[1] L XIE, F F LEE, L LIU et al. Scene recognition： a comprehensive survey. Pattern Recognition, 102, 107205(2020).
[2] X W ZHANG, L WANG, Y SU. Visual place recognition： a survey from deep learning perspective. Pattern Recognition, 113, 107760(2021).
[3] H T ZENG, X H SONG, G W CHEN et al. Learning scene attribute for scene recognition. IEEE Transactions on Multimedia, 22, 1519-1530(2020).
[4] G CHEN, X SONG, H ZENG et al. Scene recognition with prototype-agnostic scene layout. IEEE Transactions on Image Processing, 29, 5877-5888(2020).
[5] H T ZENG, X H SONG, G W CHEN et al. Amorphous region context modeling for scene recognition. IEEE Transactions on Multimedia, 24, 141-151(2022).
[6] W MADDERN, G PASCOE, C LINEGAR et al. 1 year， 1000 km： the Oxford RobotCar dataset. The International Journal of Robotics Research, 36, 3-15(2017).
[7] X H SONG, S Q JIANG, B H WANG et al. Image representations with spatial object-to-object relations for RGB-D scene recognition. IEEE Transactions on Image Processing, 29, 525-537(2020).
[8] R DUBÉ, D DUGAS, E STUMM et al. SegMatch： Segment based place recognition in 3D point clouds, 5266-5272(2017).
[9] E FERNÁNDEZ-MORAL, W MAYOL-CUEVAS, V ARÉVALO et al. Fast place recognition with plane-based maps, 2719-2724(2013).
[10] Y F FAN, Y C HE, U X TAN. Seed： a segmentation-based egocentric 3D point cloud descriptor for loop closure detection, 5158-5163(2020).
[11] R DUBÉ, A CRAMARIUC, D DUGAS et al. SegMap： Segment-based mapping and localization using data-driven descriptors. The International Journal of Robotics Research, 39, 339-355(2020).
[12] M TOMONO. Loop detection for 3D LiDAR SLAM using segment-group matching. Advanced Robotics, 34, 1530-1544(2020).
[13] M BOSSE, R ZLOT. Place recognition using keypoint voting in large 3D lidar datasets, 2677-2684(2013).
[14] T CIESLEWSKI, E STUMM, A GAWEL et al. Point cloud descriptors for place recognition using sparse visual information, 4830-4836(2016).
[15] L HE, X L WANG, H ZHANG. M2DP： a novel 3D point cloud descriptor and its application in loop closure detection, 231-237(2016).
[16] W X ZHANG, C X XIAO. PCAN： 3D attention map learning using contextual information for point cloud based retrieval, 12428-12437(2019).
[17] Y XIA, Y S XU, S LI et al. SOE-net： a self-attention and orientation encoding network for point cloud based place recognition, 11343-11352(2021).
[19] Z LIU, S B ZHOU, C Z SUO et al. LPD-net： 3D point cloud learning for large-scale place recognition and environment analysis, 2831-2840(2019).
[20] Z LIU, C Z SUO, S B ZHOU et al. SeqLPD： sequence matching enhanced loop-closure detection based on large-scale point cloud description for self-driving vehicles, 1218-1223(2019).
[21] P YIN, F Y WANG, A EGOROV et al. SeqSphereVLAD： sequence matching enhanced orientation-invariant place recognition, 5024-5029(2020).
[22] P YIN, F Y WANG, A EGOROV et al. Fast sequence-matching enhanced viewpoint-invariant 3-D place recognition. IEEE Transactions on Industrial Electronics, 69, 2127-2135(2022).
[23] M Y CHANG, S YEON et al. SpoxelNet： spherical voxel-based deep place recognition for 3D point clouds of crowded indoor spaces, 8564-8570(2020).
[24] J KOMOROWSKI. MinkLoc3D： point cloud based large-scale place recognition, 1789-1798(2021).
[25] T X XU, Y C GUO, Y K LAI et al. TransLoc. 2021： arXiv(2105). https：//arxiv.org/abs/2105.11605
[27] M G GOLLUB, R DUBÉ, H SOMMER et al. A partitioned approach for efficient graph-based place recognition, 1-5(2017).
[28] 28廖瑞杰， 杨绍发， 孟文霞， 等. SegGraph： 室外场景三维点云闭环检测算法［J］. 计算机研究与发展， 2019， 56（2）： 338-348.LIAOR J， YANGS F， MENGW X， et al. SegGraph： an algorithm for loop-closure detection in outdoor scenes using 3D point clouds［J］. Journal of Computer Research and Development， 2019， 56（2）： 338-348.（in Chinese）
[29] E FERNÁNDEZ-MORAL, P RIVES, V ARÉVALO et al. Scene structure registration for localization and mapping. Robotics and Autonomous Systems, 75, 649-660(2016).
