[1] Tian Y, Chen H W, Wang F S et al. Overview of SLAM algorithms for mobile robots[J]. Computer Science, 48, 223-234(2021).

[2] Wang F B, Cao K, Gong X et al. Lidar SLAM method for photovoltaic power stations based on factor graph optimization[J]. Laser & Optoelectronics Progress, 61, 1415002(2024).

[3] Feng G Y, Zhang J, Miao Y et al. Indoor-floor-plan construction method based on multi-source data fusion[J]. Laser & Optoelectronics Progress, 61, 1428005(2024).

[4] Wei S F, Pang F, Liu Z B et al. Survey of LiDAR-based SLAM algorithm[J]. Application Research of Computers, 37, 327-332(2020).

[5] Chen K X, Liu R, Zhao B et al. Laser positioning method based on line endpoint direction matching[J]. Laser & Optoelectronics Progress, 61, 0428010(2024).

[6] Zhao L Y, San H J, Chen J P et al. Laser location of mobile robot based on ICP algorithm[J]. Laser & Optoelectronics Progress, 61, 0811001(2024).

[7] Liu R F, Wang F, Ren H W et al. Road scene laser point cloud registration method based on geographical object features[J]. Chinese Journal of Lasers, 49, 1810002(2022).

[8] Kwok T H. DNSS: dual-normal-space sampling for 3-D ICP registration[J]. IEEE Transactions on Automation Science and Engineering, 16, 241-252(2019).

[9] Censi A. An ICP variant using a point-to-line metric[C], 19-25(2008).

[10] Biber P, Strasser W. The normal distributions transform: a new approach to laser scan matching[C], 2743-2748(2003).

[11] Fang Z, Zhao S B, Li H L. A depth vision SLAM algorithm combining sparse geometric features with range flow[J]. Robot, 41, 185-196, 241(2019).

[12] Meng J. Research on key technologies of efficient localization of workshop autonomous mobile robot[D](2021).

[13] Zhou F, Zhang L M, Deng C L et al. Improved point-line feature based visual SLAM method for complex environments[J]. Sensors, 21, 4604(2021).

[14] Li Y, Harada T. Lepard: learning partial point cloud matching in rigid and deformable scenes[C], 5544-5554(2022).

[15] Jensfelt P, Kristensen S. Active global localization for a mobile robot using multiple hypothesis tracking[J]. IEEE Transactions on Robotics and Automation, 17, 748-760(2001).

[16] Nakamura T, Tashita Y. Congruence transformation invariant feature descriptor for robust 2D scan matching[C], 1648-1653(2013).

[17] Nakamura T, Wakita S. Robust global scan matching method using congruence transformation invariant feature descriptors and a geometric constraint between keypoints[C], 3103-3108(2014).

[18] Taleghani S, Sharbafi M A, Haghighat A T et al. ICE matching, a robust mobile robot localization with application to SLAM[C], 186-192(2010).

[19] Mohamed H A, Moussa A M, Elhabiby M M et al. Improved real-time scan matching using corner features[J]. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 5, 533-539(2016).

[20] Liu Y M, Liu Y X. Research on point cloud filtering data processing method based on self-adaptive euclidean clustering network[J]. Academic Journal of Computing & Information Science, 5, 60-66(2022).

[21] AlYousefi K S. Toward accurate motion estimation with affine correspondences[D], 36-37(2020).

[22] O’Driscoll D, Ramirez D E. Limitations of the least squares estimators; a teaching perspective[C](2016).