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REFERENCES

1 
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14 
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16 
C. Zheng, Q. Zhu, W. Xu, X. Liu, Q. Guo, and F. Zhang, ``FAST-LIVO: Fast and tightly-coupled sparse-direct LiDAR-inertial-visual odometry,'' Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4003–4009, October 2022.DOI
17 
T. Shan, B. Englot, C. Ratti, and D. Rus, ``LVI-SAM: Tightly-coupled LiDAR-visual-inertial odometry via smoothing and mapping,'' Proc. of the IEEE International Conference on Robotics and Automation (ICRA), Xi'an, China, pp. 5692–5698, May–June 2021.DOI
18 
Y. Jia, H. Luo, F. Zhao, G. Jiang, Y. Li, J. Yan, Z. Jiang, and Z. Wang, ``Lvio-fusion: A self-adaptive multi-sensor fusion SLAM framework using actor-critic method,'' Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 286–293, September 2021.DOI
19 
X. Zuo, P. Geneva, W. Lee, Y. Liu, and G. Huang, ``LIC-fusion: LiDAR-inertial-camera odometry,'' Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, November 2019.DOI
20 
Q. Fu, J. Wang, H. Yu, I. Ali, F. Guo, Y. He, and H. Zhang, ``PL-VINS: Real-time monocular visual-inertial SLAM with point and line features,'' arXiv preprint arXiv:2009.07462, 2020.DOI
21 
J. He, M. Li, Y. Wang, and H. Wang, ``PLE-SLAM: A visual-inertial SLAM based on point-line features and efficient IMU initialization,'' arXiv preprint arXiv:2401.01081, 2024.DOI
22 
X. Xu, L. Zhang, J. Yang, C. Cao, W. Wang, Y. Ran, Z. Tan, and M. Luo, ``A review of multi-sensor fusion SLAM systems based on 3D LiDAR,'' Remote Sensing, vol. 14, no. 12, 2835, 2022.DOI
23 
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24 
M. Servières, V. Renaudin, A. Dupuis, and N. Antigny, ``Visual and visual-inertial SLAM: State of the art, classification, and experimental benchmarking,'' Journal of Sensors, vol. 2021, 2054828, pp. 1–26, 2021.DOI
25 
I. A. Abaspur Kazerouni, L. Fitzgerald, G. Dooly, and D. Toal, ``A survey of state-of-the-art on visual SLAM,'' Expert Systems with Applications, vol. 205, 117734, November 2022DOI