[1] He Y B, Chen R L, Wu K et al. Point cloud simplification method based on k-means clustering[J]. Laser & Optoelectronics Progress, 56, 091002(2019).

[2] Zhang C H. Clustering and tracking method research of lidar-based obstacle in urban environment[D](2019).

[3] Yang C. A study on obstacle detection method with unpiloted vehicle[D](2015).

[4] Xu G Y, Niu H, Guo C Y et al. Research on target recognition and tracking based on 3D laser point cloud[J]. Automotive Engineering, 42, 38-46(2020).

[5] Charles R Q, Hao S, Mo K C et al. PointNet: deep learning on point sets for 3D classification and segmentation[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA, 77-85(2017).

[6] Zhou Y, Tuzel O. VoxelNet: end-to-end learning for point cloud based 3D object detection[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA, 4490-4499(2018).

[7] Yan Y, Mao Y X, Li B. SECOND: sparsely embedded convolutional detection[J]. Sensors, 18, 3337(2018).

[8] Lang A H, Vora S, Caesar H et al. PointPillars: fast encoders for object detection from point clouds[C]. //2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA, 12689-12697(2019).

[9] Shi S S, Wang X G, Li H S. PointRCNN: 3D object proposal generation and detection from point cloud[C]. //2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA, 770-779(2019).

[10] Lou X Y, Wang H, Cai Y F et al. A research on an algorithm for real-time detection and classification of road obstacle by using 64-line lidar[J]. Automotive Engineering, 41, 779-784(2019).

[11] Chu P, Cho S, Sim S et al. A fast ground segmentation method for 3D point cloud[J]. Journal of Information Processing Systems, 13, 491-499(2017).

[12] Wang X, Wang J Q, Li K Q et al. Fast segmentation of 3-D point clouds for intelligent vehicles[J]. Journal of Tsinghua University (Science and Technology), 54, 1440-1446(2014).

[13] Cortinhal T, Tzelepis G, Erdal A E. SalsaNext: fast, uncertainty-aware semantic segmentation of LiDAR point clouds for autonomous driving[EB/OL]. (2020-03-07)[2021-04-10]. https://arxiv.org/abs/2003.03653

[14] Fan X H, Xu G L, Li W L et al. Target segmentation method for three-dimensional LiDAR point cloud based on depth image[J]. Chinese Journal of Lasers, 46, 0710002(2019).

[15] Li J, Zhao K, Bai R et al. Urban ground segmentation algorithm based on ray slope threshold[J]. Acta Optica Sinica, 39, 0928004(2019).

[16] Shao J T, Du C Q, Zou B. Lidar ground segmentation method based on point cloud cluster combination feature[J]. Laser & Optoelectronics Progress, 58, 0428001(2021).

[17] Sualeh M, Kim G W. Dynamic multi-LiDAR based multiple object detection and tracking[J]. Sensors, 19, 1474(2019).

[18] Ye Y T, Fu L M, Li B J. Object detection and tracking using multi-layer laser for autonomous urban driving[C]. //2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), November 1-4, 2016, Rio de Janeiro, Brazil., 259-264(2016).

[19] Zermas D, Izzat I, Papanikolopoulos N. Fast segmentation of 3D point clouds: a paradigm on LiDAR data for autonomous vehicle applications[C]. //2017 IEEE International Conference on Robotics and Automation (ICRA), May 29-June 3, 2017, Singapore, 5067-5073(2017).

[20] Douillard B, Underwood J, Vlaskine V et al. A pipeline for the segmentation and classification of 3D point clouds[M]. //Experimental robotics, 585-600(2014).

[21] Lu X H, Yao J, Tu J G et al. Pairwise linkage for point cloud segmentation[J]. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, III-3, 201-208(2016).

[22] Börcs A, Nagy B, Benedek C. Fast 3D urban object detection on streaming point clouds[M]. //Agapito L, Bronstein M M, Rother C. Computer vision-ECCV 2014 Workshops. Lecture notes in computer science, 8926, 628-639(2015).

[23] Lehtomäki M, Jaakkola A, Hyyppä J et al. Object classification and recognition from mobile laser scanning point clouds in a road environment[J]. IEEE Transactions on Geoscience and Remote Sensing, 54, 1226-1239(2016).

[24] Ye Y T, Li B J, Fu L M. Fast object detection and tracking in laser data for autonomous driving[J]. Geomatics and Information Science of Wuhan University, 44, 139-144, 152(2019).

[25] Li W M, Liu C. Research on improved PCA-based ICP point cloud registration algorithm[J]. Industrial Control Computer, 33, 11-13(2020).