TY - GEN
T1 - An automatic laser scanning system for objects with unknown model
AU - Yang, Yipeng
AU - Li, Zhan
AU - Li, Zhaoting
AU - Yang, Liu
AU - Yan, Yingxin
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - Laser scanning has been widely used in the industrial manufacturing, especially in reverse engineering, quality control and surface defect detection. Achieving automatic scanning can succeed in reducing production costs and increasing production efficiency. This paper proposes an automatic intelligent scanning system based on ROS, including robot arm, RGBD camera and line laser scanner. The system is designed to scan objects with unknown model and we use high precision calibration methods to improve the accuracy of the system. A novel scanning trajectory planning strategy is developed, including three steps. Firstly we plan preliminary trajectory based on the RGBD camera data. Then the robot arm pose is adjusted with PID controller in real time to optimize the scanning results. Finally, according to the distribution of point cloud density, the portion whose density is lower than the threshold will be re-scanned. The experiment we illustrate shows the feasibility and high accuracy of our system.
AB - Laser scanning has been widely used in the industrial manufacturing, especially in reverse engineering, quality control and surface defect detection. Achieving automatic scanning can succeed in reducing production costs and increasing production efficiency. This paper proposes an automatic intelligent scanning system based on ROS, including robot arm, RGBD camera and line laser scanner. The system is designed to scan objects with unknown model and we use high precision calibration methods to improve the accuracy of the system. A novel scanning trajectory planning strategy is developed, including three steps. Firstly we plan preliminary trajectory based on the RGBD camera data. Then the robot arm pose is adjusted with PID controller in real time to optimize the scanning results. Finally, according to the distribution of point cloud density, the portion whose density is lower than the threshold will be re-scanned. The experiment we illustrate shows the feasibility and high accuracy of our system.
KW - Automatic scanning
KW - Hand-eye calibration
KW - Point cloud processing
KW - Trajectory planning
UR - https://www.scopus.com/pages/publications/85083571784
U2 - 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00057
DO - 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00057
M3 - 会议稿件
AN - SCOPUS:85083571784
T3 - Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019
SP - 82
EP - 87
BT - Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019
Y2 - 19 August 2019 through 23 August 2019
ER -