[1] 王耀南, 江一鸣, 姜娇. 机器人感知与控制关键技术及其智能制造应用[J]. 自动化学报, 49, 494-513(2023).

     WANG Y N, JIANG Y M, JIANG J et al. Key technologies of robot perception and control and its intelligent manufacturing applications[J]. Acta Automatica Sinica, 49, 494-513(2023).

[2] YU Y W, WANG K, DU L Q et al. Matching point pair optimization registration method for point cloud model[J]. Opt. Precision Eng., 31, 503-516(2023).

     余永维, 王康, 杜柳青. 点云模型的匹配点对优化配准[J]. 光学 精密工程, 31, 503-516(2023).

[3] 李博洋, 刘思健, 崔明月. 基于最小回环检测的多车协同SLAM框架[J]. 电子学报, 49, 2241-2250(2021).

     LI B Y, LIU S J, CUI M Y et al. Multi-vehicle collaborative SLAM framework for minimum loop detection[J]. Acta Electronica Sinica, 49, 2241-2250(2021).

[4] CHARLES R Q, HAO S, MO K C et al. PointNet： Deep Learning on Point Sets for 3d Classification and Segmentation[C], 77-85(2017).

[5] YEW Z J, LEE G H. RPM-net： Robust Point Matching Using Learned Features[C], 11821-11830(2020).

[6] LI J H, ZHANG C H, XU Z Y et al. Iterative distance-aware similarity matrix convolution with mutual-supervised point elimination for efficient point cloud registration[C], 378-394(2020).

[7] HUANG S Y, GOJCIC Z, USVYATSOV M et al. PREDATOR： registration of 3d point clouds with low overlap[C], 4265-4274(2021).

[8] ZHU L F, LIU D R, LIN C W et al. Point Cloud Registration Using Representative Overlapping Points[webpage]. 2021： arXiv, 2107-02583. https：//doi.org/10.4850/

[9] XU H, LIU S C, WANG G F et al. OMNet： learning overlapping mask for partial-to-partial point cloud registration[C], 3112-3121(2021).

[10] LI S K, YE Y, LIU J Y et al. VPRNet： virtual points registration network for partial-to-partial point cloud registration[J]. Remote Sensing, 14, 2559(2022).

[11] CHEN Z L, CHEN H H, GONG L N et al. UTOPIC： uncertainty-aware overlap prediction network for partial point cloud registration[J]. Computer Graphics Forum, 41, 87-98(2022).

[12] TAN B Y, QIN H X, ZHANG X X et al. Using multi-level consistency learning for partial-to-partial point cloud registration[J]. IEEE Transactions on Visualization and Computer Graphics, 30, 4881-4894(2024).

[13] WU Y, YAO Q L, FAN X L et al. PANet： a point-attention based multi-scale feature fusion network for point cloud registration[J]. IEEE Transactions on Instrumentation and Measurement, 72, 2512913(2023).

[14] KADAM P, ZHANG M, LIU S et al. R-PointHop： a green， accurate， and unsupervised point cloud registration method[J]. IEEE Transactions on Image Processing： a Publication of the IEEE Signal Processing Society, 31, 2710-2725(2022).

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

[16] ZHOU Q Y, PARK J, KOLTUN V. Fast global registration[C], 766-782(2016).

[17] YUAN W T, ECKART B, KIM K et al. DeepGMR： learning latent gaussian mixture models for registration[C], 733-750(2020).

[18] CHEN Y, WANG Y, LI J L et al. A partial-to-partial point cloud registration method based on geometric attention network[J]. Journal of Sensors, 2023, 3427758(2023).

[19] ZHANG H B, HAI L Q, SUN H R et al. GTGMM： geometry transformer and Gaussian Mixture Models for robust point cloud registration[J]. Multimedia Tools and Applications, 83, 79757-79783(2024).

[20] YEW Z J, LEE G H. REGTR： End-to-end point cloud correspondences with transformers[C], 6667-6676(2022).

