[1] Lin H C, Lü Q, Wei H et al. Quadrotor autonomous flight and three-dimensional dense reconstruction based on VI-SLAM[J]. Acta Optica Sinica, 38, 223-230(2018).

[2] Liu W H, Sun L Y, Li Q X et al. SLAM algorithm with tight coupling of vision and LiDAR odometer[J]. Laser & Optoelectronics Progress, 60, 1428006(2023).

[3] Jiang L, Liu Q, Lei B et al. Identifying and constructing semantic maps based on laser and vision fusions for improving localization performance[J]. Chinese Journal of Lasers, 49, 1810003(2022).

[4] Zhao L Y, Yan L. Advanced quaternion unscented Kalman filter based on SLAM of mobile robot pose estimation[J]. Acta Geodaetica et Cartographica Sinica, 51, 212-223(2022).

[5] Leutenegger S, Lynen S, Bosse M et al. Keyframe-based visual-inertial odometry using nonlinear optimization[J]. The International Journal of Robotics Research, 34, 314-334(2015).

[6] Mur-Artal R, Tardós J D. Visual-inertial monocular SLAM with map reuse[J]. IEEE Robotics and Automation Letters, 2, 796-803(2017).

[7] 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).

[8] Rambach J R, Tewari A, Pagani A et al. Learning to fuse: a deep learning approach to visual-inertial camera pose estimation[C], 71-76(2016).

[9] Yu H S, Guo F, Guo L F et al. Robust monocular visual-inertial SLAM based on the improved SuperPoint network[J]. Chinese Journal of Scientific Instrument, 42, 116-126(2021).

[10] Xu C, Zhou Y J, Luo C. Visual SLAM method based on optical flow and instance segmentation for dynamic scenes[J]. Acta Optica Sinica, 42, 1415002(2022).

[11] Clark R, Wang S, Wen H K et al. VINet: visual-inertial odometry as a sequence-to-sequence learning problem[EB/OL]. https://arxiv.org/abs/1701.08376

[12] Li L Q, Cai C L. Robust SLAM in dynamic scenes based on deep learning and edge detection[J]. Chinese Journal of Sensors and Actuators, 34, 80-88(2021).

[13] Xia L L, Shen R, Chi D R et al. An optical flow-line feature based monocular visual-inertial SLAM algorithm[J]. Journal of Chinese Inertial Technology, 28, 568-575(2020).

[14] Li H L, Zhang Z B, Xu X J. Low-light image enhancement algorithm for visual SLAM based on online estimation[J]. Journal of Optoelectronics·Laser, 32, 945-952(2021).

[15] Hu H W, Wang M, Cheng W P et al. An image dust-filtering and feature enhancement algorithm for underwater visual SLAM[J]. Robot, 45, 197-206(2023).

[16] Yu J Z, Kong S H, Meng Y. Methods and technologies for visual perception of underwater environment[J]. Robot, 44, 224-235(2022).

[17] Liao J, Chen M H, Wang Y et al. Image enhancement algorithm for seedling image with dual gamma correction[J]. Jiangsu Journal of Agricultural Sciences, 36, 1411-1418(2020).

[18] Tang Y H, Han K, Guo J Y et al. GhostNetv2: enhance cheap operation with long-range attention[EB/OL]. https://arxiv.org/abs/2211.12905

[19] DeTone D, Malisiewicz T, Rabinovich A. SuperPoint: self-supervised interest point detection and description[C], 337-33712(2018).

[20] Bouguet J Y. Pyramidal implementation of the affine lucas kanade feature tracker description of the algorithm[EB/OL]. http://robots.stanford.edu/cs223b04/algo_tracking.pdf

[21] Li H K, Li H, Zhao L et al. Underwater image enhancement based on improved Gamma correction and multi-scale fusion[J]. Journal of Optoelectronics·Laser, 33, 700-708(2022).

[22] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. http://arxiv.org/abs/1409.1556v6

[23] Miao Z H, Zhu L J, Zhao C C et al. Image enhancement of low-light parking space based on Retinex[J]. Automotive Engineering, 45, 989-996(2023).

[24] Dong X, Wang G, Pang Y et al. Fast efficient algorithm for enhancement of low lighting video[C](2011).

[25] Li C Y, Guo C L, Loy C C. Learning to enhance low-light image via zero-reference deep curve estimation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, 4225-4238(2022).