[1] 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).

[2] Campos C, Elvira R, Rodríguez J J G et al. ORB-SLAM3: an accurate open-source library for visual, visual-inertial, and multimap SLAM[J]. IEEE Transactions on Robotics, 37, 1874-1890(2021).

[3] Engel J, Schöps T, Cremers D. LSD-SLAM: large-scale direct monocular SLAM[M]. Cremers D, Reid I, Saito H, et al. Computer vision-ECCV 2014. Lecture notes in computer science, 8690, 834-849(2014).

[4] Fischler M A, Bolles R C. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the ACM, 24, 381-395(1981).

[5] Sun Y X, Liu M, Meng M Q H. Improving RGB-D SLAM in dynamic environments: a motion removal approach[J]. Robotics and Autonomous Systems, 89, 110-122(2017).

[6] Zou B, Lin S Y, Yin Z S. Semantic mapping based on YOLOv3 and visual SLAM[J]. Laser & Optoelectronics Progress, 57, 201012(2020).

[7] Bescos B, Fácil J M, Civera J et al. DynaSLAM: tracking, mapping, and inpainting in dynamic scenes[J]. IEEE Robotics and Automation Letters, 3, 4076-4083(2018).

[8] Wang M Y, Song W. RGB-D SLAM algorithm based on adaptive semantic segmentation in dynamic scene[J]. Robot, 45, 16-27(2023).

[9] Li S L, Lee D. RGB-D SLAM in dynamic environments using static point weighting[J]. IEEE Robotics and Automation Letters, 2, 2263-2270(2017).

[10] Li A, Wang J K, Xu M et al. DP-SLAM: a visual SLAM with moving probability towards dynamic environments[J]. Information Sciences, 556, 128-142(2021).

[11] He K M, Gkioxari G, Dollár P et al. Mask R-CNN[C], 2980-2988(2017).

[12] Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 640-651(2016).

[13] Badrinarayanan V, Kendall A, Cipolla R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 2481-2495(2017).

[14] Bolya D, Zhou C, Xiao F Y et al. YOLACT: real-time instance segmentation[C], 9156-9165(2020).

[15] Kasper-Eulaers M, Hahn N, Berger S et al. Short communication: detecting heavy goods vehicles in rest areas in winter conditions using YOLOv5[J]. Algorithms, 14, 114(2021).

[16] 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

[17] Han K, Wang Y H, Tian Q et al. GhostNet: more features from cheap operations[C], 1577-1586(2020).

[18] Hartley R, Zisserman A[M]. Multiple view geometry in computer vision(2003).

[19] Engels C, Stewénius H, Nistér D. Bundle adjustment rules[EB/OL]. https://www.isprs.org/proceedings/XXXVI/part3/singlepapers/O_24.pdf

[20] Sturm J, Engelhard N, Endres F et al. A benchmark for the evaluation of RGB-D SLAM systems[C], 573-580(2012).

[21] Yu C, Liu Z X, Liu X J et al. DS-SLAM: a semantic visual SLAM towards dynamic environments[C], 1168-1174(2019).

[22] Liu Y B, Miura J. RDS-SLAM: real-time dynamic SLAM using semantic segmentation methods[J]. IEEE Access, 9, 23772-23785(2021).