[21] MEI G F, TANG H, HUANG X S et al. Unsupervised deep probabilistic approach for partial point cloud registration[C], 13611-13620(2023).

[22] WEI J X, CHENG L, HU Z M et al. HPNet： high precision point cloud registration using feature pyramid and hybrid position encoding[J]. Computers & Graphics, 119, 103896(2024).

[23] BAUER D, PATTEN T, VINCZE M. Reagent： point cloud registration using imitation and reinforcement learning[C], 14581-14589(2021).

[24] SUN Z L, ZHANG Y X, CHEN X. Partial point cloud registration based on dilated graph convolution and outlier filtering[J]. Computer Engineering and Applications, 58, 186-194(2022).

     孙战里, 张玉欣, 陈霞. 基于膨胀图卷积与离群点过滤的残缺点云配准[J]. 计算机工程与应用, 58, 186-194(2022).

[25] HU J H, WANG F. Research on partial overlapping point cloud registration algorithm for matching geometric features[J]. Computer Engineering and Applications：, -2023. http：//kns.cnki.net/kcms/detail/11.2127.TP.20230718.1820.012.html.

     胡江豪, 王丰. 匹配几何特征的部分重叠点云配准算法研究[J]. 计算机工程与应用：, -2023. http：//kns.c nki.net/kcms/detail/11.2127.TP.20230718.1820.012.html

[26] ZHANG Y X, SUN Z L, ZENG Z G et al. Partial point cloud registration with deep local feature[J]. IEEE Transactions on Artificial Intelligence, 4, 1317-1327(2023).

[27] SONG Y N, SHEN W M, PENG K K. A novel partial point cloud registration method based on graph attention network[J]. The Visual Computer, 39, 1109-1120(2023).

[28] YANG J L, LI H D, CAMPBELL D et al. Go-ICP： a globally optimal solution to 3D ICP point-set registration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 2241-2254(2016).

[29] WANG Y, SOLOMON J. Deep Closest Point： Learning Representations for Point Cloud Registration[C], 3522-3531(2019).

[30] YANG J Q, ZHANG S K, FAN S C et al. Survey on multi-view point cloud registration algorithm[J]. Journal of Huazhong University of Science and Technology （Natural Science Edition）, 50, 16-34， 43(2022).

     杨佳琪, 张世坤, 范世超. 多视图点云配准算法综述[J]. 华中科技大学学报（自然科学版）, 50, 16-34， 43(2022).

[31] WANG H Y, LIU X, KANG W et al. Multi-features guidance network for partial-to-partial point cloud registration[J]. Neural Computing and Applications, 34, 1623-1634(2022).

[32] XU H, YE N J, LIU G H et al. FINet： dual branches feature interaction for partial-to-partial point cloud registration[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 36, 2848-2856(2022).

[33] MEI G F, POIESI F, SALTORI C et al. Overlap-guided gaussian mixture models for point cloud registration[C], 4500-4509(2023).

[34] FU K X, LUO J Z, LUO X Y et al. Robust point cloud registration framework based on deep graph matching[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 6183-6195(2023).

[35] QIN Z, YU H, WANG C J et al. GeoTransformer： fast and robust point cloud registration with geometric transformer[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 9806-9821(2023).

[36] WANG L P, YANG B, YE H L et al. Two-view point cloud registration network： feature and geometry[J]. Applied Intelligence, 54, 3135-3151(2024).

[37] MENG X H, ZHU L, YE H L et al. A new method for two-stage partial-to-partial 3D point cloud registration： multi-level interaction perception[J]. International Journal of Machine Learning and Cybernetics, 14, 3765-3781(2023).

[38] AOKI Y, GOFORTH H, SRIVATSAN R A et al. PointNetLK： Robust & Efficient point cloud registration using PointNet[C], 7156-7165(2019).

[39] ZHANG Y X, SUN Z L, ZENG Z G et al. Point cloud registration using multiattention mechanism and deep hybrid features[J]. IEEE Intelligent Systems, 38, 58-68(2023).