Welding Line Detection Using Point Clouds from Optimal Shooting Position
Tomohito Takubo, Erika Miyake, Atsushi Ueno, and Masaki Kubo
Osaka Metropolitan University
3-3-138 Sugimoto, Sumiyoshi-ku, Osaka 558-8585, Japan
A method for welding line detection using point cloud data is proposed to automate welding operations combined with a contact sensor. The proposed system targets a fillet weld, in which the joint line between two metal plates attached vertically is welded. In the proposed method, after detecting the position and orientation of two flat plates regarding a single viewpoint as a rough measurement, the flat plates are measured from the optimal shooting position in each plane in detail to detect a precise weld line. When measuring a flat plate from an angle, the 3D point cloud obtained by a depth camera contains measurement errors. For example, a point cloud measuring a plane has a wavy shape or void owing to light reflection. However, by shooting the plane vertically, the point cloud has fewer errors. Using these characteristics, a two-step measurement algorithm for determining weld lines was proposed. The weld line detection results show an improvement of 5 mm compared with the rough and precise measurements. Furthermore, the average measurement error was less than 2.5 mm, and it is possible to narrow the range of the search object contact sensor for welding automation.
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