[1] Durrant-Whyte H, Bailey T. Simultaneous localization and mapping: part I[J]. IEEE Robotics & Automation Magazine, 13, 99-110(2006).
[2] Nedjah N, de Luiza M M, de Oliveira P J A. Simultaneous localization and mapping using swarm intelligence based methods[J]. Expert Systems with Applications, 159, 113547(2020).
[3] Hu M C, Ao H R, Jiang H Y. Experimental research on feature extraction of laser SLAM based on artificial landmarks[C]. //2019 Chinese Control and Decision Conference (CCDC), June 3-5, 2019, Jiangxi, China, 5495-5500(2019).
[4] Bavle H, de la Puente P, How J P et al. VPS-SLAM: visual planar semantic SLAM for aerial robotic systems[J]. IEEE Access, 8, 60704-60718(2020).
[5] 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).
[6] Nister D, Naroditsky O, Bergen J. Visual odometry[C]. //Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 27-July 2, 2004, Washington, DC, USA., 652-659(2004).
[7] Xu G F, Zeng J C, Liu X X. Visual odometer based on optical flow method and feature matching[J]. Laser & Optoelectronics Progress, 57, 201501(2020).
[9] Bay H, Ess A, Tuytelaars T et al. Speeded-up robust features (SURF)[J]. Computer Vision and Image Understanding, 110, 346-359(2008).
[10] Rublee E, Rabaud V, Konolige K et al. ORB: an efficient alternative to SIFT or SURF[C]. //2011 International Conference on Computer Vision, November 6-13, 2011, Barcelona, Spain, 2564-2571(2011).
[11] Zheng G Q, Zhou Z P. Improved augmented reality registration method based on VSLAM[J]. Laser & Optoelectronics Progress, 56, 061501(2019).
[12] Klein G, Murray D. Parallel tracking and mapping on a camera phone[C]. //2009 8th IEEE International Symposium on Mixed and Augmented Reality, October 19-22, 2009, Orlando, FL, USA., 83-86(2009).
[13] 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).
[14] Mur-Artal R, Tardós J D. ORB-SLAM2: an open-source SLAM system for monocular, stereo, and RGB-D cameras[J]. IEEE Transactions on Robotics, 33, 1255-1262(2017).
[15] Campos C, Montiel J M M, Tardós J D. Inertial-only optimization for visual-inertial initialization[C]. //2020 IEEE International Conference on Robotics and Automation (ICRA), May 31-August 31, 2020, Paris, France., 51-57(2020).
[16] Ummenhofer B, Zhou H Z, Uhrig J et al. DeMoN: depth and motion network for learning monocular stereo[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA, 5622-5631(2017).
[17] Mahjourian R, Wicke M, Angelova A. Unsupervised learning of depth and ego-motion from monocular video using 3D geometric constraints[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 5667-5675(2018).
[18] Liu Q, Li R H, Hu H S et al. Using unsupervised deep learning technique for monocular visual odometry[J]. IEEE Access, 7, 18076-18088(2019).
[19] Yang X H, Li X J, Guan Y et al. Overfitting reduction of pose estimation for deep learning visual odometry[J]. China Communications, 17, 196-210(2020).
[20] Kendall A, Grimes M, Cipolla R. PoseNet: a convolutional network for real-time 6-DOF camera relocalization[C]. //2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile, 2938-2946(2015).
[21] Wang S, Clark R, Wen H K et al. DeepVO: towards end-to-end visual odometry with deep recurrent convolutional neural networks[C]. //2017 IEEE International Conference on Robotics and Automation (ICRA), May 29-June 3, 2017, Singapore, 2043-2050(2017).
[22] Li R H, Wang S, Long Z Q et al. UnDeepVO: monocular visual odometry through unsupervised deep learning[C]. //2018 IEEE International Conference on Robotics and Automation (ICRA), May 21-25, 2018, Brisbane, QLD, Australia, 7286-7291(2018).
[23] Sheng L, Xu D, Ouyang W L et al. Unsupervised collaborative learning of keyframe detection and visual odometry towards monocular deep SLAM[C]. //2019 IEEE/CVF International Conference on Computer Vision (ICCV), October 27-November 2, 2019, Seoul, Korea (South)., 4301-4310(2019).
[24] Liu Q, Zhang H D, Xu Y M et al. Unsupervised deep learning-based RGB-D visual odometry[J]. Applied Sciences, 10, 5426(2020).
[25] Zhang Z T, Zhang R F, Liu Y H. Visual odometry algorithm based on deep learning[J]. Laser & Optoelectronics Progress, 58, 041501(2021).
[26] Szegedy C, Liu W, Jia Y Q et al. Going deeper with convolutions[C]. //2015 IEEE Conferenceon Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA., 1-9(2015).
[27] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. //Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 3-19(2018).
[28] Geiger A, Lenz P, Stiller C et al. Vision meets robotics: the KITTI dataset[J]. The International Journal of Robotics Research, 32, 1231-1237(2013).
[29] Zhou T H, Brown M, Snavely N et al. Unsupervised learning of depth and ego-motion from video[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 6612-6619(2017).
[30] Kitt B, Geiger A, Lategahn H. Visual odometry based on stereo image sequences with RANSAC-based outlier rejection scheme[C]. //2010 IEEE Intelligent Vehicles Symposium, June 21-24, 2010, La Jolla, CA, USA., 486-492(2010).
[31] Geiger A, Ziegler J, Stiller C. StereoScan: dense 3D reconstruction in real-time[C]. //2011 IEEE Intelligent Vehicles Symposium (IV), June 5-9, 2011, Baden-Baden, Germany, 963-968(2011).