An RGB-D video-based wire detection tool to aid robotic arms during machine alignment measurement

Industrial equipment may require precise alignment of components in order to function properly. In this work, we consider the machine alignment of a beamline in a research facility where an autonomous measurement system is used to execute these machine alignment procedures. The system consists of two robotic arms equipped with laser sensors placed at their ends which with the help of fiducial markers, are used by a camera system to calculate the position of the machine with respect to a stretched wire installed on top of the machines. In this work we propose a wire detection tool that assists the measurement process by detecting the line automatically through RGB-D images. Although the system was implemented for the specific application of beam line component alignment in the LHC tunnel, the same principle can be used in various other applications requiring detection of fine lines at a close distance.

[1]  Jaya S. Kulchandani,et al.  Moving object detection: Review of recent research trends , 2015, 2015 International Conference on Pervasive Computing (ICPC).

[2]  Kartik Umesh Sharma,et al.  A review and an approach for object detection in images , 2017 .

[3]  Kok Kiong Tan,et al.  Vision-based approach towards lane line detection and vehicle localization , 2015, Machine Vision and Applications.

[4]  Simone Gilardoni,et al.  LHC Train Control System for Autonomous Inspections and Measurements , 2018 .

[5]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[6]  Keshou Wu,et al.  A fast and stable lane detection method based on B-spline curve , 2009, 2009 IEEE 10th International Conference on Computer-Aided Industrial Design & Conceptual Design.

[7]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[9]  Qingfeng Zhang,et al.  Straight line detection from remote sensing images by rule-based feature fusion , 2012, Geo spatial Inf. Sci..

[10]  Ming-Hsuan Yang,et al.  Visual tracking with online Multiple Instance Learning , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[12]  Jiri Matas,et al.  Forward-Backward Error: Automatic Detection of Tracking Failures , 2010, 2010 20th International Conference on Pattern Recognition.

[13]  Irwin Sobel,et al.  An Isotropic 3×3 image gradient operator , 1990 .

[14]  Anshika Sharma,et al.  Analytical review on object segmentation and recognition , 2016, 2016 6th International Conference - Cloud System and Big Data Engineering (Confluence).

[15]  Terrence Fong,et al.  An Edge-Less Approach to Horizon Line Detection , 2015, 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA).

[16]  Rui Caseiro,et al.  Exploiting the Circulant Structure of Tracking-by-Detection with Kernels , 2012, ECCV.

[17]  Carl J. Debono,et al.  Vision-based change detection for inspection of tunnel liners , 2018, Automation in Construction.

[18]  A. Masi,et al.  LHC Collimators Low Level Control System , 2007, IEEE Transactions on Nuclear Science.

[19]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[20]  R J Steinhagen,et al.  The alignment of the LHC , 2009 .