Point Cloud Based Three-Dimensional Reconstruction and Identification of Initial Welding Position

被引:27
作者
Zhang, Lunzhao [1 ]
Xu, Yanling [1 ]
Du, Shaofeng [3 ]
Zhao, Wenjun [3 ]
Hou, Zhen [1 ]
Chen, Shanben [1 ,2 ]
机构
[1] Shanghai Jiao Tong Univ, Sch Mat Sci & Engn, Shanghai, Peoples R China
[2] Collaborat Innovat Ctr Adv Ship & Deep Sea Explor, Shanghai, Peoples R China
[3] State Key Lab Smart Mfg Special Vehicles & Transm, Baotou, Peoples R China
来源
TRANSACTIONS ON INTELLIGENT WELDING MANUFACTURING, VOLUME I NO. 3 2017 | 2018年 / I卷 / 03期
基金
中国国家自然科学基金;
关键词
Intelligentized welding; Welding initial position; Laser vision sensor; Welding robot; Point cloud; KD-tree; Segmentation; TECHNOLOGY; VISION;
D O I
10.1007/978-981-10-8330-3_4
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Initial welding position guidance is necessary for vision-based intelligentized robotic welding. In this paper, we proposed a point cloud based approach to recognize working environment and locate welding initial position using laser stripe sensor. Calibrated laser sensor can achieve high accuracy in transforming from image coordinate system to camera coordinate system and to robot tool coordinate system with hand-eye calibration. Linear feature based image processing algorithm is developed to extract the position of laser stripe center in sub-pixel-level accuracy; then trajectory-queue based interpolation is implemented to convert down-sampled laser points to robot base coordinate system in real-time scanning. Identification of workpiece is implemented by segmenting workpieces from the point cloud data in the image. Before segmentation, KD-Tree based background model is constructed to filter out background points; then RANSAC fitting procedure rejects outliers and fits the correct workpiece plane model; and the welding initial position can be found along the weld seam which is the intersection of fitted planes. In verification experiment, workpiece planes and welding initial position can be correctly recognized despite the presence of abnormal noises.
引用
收藏
页码:61 / 77
页数:17
相关论文
共 11 条
  • [1] On Intelligentized Welding Manufacturing
    Chen, Shan-Ben
    [J]. ROBOTIC WELDING, INTELLIGENCE AND AUTOMATION, RWIA'2014, 2015, 363 : 3 - 34
  • [2] The autonomous detection and guiding of start welding position for arc welding robot
    Chen, X. Z.
    Chen, S. B.
    [J]. INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION, 2010, 37 (01): : 70 - 78
  • [3] An on-line shape-matching weld seam tracking system
    Ding, Yaoyu
    Huang, Wei
    Kovacevic, Radovan
    [J]. ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2016, 42 : 103 - 112
  • [4] Autonomous weld seam identification and localisation using eye-in-hand stereo vision for robotic arc welding
    Dinham, Mitchell
    Fang, Gu
    [J]. ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2013, 29 (05) : 288 - 301
  • [5] Du J, 2015, IEEE IMAGE PROC, P4912, DOI 10.1109/ICIP.2015.7351741
  • [6] Laser stripe peak detector for 3D scanners. A FIR filter approach
    Forest, J
    Salvi, J
    Cabruja, E
    Pous, C
    [J]. PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 3, 2004, : 646 - 649
  • [7] Calibrating a structured light stripe system: A novel approach
    Huynh, DQ
    Owens, RA
    Hartmann, PE
    [J]. INTERNATIONAL JOURNAL OF COMPUTER VISION, 1999, 33 (01) : 73 - 86
  • [8] Li X., 2017, IEEE Transactions on Affective Computing
  • [9] Wengert Christian., 2006, BILDVERARBEITUNG F R, P419
  • [10] Real-time seam tracking control technology during welding robot GTAW process based on passive vision sensor
    Xu, Yanling
    Yu, Huanwei
    Zhong, Jiyong
    Lin, Tao
    Chen, Shanben
    [J]. JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 2012, 212 (08) : 1654 - 1662