[1] Xie D S, Xu Y C, Wang R D et al. Obstacle detection and tracking for unmanned vehicles based on 3D laser radar[J]. Automotive Engineering, 40, 952-959(2018).

[2] Douillard B, Underwood J, Kuntz N et al. On the segmentation of 3D LIDAR point clouds. [C]∥2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011. Shanghai, China: IEEE, 2798-2805(2011).

[3] He Y F, Hu R, Zou J G[J]. Analysis on point cloud of building facade segmentation algorithms based on 3D laser scanning data Bulletin of Surveying and Mapping, 2019, 26-31.

[4] Huang K, Cheng X J, Jia D F et al. An automatic segmentation algorithm for dense pipeline point cloud data[J]. Chinese Journal of Lasers, 45, 1104004(2018).

[5] Krizhevsky A, Sutskever I, Hinton G E . ImageNet classification with deep convolutional neural networks. [C]∥Proceedings of the 25th Conference and Workshop on Neural Information Processing Systems. [S.l.:s.n.], 1097-1105(2012).

[6] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016. Las Vegas, NV, USA: IEEE, 770-778(2016).

[7] 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: IEEE, 652-660(2017).

[8] Qi C R, Yi L, Su H et al. PointNet++: deep hierarchical feature learning on point sets in a metric space. [C]∥Advances in Neural Information Processing Systems.Long Beach, USA:[s.n.], 5099-5108(2017).

[9] Simon M, Milz S, Amende K et al[M]. Complex-YOLO:an Euler-region-proposal for real-time 3D object detection on point clouds, 197-209(2019).

[10] Wu B C, Wan A, Yue X Y et al. SqueezeSeg: convolutional neural nets with recurrent CRF for real-time road-object segmentation from 3D LiDAR point cloud. [C]∥2018 IEEE International Conference on Robotics and Automation (ICRA), May 21-25, 2018. Brisbane, QLD: IEEE, 1887-1893(2018).

[11] Wang Y, Shi T Y, Yun P et al[J/OL]. PointSeg: real-time semantic segmentation based on 3D LiDAR point cloud https:∥arxiv., org/abs/1807, 06288.

[12] Wu B C, Zhou X, Zhao S C et al. SqueezeSeg V2: improved model structure and unsupervised domain adaptation for road-object segmentation from a LiDAR point cloud. [C]∥2019 International Conference on Robotics and Automation (ICRA), May 20-24, 2019. Montreal, QC, Canada: IEEE, 4376-4382(2019).

[13] Bogoslavskyi I, Stachniss C. Efficient online segmentation for sparse 3D laser scans[J]. PFG-Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 85, 41-52(2017).

[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 B, Zhang T L, Xia T[J/OL]. Vehicle detection from 3D lidar using fully convolutional network https:∥arxiv.org/pdf/1608.07916.pdf..

[16] Halterman R, Michael B. Velodyne HDL-64E lidar for unmanned surface vehicle obstacle detection[J]. Proceedings of SPIE, 7692, 76920D(2010).

[17] He M F, Zhou L S, Shen H C. Curvature estimation of scattered-point cloud data and its application[J]. Journal of Nanjing University of Aeronautics & Astronautics, 37, 515-519(2005).

[18] Zeng B, Huang W. An indoor point cloud segmentation method fusing with multi-feature cluster ensemble[J]. Computer Engineering, 44, 281-286(2018).

[19] Cai T J, Tang H. Overview of the principle of least square fitting of Sabitzky-Golay smooting filters[J]. Digital Communication, 38, 63-68, 82(2011).

[20] Yang P, Qian X. Direct computing of surface curvatures for point-set surfaces. [C]∥Symposium on Point Based Graphics. Prague, Czech Republic: [s.n.], 29-36(2007).

[21] Iandola F N, Han S, Moskewicz M et al[J/OL]. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model sizehttps:∥arxiv., org/abs/1602, 07360.

[22] Geiger A, Lenz P, Urtasun R. Are we ready for autonomous driving? The KITTI vision benchmark suite. [C]∥2012 IEEE Conference on Computer Vision and Pattern Recognition, June 16-21, 2012. Providence, RI: IEEE, 3354-3361(2012).

[23] Evaluation of clustering[EB/OL]. -04-07)[2019-11-20]. https:∥nlp.stanford.edu/IR-book/html/htmledition/evaluation-of-clustering-1.html.(2009).