[1] [1] 周芮， 刘延芳， 齐乃明， 等. 面向空间应用的视觉位姿估计技术综述［J］. 光学 精密工程， 2022， 30（20）： 2538-2553. doi: 10.37188/OPE.20223020.2538ZHOUR， LIUY F， QIN M， et al. Overview of visual pose estimation methods for space missions［J］. Optics and Precision Engineering， 2022， 30（20）： 2538-2553.（in Chinese）. doi: 10.37188/OPE.20223020.2538

[2] [2] 杨宁， 申景诗， 张建德， 等. 基于立体视觉的空间非合作航天器相对位姿自主测量［J］. 光学 精密工程， 2017， 25（5）：1331-1339. doi: 10.3788/ope.20172505.1331YANGN， SHENJ SH， ZHANGJ D， et al. Autonomous measurement of relative attitude and positionfor spatial non-cooperative spacecraft based on stereo vision［J］. Optics and Precision Engineering， 2017， 25（5）：1331-1339. （in Chinese）. doi: 10.3788/ope.20172505.1331

[3] [3] 李莹， 叶培建， 彭兢， 等. 基于火星探测的人工标志识别定位［J］. 光学 精密工程， 2015， 23（2）：566-572. doi: 10.3788/ope.20152302.0566LIY， YEP J， PENGJ， et al. Artificial target recognition and location based on Mars exploration［J］. Optics and Precision Engineering， 2015， 23（2）：566-572.（in Chinese）. doi: 10.3788/ope.20152302.0566

[4] B DROST, M ULRICH, N NAVAB et al. Model globally， match locally： efficient and robust 3D object recognition, 998-1005(2010).

[5] S HINTERSTOISSER, V LEPETIT, S ILIC et al. Model Based Training， Detection and Pose Estimation of Texture-less 3D Objects in Heavily Cluttered Scenes. Computer Vision - ACCV 2012, 548-562(2013).

[6] P S OGUN, Z USMAN, K DHARMARAJ et al. 3D vision assisted flexible robotic assembly of machine components, 9875, 342-348(9875).

[7] A BOCHKOVSKIY, C Y WANG, H LIAO. YOLOv4： optimal speed and accuracy of object detection(2020).

[8] D WU, M W LIAO, W T ZHANG et al. YOLOP： You only look once for panoptic driving perception. Machine Intelligence Research, 19, 550-562(2022).

[9] [9] 艾卫涛. 三维激光扫描点云去噪及在建模中的应用［D］. 西安： 西安科技大学， 2017.AIW T. Denoising of 3D Laser Scanning Point Cloud and Its Application In Modeling［D］. Xi'an： Xi'an University of Science and Technology， 2017. （in Chinese）

[10] [10] 曾清红， 卢德唐. 基于移动最小二乘法的曲线曲面拟合［J］. 工程图学学报， 2004（1）： 84-89. doi: 10.3969/j.issn.1003-0158.2004.01.017ZENGQ H， LUD. Curve and surface fitting based on moving least-squares methods［J］. Journal of Engineering Graphics， 2004（1）： 84-89.（in Chinese）. doi: 10.3969/j.issn.1003-0158.2004.01.017

[11] Q Y ZHOU, J PARK, V KOLTUN. Fast Global Registration. Computer Vision - ECCV 2016, 766-782(2016).

[12] R B RUSU, N BLODOW, Z C MARTON et al. Aligning point cloud views using persistent feature histograms, 3384-3391(2008).

[13] R B RUSU, N BLODOW, M BEETZ. Fast point feature histograms （FPFH） for 3D registration, 3212-3217(2009).

[14] C R QI, L YI, H SU et al. PointNet++： Deep Hierarchical Feature Learning on Point Sets in A Metric Space. arXiv, 1706-02413(2017). https：//arxiv.org/abs/1706.02413.pdf

[15] R Q CHARLES, S HAO, K C MO et al. PointNet： deep learning on point sets for 3D classification and segmentation, 77-85(2017).

[16] M A FISCHLER, R C BOLLES. Random Sample Consensus： A Paradigm for Model Fitting With Applications to Image Analysis and Automated Cartography. Readings in Computer Vision, 726-740(1987).

[17] P J HUBER. Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35, 73-101(1964).

[18] P J BESL, N D MCKAY. A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14, 239-256(1992).