• Laser & Optoelectronics Progress
  • Vol. 58, Issue 4, 0411003 (2021)
Hao Zhan1、2、3、*, Zhencai Zhu1、2、3, Yonghe Zhang1、2、3, Ming Guo1、2, and Guopeng Ding1、2
Author Affiliations
  • 1Innovation Academy for Microsatellite, Chinese Academy of Sciences, Shanghai 201203, China
  • 2Key Laboratory of Microsatellites, Chinese Academy of Sciences, Shanghai 201203, China
  • 3University of Chinese Academy of Sciences, Beijing 100049, China
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    DOI: 10.3788/LOP202158.0411003 Cite this Article Set citation alerts
    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 Copy Citation Text show less
    References

    [1] Boal J, Sánchez-Miralles Á, Arranz Á. Topological simultaneous localization and mapping: a survey[J]. Robotica, 32, 803-821(2014).

    [2] Konolige K, Agrawal M. FrameSLAM: from bundle adjustment to real-time visual mapping[J]. IEEE Transactions on Robotics, 24, 1066-1077(2008).

    [3] Lin F C, Liu Y H, Zhou J F et al. Optimization of visual odometry algorithm based on ORB feature[J]. Laser & Optoelectronics Progress, 56, 211507(2019).

    [4] Ho K L, Newman P. Detecting loop closure with scene sequences[J]. International Journal of Computer Vision, 74, 261-286(2007).

    [5] Williams B, Klein G, Reid I. Automatic relocalization and loop closing for real-time monocular SLAM[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 1699-1712(2011).

    [6] Lu S D, Tu M Y, Luo X Y et al. Laser SLAM pose optimization algorithm based on graph optimization theory and GNSS[J]. Laser & Optoelectronics Progress, 57, 081024(2020).

    [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, 31, 1147-1163(2015).

    [8] Qin T, Li P L, Shen S J. VINS-mono: a robust and versatile monocular visual-inertial state estimator[J]. IEEE Transactions on Robotics, 34, 1004-1020(2018). http://ieeexplore.ieee.org/document/8421746/

    [9] Cummins M, Newman P. FAB-MAP: probabilistic localization and mapping in the space of appearance[J]. The International Journal of Robotics Research, 27, 647-665(2008).

    [10] Bay H. 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., 3951, 404-417(2006).

    [11] Galvez-López D, Tardos J D. Bags of binary words for fast place recognition in image sequences[J]. IEEE Transactions on Robotics, 28, 1188-1197(2012).

    [12] Salas-Moreno R F, Newcombe R A, Strasdat H 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., 1352-1359(2013).

    [13] Calonder M, Lepetit V, Strecha C 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, 6314, 778-792(2010).

    [14] Liu G Z, Hu Z Z. Fast loop closure detection based on holistic features from SURF and ORB[J], 39, 36-45(2017).

    [15] Liu Y, Zhang H. 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., 1051-1056(2012).

    [16] Friedman A. Framing pictures: the role of knowledge in automatized encoding and memory for gist[J]. Journal of Experimental Psychology. General, 108, 316-355(1979). http://psycnet.apa.org/journals/xge/108/3/316/

    [17] Gao X, Zhang T. Unsupervised learning to detect loops using deep neural networks for visual SLAM system[J]. Autonomous Robots, 41, 1-18(2017).

    [18] Qiu C L, Huang D Z, Liu H W et al. Loop closure detection algorithm based on convolutional autoencoder fused with GIST feature[J]. Laser & Optoelectronics Progress, 56, 181501(2019).

    [19] Bao Z Q, Li A H, Cui Z G et al. Loop closure detection algorithm based on multi-level convolutional neural network features[J]. Laser & Optoelectronics Progress, 55, 111507(2018).

    [20] Zhang X D, Gu Z Q, Qin X F. VGG16 model-based fast loop closure detection algorithm[J]. Optical Instruments, 41, 20-26(2019).

    [21] Hou Y, Zhang H, Zhou S L. 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., 2238-2245(2015).

    [22] He K M, Zhang X Y, Ren S Q et al. -12-10)[2020-07-14], org/abs/1512, 03385(2015). https://arxiv.

    [23] Simonyan K. -04-10)[2020-07-14][EB/OL]. Zisserman A. Very deep convolutional networks for large-scale image recognition., org/abs/1409, 1556(2015). 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.

    [25] Zhou B L, Lapedriza A, Khosla A et al. Places: a 10 million image database for scene recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 1452-1464(2018). http://www.ncbi.nlm.nih.gov/pubmed/28692961

    [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, 24, 381-395(1981).

    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
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