuytelaars T, van Gool L. SURF: speeded up robust features[M] //Leonardis A, Bischof H, Pinz A. Computer vision-ECCV 2006. Lecture notes in computer science.2006, 3951: 404- 417.
[12] Salas-Moreno RF, Newcombe RA, StrasdatH, et al. SLAM++: simultaneous localisation and mapping at the level of objects[C]//2013 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2013, Portland, OR, USA.New York: IEEE Press, 2013: 1352- 1359.
[13] CalonderM, LepetitV, StrechaC, et al. BRIEF: binary robust independent elementary features[M] //Daniilidis K, Maragos P, Paragios N. Computer vision-ECCV 2010. Lecture notes in computer science. Berlin, Heidelberg: Springer, 2010, 6314: 778- 792.
[14] 刘国忠, 胡钊政. 基于SURF和ORB全局特征的快速闭环检测[J]. 机器人, 2017, 39(1): 36-45.
[15] LiuY, ZhangH. Visual loop closure detection with a compact image descriptor[C]//2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, October 7-12, 2012, Vilamoura, Portugal.New York: IEEE Press, 2012: 1051- 1056.
[17] 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.
[18] 邱晨力, 黄东振, 刘华巍, 等. 融合Gist特征与卷积自编码的闭环检测算法[J]. 激光与光电子学进展, 2019, 56(18): 181501.
[19] 鲍振强, 李艾华, 崔智高, 等. 融合多层次卷积神经网络特征的闭环检测算法[J]. 激光与光电子学进展, 2018, 55(11): 111507.
[20] 张学典, 顾璋琦, 秦晓飞. 基于VGG16模型的快速闭环检测算法[J]. 光学仪器, 2019, 41(3): 20-26.
[21] HouY, ZhangH, Zhou SL. Convolutional neural network-based image representation for visual loop closure detection[C]//2015 IEEE International Conference on Information and Automation, August 8-10, 2015, Lijiang, China.New York: IEEE Press, 2015: 2238- 2245.
[22] He KM, Zhang XY, Ren SQ, et al. ( 2015-12-10)[2020-07-14]. org/abs/1512. 03385. https://arxiv.
[24] DengJ, DongW, SocherR, et al.ImageNet: a large-scale hierarchical image database[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition, June 20-25, 2009, Miami, FL, USA. New York: IEEE Press, 2009: 248- 255.
[26] Fischler M A. Bolles R C. rando 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.
占浩, 朱振才, 张永合, 郭明, 丁国鹏. 基于残差网络的图像序列闭环检测[J]. 激光与光电子学进展, 2021, 58(4): 0411003. Hao Zhan, Zhencai Zhu, Yonghe Zhang, Ming Guo, Guopeng Ding. Loop-Closure Detection Using Image Sequencing Based on ResNet[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0411003.