Run length encoding based weld seam detection from point clouds of ship stiffened panel

Abstract

A vision-based weld seam detector is crucial for intelligent welding in ship stiffened panel as it influences the working efficiency of welding robots. This paper introduces an innovative Run Length Encoding (RLE) based Line Segment Detector (RSD) that is capable of extracting line segments from point clouds. The RLE is firstly employed to compress point cloud data by encoding them into runs. Then, the directional feature of significant line segments is determined according to the analysis of level line angles, facilitating a global line segment detection mechanism. This enables RSD to capture more complete line segments compared to region-based detection methods. Notably, this method obviates the need for manual parameter adjustments and does not require any prior information. The effectiveness and superiority of RSD are confirmed by testing various workpieces in a laboratory setting and comparing its performance with other detectors.