[1] Placed J A, Strader J, Carrillo H et al. A survey on active simultaneous localization and mapping: state of the art and new frontiers[J]. IEEE Transactions on Robotics, 39, 1686-1705(2023).

[2] Xi Z H, Wang H X, Han S Q. Fast mismatch elimination algorithm and map-building based on ORB-SLAM2 system[J]. Journal of Computer Applications, 40, 3289-3294(2020).

[3] Ma A Q, Yao W Q. Multi sensor adaptive fusion SLAM method for underground mobile robots in coal mines[J]. Industry and Mine Automation, 50, 107-117(2024).

[4] Ma Y T, Liu F, Wang J et al. An adaptive visual SLAM positioning method considering brightness characteristics of images[J]. Science of Surveying and Mapping, 48, 42-50(2023).

[5] Xing F Y, Xu C, Gao H W. Efficient and high-precision illumination adaptive ORB feature matching algorithm[J]. Journal of Electronic Measurement and Instrumentation, 37, 140-147(2023).

[6] Jiang S, Cui Z X, Dai B B et al. SLAM based localization method for underground coal mine robots with adaptability to dark environments[J/OL], 1-14. http://kns.cnki.net/kcms/detail/11.2402.td.20240325.1341.004.html

[7] Liu C, Dang S W, Chen L. Improved feature matching and dense-mapping algorithm based on ORB-SLAM3[J]. Application Research of Computers, 40, 3443-3449(2023).

[8] Xiao Z, Li S. SL-SLAM: a robust visual-inertial SLAM based deep feature extraction and matching[EB/OL]. https://arxiv.org/html/2405.03413v2

[9] Cai X Q, Wang X S, Li W. A visual SLAM algorithm in indoor weak texture environment[J]. Robot, 46, 284-293, 304(2024).

[10] Baráth D, Noskova J, Ivashechkin M et al. MAGSAC++, a fast, reliable and accurate robust estimator[C], 1301-1309(2020).

[11] Fu L, Fan Y G, Li G S et al. Grid motion statistics fast matching algorithm for rotating images[J]. Laser & Optoelectronics Progress, 58, 1210005(2021).

[12] 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).

[13] Barath D, Matas J, Noskova J. MAGSAC: marginalizing sample consensus[C], 10189-10197(2019).

[14] Chen W F, Shang G T, Ji A H et al. An overview on visual SLAM: from tradition to semantic[J]. Remote Sensing, 14, 3010(2022).

[15] Bian J W, Lin W Y, Liu Y et al. GMS: grid-based motion statistics for fast, ultra-robust feature correspondence[J]. International Journal of Computer Vision, 128, 1580-1593(2020).

[16] 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).

[17] Fu Q, Yu H S, Lai L H et al. A robust RGB-D SLAM system with points and lines for low texture indoor environments[J]. IEEE Sensors Journal, 19, 9908-9920(2019).

[18] Fu Q, Yu H S, Wang X L et al. Fast ORB-SLAM without keypoint descriptors[J]. IEEE Transactions on Image Processing, 31, 1433-1446(2022).

[19] Wu Z H, Fan Y S, Wang G F. Visual SLAM algorithm based on deep learning feature extraction and mismatch elimination[C], 21-27(2023).