[30] K VIDANAPATHIRANA, P MOGHADAM, B HARWOOD et al. Locus： LiDAR-based place recognition using spatiotemporal higher-order pooling. 2021 IEEE International Conference on Robotics and Automation （ICRA）, 5075-5081(2021).
[31] D ROZENBERSZKI, A L MAJDIK. LOL： Lidar-only Odometry and Localization in 3D point cloud maps, 4379-4385(2020).
[32] A E JOHNSON, M HEBERT. Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21, 433-449(1999).
[33] S SALTI, F TOMBARI, L DI STEFANO. SHOT： Unique signatures of histograms for surface and texture description. Computer Vision and Image Understanding, 125, 251-264(2014).
[34] F TOMBARI, S SALTI, L D STEFANO. Unique shape context for 3d data description, 57-62(2010).
[35] R B RUSU, N BLODOW, M BEETZ. Fast point feature histograms （FPFH） for 3D registration, 3212-3217(2009).
[36] A ALDOMA, M VINCZE, N BLODOW et al. CAD-model recognition and 6DOF pose estimation using 3D cues, 585-592(2011).
[37] G H FENG, Y LIU, Y Y LIAO. LOIND： an illumination and scale invariant RGB-D descriptor, 1893-1898(2015).
[38] K B LOGOGLU, S KALKAN, A TEMIZEL. CoSPAIR： Colored Histograms of Spatial Concentric Surflet-Pairs for 3D object recognition. Robotics and Autonomous Systems, 75, 558-570(2016).
[39] M F YU, L ZHANG, W F WANG et al. Loop closure detection by using global and local features with photometric and viewpoint invariance. IEEE Transactions on Image Processing： a Publication of the IEEE Signal Processing Society, 30, 8873-8885(2021).
[40] L PERDOMO, D PITTOL, M MANTELLI et al. C-M2DP： a fast point cloud descriptor with color information to perform loop closure detection, 1145-1150(2019).
[41] T RÖHLING, J MACK, D SCHULZ. A fast histogram-based similarity measure for detecting loop closures in 3-D LIDAR data, 736-741(2015).
[42] D L RIZZINI. Place recognition of 3D landmarks based on geometric relations, 648-654(2017).
[43] D L RIZZINI, F GALASSO, S CASELLI. Geometric relation distribution for place recognition. IEEE Robotics and Automation Letters, 4, 523-529(2019).
[44] G KIM, A KIM. Scan context： egocentric spatial descriptor for place recognition within 3D point cloud map, 4802-4809(2018).
[45] G KIM, B PARK, A KIM. 1-day learning， 1-year localization： long-term LiDAR localization using scan context image. IEEE Robotics and Automation Letters, 4, 1948-1955(2019).
[46] H WANG, C WANG, L H XIE. Intensity scan context： coding intensity and geometry relations for loop closure detection, 2095-2101(2020).
[47] L LI, X KONG, X R ZHAO et al. SSC： semantic scan context for large-scale place recognition, 2092-2099(2021).
[48] S SIVA, Z NAHMAN, H ZHANG. Voxel-based representation learning for place recognition based on 3D point clouds, 8351-8357(2020).
[49] J D GUO, P V K BORGES, C PARK et al. Local descriptor for robust place recognition using LiDAR intensity. IEEE Robotics and Automation Letters, 4, 1470-1477(2019).
[50] W J SHI, R RAJKUMAR. Point-GNN： graph neural network for 3D object detection in a point cloud, 1708-1716(2020).
[51] 51王张飞， 刘春阳， 隋新， 等. 基于深度投影的三维点云目标分割和碰撞检测［J］. 光学 精密工程， 2020， 28（7）： 1600-1608. doi: 10.37188/OPE.20202807.1600WANGZ F， LIUC Y， SUIX， et al. Three-dimensional point cloud object segmentation and collision detection based on depth projection［J］. Opt. Precision Eng.， 2020， 28（7）： 1600-1608.（in Chinese）. doi: 10.37188/OPE.20202807.1600
[52] R H LI, X Z LI, P A HENG et al. PointAugment： an auto-augmentation framework for point cloud classification, 6377-6386(2020).
