• Opto-Electronic Engineering
  • Vol. 45, Issue 12, 180266 (2018)
Zhao Kai1, Xu Youchun2, and Wang Rendong1
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • show less
    DOI: 10.12086/oee.2018.180266 Cite this Article
    Zhao Kai, Xu Youchun, Wang Rendong. A preprocessing method of 3D point clouds registration in urban environments[J]. Opto-Electronic Engineering, 2018, 45(12): 180266 Copy Citation Text show less
    References

    [1] Kim J U, Kang H B. LiDAR Based 3D object detection using CCD information[C]//IEEE Third International Conference on Multimedia Big Data, 2017: 303–309.

    [2] Han D B, Xu Y C, Li H, et al. Calibration of extrinsic parameters for three‐dimensional lidar based on hand‐eye model[J]. Opto- Electronic Engineering, 2017, 44(8): 798–804.

    [3] Biosca J M, Lerma J L. Unsupervised robust planar segmentation of terrestrial laser scanner point clouds based on fuzzy clustering methods[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2008, 63(1): 84–98.

    [4] Himmelsbach M, Hundelshausen F V, Wuensche H J. Fast segmentation of 3D point clouds for ground vehicles[C]// Proceedings of 2010 IEEE Intelligent Vehicles Symposium, 2010: 560–565.

    [5] Moosmann F, Pink O, Stiller C. Segmentation of 3D lidar data in non-flat urban environments using a local convexity criterion[C]// Proceedings of 2009 IEEE Intelligent Vehicles Symposium, 2009: 215–220.

    [6] Zhang M F, Fu R, Guo Y S, et al. Road segmentation method based on irregular three dimensional point cloud[J]. Journal of Jilin University (Engineering and Technology Edition), 2017, 47(5): 1387–1394.

    [7] Fleishman S, Drori I, Cohen-Or D. Bilateral mesh denoising[J]. ACM Transactions on Graphics, 2003, 22(3): 950–953.

    [8] Li R Z, Yang M, Ran Y, et al. Point cloud denoising and simplification algorithm based on method library[J]. Laser & Optoelectronics Progress, 2018, 55(1): 011008.

    [9] Su B Y, Ma J Y, Peng Y S, et al. Algorithm for RGBD point cloud denoising and simplification based on K-means clustering[J]. Journal of System Simulation, 2016, 28(10): 2329–2334, 2341.

    [10] Siciliano B, Khatib O. Springer Handbook of Robotics[M]. Berlin, Heidelberg: Springer-Verlag, 2007.

    [11] Ester M, Kriegel H P, Sander J, et al. A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise[C]//Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, 1996: 226–231.

    [12] Rusu R B, Cousins S. 3D is here: Point Cloud Library (PCL)[C]// Proceedings of IEEE International Conference on Robotics and Automation, 2011: 1–4.

    CLP Journals

    [1] [in Chinese], [in Chinese], [in Chinese]. Ground segmentation from 3D point cloud using features of scanning line segments[J]. Opto-Electronic Engineering, 2019, 46(7): 180268

    Zhao Kai, Xu Youchun, Wang Rendong. A preprocessing method of 3D point clouds registration in urban environments[J]. Opto-Electronic Engineering, 2018, 45(12): 180266
    Download Citation