Volume 36 Issue 3
Jun.  2022
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YANG Hao, NING Yeyan, FANG Yu, LI Haoyu, YANG Yunjie. Distortion compensation method for high-precision point cloud model[J]. Journal of Shanghai University of Engineering Science, 2022, 36(3): 278-283. doi: 10.12299/jsues.21-0249
Citation: YANG Hao, NING Yeyan, FANG Yu, LI Haoyu, YANG Yunjie. Distortion compensation method for high-precision point cloud model[J]. Journal of Shanghai University of Engineering Science, 2022, 36(3): 278-283. doi: 10.12299/jsues.21-0249

Distortion compensation method for high-precision point cloud model

doi: 10.12299/jsues.21-0249
  • Received Date: 2021-11-12
  • Publish Date: 2022-06-30
  • Accurate acquisition and distortion compensation of point cloud model was the key to 3D laser scanning technology for inspection of parts. A method of obtaining high-precision 3D point cloud model by distortion compensation is proposed. The 3D point cloud model of parts was reconstructed by using a line laser, and the distortion compensation was applied to the included angle error in the model to achieve high-precision point cloud data acquisition. The test platform was established, and the test objects with different materials and structures were selected such as instrument parts, double-layer hole parts and grid components. Through comparative analysis, it has been found that the root-mean-square differences after the distortion compensation were reduced by 0.009, 0.036 and 0.024 mm respectively. The results verified the effectiveness of the distortion compensation method for the point cloud model and its good generality.

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  • [1]
    BUONAMICI F, CARFAGNI M, FURFERI R, et al. Reverse engineering modeling methods and tools: A survey[J] . Computer-Aided Design and Applications,2018,15(3):443 − 464. doi: 10.1080/16864360.2017.1397894
    [2]
    HELLE R H, LEMU H G. A case study on use of 3D scanning for reverse engineering and quality control[J] . Materials Today:Proceedings,2021,45(6):5255 − 5262.
    [3]
    YU F, WEI Y X, YU H G. Research on target recognition method based on laser point cloud data[J] . Cyber Security Intelligence and Analytics,2020,928:1305 − 1310.
    [4]
    CHAO W, YONG K C. Smart scanning and near real-time 3D surface modeling of dynamic construction equipment from a point cloud[J] . Automation in Construction,2015,49(B):239 − 249.
    [5]
    NGUYEN C, CHOI Y. Comparison of point cloud data and 3D CAD data for on-site dimensional inspection of industrial plant piping systems[J] . Automation in Construction,2018,91:44 − 52. doi: 10.1016/j.autcon.2018.03.008
    [6]
    JOVANCEVIC I, PHAM H H, ORTEU J J, et al. 3D point cloud analysis for detection and characterization of defects on airplane exterior surface[J] . Journal of Nondestructive Evaluation,2017,36(4):74. doi: 10.1007/s10921-017-0453-1
    [7]
    XIAO J H, ZHANG J H, ADLER B, et al. Three-dimensional point cloud plane segmentation in both structured and unstructured environments[J] . Robotics and Autonomous Systems,2013,61(12):1641 − 1652. doi: 10.1016/j.robot.2013.07.001
    [8]
    WANG J, LI L P, SHI S S, et al. Fine exploration and control of subway crossing karst area[J] . Applied Sciences,2019,9(13):2588. doi: 10.3390/app9132588
    [9]
    CALIGNANO F, VEZZETTI E. Soft tissue diagnosis in maxillofacial surgery: A preliminary study on three-dimensional face geometrical features-based analysis[J] . Aesthetic Plastic Surgery,2010,34(2):200 − 211. doi: 10.1007/s00266-009-9410-4
    [10]
    FARAHANI N, BRAUN A, JUTT D, et al. Three-dimensional imaging and scanning: current and future applications for pathology[J] . Journal of Pathology Informatics,2017,8(1):1 − 10. doi: 10.4103/jpi.jpi_47_16
    [11]
    SUN S P, LI C Y, CHEE P W, et al. Three-dimensional photogrammetric mapping of cotton bolls in situ based on point cloud segmentation and clustering[J] . ISPRS Journal of Photogrammetry and Remote Sensing,2020,160:195 − 207. doi: 10.1016/j.isprsjprs.2019.12.011
    [12]
    WANG Y W, CHEN Y F. Non-destructive measurement of three-dimensional plants based on point cloud[J] . Plants,2020,9(5):571. doi: 10.3390/plants9050571
    [13]
    LI Y D, GU P H. Automatic localization and comparison for free-form surface inspection[J] . Journal of Manufacturing Systems,2006,25(4):251 − 268. doi: 10.1016/S0278-6125(08)00007-1
    [14]
    WANG J J, XU L J, LI X L, et al. A proposal to compensate platform attitude deviation's impact on laser point cloud from airborne LiDAR[J] . IEEE Transactions on Instrumentation & Measurement,2013,62(9):2549 − 2558.
    [15]
    BARNFATHER J D, ABRAM T. Efficient compensation of dimensional errors in robotic machining using imperfect point cloud part inspection data[J] . Measurement,2018,117:176 − 185. doi: 10.1016/j.measurement.2017.12.021
    [16]
    GONG C, YIN X, LIANG J, et al. A compensation filter method for extracting surface characteristic from optical point cloud [J]. Measurement Science and Technology, 2019, 31(2). DOI: 10.1088/1361-6501/ab42f0.
    [17]
    BALLIT A, MOUGHARBEL I, GHAZIRI H, et al. Visual sensor fusion with error compensation strategy toward a rapid and low-cost 3D scanning system for the lower residual limb[J] . IEEE Sensors Journal,2020,20(24):15043 − 15052. doi: 10.1109/JSEN.2020.3011172
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