[53] Y WANG, Y B SUN, Z W LIU et al. Dynamic graph CNN for learning on point clouds. ACM Transactions on Graphics, 38, 1-12(2019).
[54] 54杨军， 党吉圣. 采用深度级联卷积神经网络的三维点云识别与分割［J］. 光学 精密工程， 2020， 28（5）： 1187-1199.YANGJ， DANGJ S. Recognition and segmentation of three-dimensional point cloud based on deep cascade convolutional neural network［J］. Opt. Precision Eng.， 2020， 28（5）： 1187-1199.（in Chinese）
[55] 55赵传， 张保明， 余东行， 等. 利用迁移学习的机载激光雷达点云分类［J］. 光学 精密工程， 2019， 27（7）： 1601-1612. doi: 10.3788/OPE.20192707.1601ZHAOC， ZHANGB M， YUD H， et al. Airborne LiDAR point cloud classification using transfer learning［J］. Opt. Precision Eng.， 2019， 27（7）： 1601-1612.（in Chinese）. doi: 10.3788/OPE.20192707.1601
[56] R Q CHARLES, S HAO, K C MO et al. PointNet： deep learning on point sets for 3D classification and segmentation, 77-85(2017).
[57] C R QI, L YI, H SU et al. Pointnet++： Deep hierarchical feature learning on point sets in a metric space. Advances in Neural Information Processing Systems., 5099-5108(2017).
[58] M A UY, G H LEE. PointNetVLAD： deep point cloud based retrieval for large-scale place recognition, 4470-4479(2018).
[59] Q SUN, H Y LIU, J HE et al. DAGC： employing dual attention and graph convolution for point cloud based place recognition, 224-232(2020).
[60] Z X FAN, H Y LIU, J HE et al. SRNet： a 3D scene recognition network using static graph and dense semantic fusion. Computer Graphics Forum, 39, 301-311(2020).
[61] X KONG, X M YANG, G Y ZHAI et al. Semantic graph based place recognition for 3D point clouds, 8216-8223(2020).
[63] L HUI, M M CHENG, J XIE et al. Efficient 3D point cloud feature learning for large-scale place recognition. IEEE Transactions on Image Processing, 31, 1258-1270(2022).
[64] J DU, R WANG, D CREMERS. DH3D： deep hierarchical 3D descriptors for robust large-scale 6DoF relocalization, 744-762(2020).
[65] Y S GONG, F C SUN, J YUAN et al. A two-level framework for place recognition with 3D LiDAR based on spatial relation graph. Pattern Recognition, 120, 108171(2021).
[66] Z C ZHOU, C ZHAO, D ADOLFSSON et al. NDT-transformer： large-scale 3D point cloud localisation using the normal distribution transform representation. 2021 IEEE International Conference on Robotics and Automation （ICRA）, 5654-5660(2021).
[67] L HUI, H YANG, M M CHENG et al. Pyramid point cloud transformer for large-scale place recognition, 6078-6087(2021).
[68] R ARANDJELOVIC, P GRONAT, A TORII et al. NetVLAD： CNN architecture for weakly supervised place recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 1437-1451(2018).
[69] A VASWANI, N SHAZEER, N PARMAR et al. Attention is all you need. In Advances in neural information processing systems, 5998-6008(2017).
[70] A GEIGER, P LENZ, R URTASUN. Are we ready for autonomous driving？, 3354-3361(2012).
[71] A GEIGER, P LENZ, C STILLER et al. Vision meets robotics： the KITTI dataset. The International Journal of Robotics Research, 32, 1231-1237(2013).
[72] N CARLEVARIS-BIANCO, A K USHANI, R M EUSTICE. University of Michigan north campus long-term vision and lidar dataset. The International Journal of Robotics Research, 35, 1023-1035(2016).
[73] G KIM, Y S PARK, Y CHO et al. MulRan： multimodal range dataset for urban place recognition, 6246-6253(2020).
[74] G PANDEY, J R MCBRIDE, R M EUSTICE. Ford Campus vision and lidar data set. The International Journal of Robotics Research, 30, 1543-1552(2011).
[75] C Z SUO, Z LIU, L F MO et al. LPD-AE： latent space representation of large-scale 3D point cloud. IEEE Access, 8, 108402-108417(2020).
[76] 76杨必胜， 董震. 点云智能研究进展与趋势［J］. 测绘学报， 2019， 48（12）： 1575-1585.YANGB S， DONGZ. Progress and perspective of point cloud intelligence［J］. Acta Geodaetica et Cartographica Sinica， 2019， 48（12）： 1575-1585.（in Chinese）
