[1] [1] Lowry S, Sünderhauf N, Newman P, et al. Visual place recognition: A survey[J]. IEEE Transactions on Robotics, 2016, 32(1): 1-19.

[2] [2] Lowe D G. Object recognition from local scale-invariant features[C]//The Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999, 2: 1150-1157.

[3] [3] Bay H, Ess A, Tuytelaars T, et al. Speeded-up robust features (SURF)[J]. Computer Vision and Image Understanding, 2008, 110(3): 346-359.

[4] [4] Cummins M, Newman P M. Appearance-only SLAM at large scale with FAB-MAP 2.0[J]. International Journal of Robotics Research, 2011, 30(9): 1100-1123.

[5] [5] Angeli A, Filliat D, Doncieux S, et al. Fast and incremental method for loop-closure detection using bags of visual words[J]. IEEE Transactions on Robotics, 2008, 24(5): 1027-1037.

[6] [6] Nister D, Stewenius H. Scalable recognition with a vocabulary tree[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2006, 2: 2161-2168.

[7] [7] Oliva A, Torralba A. Building the gist of a scene: The role of global image features in recognition[J]. Progress in Brain Research, 2006, 155: 23-36.

[8] [8] Blaer P, Allen P. Topological mobile robot localization using fast vision techniques[C]//IEEE International Conference on Robotics and Automation, 2002, 1: 1031-1036.

[9] [9] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[C]//Advances in Neural Information Processing Systems, 2012: 1097-1105.

[10] [10] Babenko A, Slesarev A, Chigorin A, et al. Neural codes for image retrieval[C]//European Conference on Computer Vision. Springer International Publishing, 2014: 584-599.

[11] [11] Redmon J, Divvala S, Girshick R, et al. You only look once: unified, real-time object detection[C]//Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, 2016:779-788.

[12] [12] Luo Haibo, Xu Lingyun, Hui Bin, et al. Status and prospect of target tracking based on deep learning[J]. Infrared and Laser Engineering, 2017, 46(5): 0502002. (in Chinese)

[13] [13] Bao Xuejing, Dai Shijie, Guo Cheng, et al. Nonlinear distortion image correction from confocal microscope based on interpolation[J]. Infrared and Laser Engineering, 2017, 46(11): 1103006. (in Chinese)

[14] [14] Li Q, Li K, You X, et al. Place recognition based on deep feature and adaptive weighting of similarity matrix[J]. Neurocomputing, 2016, 199: 114-127.

[15] [15] Hou Y, Zhang H, Zhou S. Convolutional neural network-based image representation for visual loop closure detection[C]//IEEE International Conference on Information and Automation, 2015: 2238-2245.

[16] [16] Zhou B, Lapedriza A, Xiao J, et al. Learning deep features for scene recognition using places database[C]//Advances in Neural Information Processing Systems, 2014: 487-495.

[17] [17] Arandjelovic R, Gronat P, Torii A, et al. NetVLAD: CNN architecture for weakly supervised place recognition[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 5297-5307.

[18] [18] Jégou H, Douze M, Schmid C, et al. Aggregating local descriptors into a compact image representation[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2010: 3304-3311.

[19] [19] Sünderhauf N, Shirazi S, Dayoub F, et al. On the performance of convnet features for place recognition[C]// IEEE International Conference on Intelligent Robots and Systems, 2015: 4297-4304.

[20] [20] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. Computer Vision and Pattern Recognition, 2014, arXiv preprint arXiv:1409.1556.

[21] [21] Zeiler M D, Fergus R. Visualizing and Understanding Convolutional Networks[C]//European Conference on Computer Vision, 2014: 818-833.

[22] [22] Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks[C]//Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010: 249-256.

[23] [23] Jia Y, Shelhamer E, Donahue J, et al. Caffe: Convolutional architecture for fast feature embedding[C]//Proceedings of the 22nd ACM international conference on Multimedia, 2014, arXiV preprint arxiv: 1408.5093.

[24] [24] Torii A, Arandjelovic R, Sivic J, et al. 24/7 place recognition by view synthesis[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 2667665.