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Zou, D., & Tan, P. (2012). Coslam: Collaborative visual slam in dynamic environments. IEEE transactions on pattern analysis and machine intelligence, 35(2), 354-366.Search in Google Scholar
Demim, F., Nemra, A., Boucheloukh, A., Kobzili, E., Hamerlain, M., & Bazoula, A. (2019). SLAM based on adaptive SVSF for cooperative unmanned vehicles in dynamic environment. IFAC-PapersOnLine, 52(8), 73-80.Search in Google Scholar
Yan, Z., Chu, S., & Deng, L. (2021). Visual SLAM based on instance segmentation in dynamic scenes. Measurement Science and Technology, 32(9), 095113.Search in Google Scholar
Taketomi, T., Uchiyama, H., & Ikeda, S. (2017). Visual SLAM algorithms: A survey from 2010 to 2016. IPSJ transactions on computer vision and applications, 9, 1-11.Search in Google Scholar
Li, X., Shen, Y., Lu, J., Jiang, Q., Xie, O., Yang, Y., & Zhu, Q. (2022). DyStSLAM: an efficient stereo vision SLAM system in dynamic environment. Measurement Science and Technology, 34(2), 025105.Search in Google Scholar
Cheng, J., Zhang, H., & Meng, M. Q. H. (2020). Improving visual localization accuracy in dynamic environments based on dynamic region removal. IEEE Transactions on Automation Science and Engineering, 17(3), 1585-1596.Search in Google Scholar
Wahrmann, D., Hildebrandt, A. C., Bates, T., Wittmann, R., Sygulla, F., Seiwald, P., & Rixen, D. (2019). Vision-based 3d modeling of unknown dynamic environments for real-time humanoid navigation. International Journal of Humanoid Robotics, 16(01), 1950002.Search in Google Scholar
Zhao, Y., Xiong, Z., Zhou, S., Wang, J., Zhang, L., & Campoy, P. (2022). Perception-aware planning for active SLAM in dynamic environments. Remote Sensing, 14(11), 2584.Search in Google Scholar
Liang, Z., & Wang, C. (2021). A semi-direct monocular visual SLAM algorithm in complex environments. Journal of Intelligent & Robotic Systems, 101(1), 25.Search in Google Scholar
Dai, W., Zhang, Y., Li, P., Fang, Z., & Scherer, S. (2020). Rgb-d slam in dynamic environments using point correlations. IEEE transactions on pattern analysis and machine intelligence, 44(1), 373-389.Search in Google Scholar
Song, B., Yuan, X., Ying, Z., Yang, B., Song, Y., & Zhou, F. (2023). DGM-VINS: Visual–inertial SLAM for complex dynamic environments with joint geometry feature extraction and multiple object tracking. IEEE Transactions on Instrumentation and Measurement, 72, 1-11.Search in Google Scholar
Urzua, S., Munguía, R., & Grau, A. (2017). Vision-based SLAM system for MAVs in GPS-denied environments. International Journal of Micro Air Vehicles, 9(4), 283-296.Search in Google Scholar
Ma, H., Qin, Y., Duan, S., & Wang, L. (2024, July). A Robust Visual SLAM System in Dynamic Environment. In International Symposium on Neural Networks (pp. 248-257). Singapore: Springer Nature Singapore.Search in Google Scholar
Saputra, M. R. U., Markham, A., & Trigoni, N. (2018). Visual SLAM and structure from motion in dynamic environments: A survey. ACM Computing Surveys (CSUR), 51(2), 1-36.Search in Google Scholar
Dang, X., Rong, Z., & Liang, X. (2021). Sensor fusion-based approach to eliminating moving objects for SLAM in dynamic environments. Sensors, 21(1), 230.Search in Google Scholar
Cioffi, G., Cieslewski, T., & Scaramuzza, D. (2022). Continuous-time vs. discrete-time vision-based SLAM: A comparative study. IEEE Robotics and Automation Letters, 7(2), 2399-2406.Search in Google Scholar
Yu, C., Liu, Z., Liu, X. J., Xie, F., Yang, Y., Wei, Q., & Fei, Q. (2018, October). DS-SLAM: A semantic visual SLAM towards dynamic environments. In 2018 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 1168-1174). IEEE.Search in Google Scholar
Xing, Z., Zhu, X., & Dong, D. (2022). DE‐SLAM: SLAM for highly dynamic environment. Journal of Field Robotics, 39(5), 528-542.Search in Google Scholar
Cui, L., & Ma, C. (2019). SOF-SLAM: A semantic visual SLAM for dynamic environments. IEEE access, 7, 166528-166539.Search in Google Scholar
Li, A., Wang, J., Xu, M., & Chen, Z. (2021). DP-SLAM: A visual SLAM with moving probability towards dynamic environments. Information Sciences, 556, 128-142.Search in Google Scholar
Wen, S., Li, P., Zhao, Y., Zhang, H., Sun, F., & Wang, Z. (2021). Semantic visual SLAM in dynamic environment. Autonomous Robots, 45(4), 493-504.Search in Google Scholar
Xiao, L., Wang, J., Qiu, X., Rong, Z., & Zou, X. (2019). Dynamic-SLAM: Semantic monocular visual localization and mapping based on deep learning in dynamic environment. Robotics and Autonomous Systems, 117, 1-16.Search in Google Scholar
He, J., Li, M., Wang, Y., & Wang, H. (2023). OVD-SLAM: An online visual SLAM for dynamic environments. IEEE Sensors Journal, 23(12), 13210-13219.Search in Google Scholar
Ping Wang,Chuanxue Li,Fangkai Cai & Li Zheng. (2024). An improved SLAM algorithm for substation inspection robot based on the fusion of IMU and visual information. Energy Informatics(1),86-86.Search in Google Scholar
Huiran Hu & Aiguo Song. (2025). Digital image correlation calculation method for RGB-D camera multi-view matching using variable template. Measurement115617-115617.Search in Google Scholar
Hairong You & Yang Xie. (2024). Automatic driving image matching via Random Sample Consensus (RANSAC) and Spectral Clustering (SC) with monocular camera. The Review of scientific instruments(8).Search in Google Scholar
Zhichao Cui,Zeqi Chen,Chi Zhang,Gaofeng Meng,Yuehu Liu & Xiangmo Zhao. (2024). DDGPnP: Differential degree graph based PnP solution to handle outliers. Computer Vision and Image Understanding 104130-104130.Search in Google Scholar
XiangYin Zhang,HaiBin Duan & QiNan Luo. (2014). Levenberg-Marquardt based artificial physics method for mobile robot oscillation alleviation. Science China Physics, Mechanics & Astronomy(9), 1771-1777.Search in Google Scholar
Henan Li,Junping Yin & Liguo Jiao. (2024). Digital Surface Model Generation from Satellite Images Based on Double-Penalty Bundle Adjustment Optimization. Applied Sciences(17),7777-7777.Search in Google Scholar