Edge and Corner Detection for Unorganized 3D Point Clouds with Application to Robotic Welding

TL;DR

This paper introduces new algorithms for detecting edges and corners in unorganized 3D point clouds, with applications in robotic welding, demonstrating improved accuracy and enabling direct weld seam detection from raw data.

Contribution

The paper presents novel edge and corner detection methods for unorganized point clouds, extending their application to automatic weld seam detection in robotic welding.

Findings

Higher precision and recall in segmentation tasks

Effective detection of weld seams directly from point clouds

Comparison shows advantages over Harris 3D in weld seam detection

Abstract

In this paper, we propose novel edge and corner detection algorithms for unorganized point clouds. Our edge detection method evaluates symmetry in a local neighborhood and uses an adaptive density based threshold to differentiate 3D edge points. We extend this algorithm to propose a novel corner detector that clusters curvature vectors and uses their geometrical statistics to classify a point as corner. We perform rigorous evaluation of the algorithms on RGB-D semantic segmentation and 3D washer models from the ShapeNet dataset and report higher precision and recall scores. Finally, we also demonstrate how our edge and corner detectors can be used as a novel approach towards automatic weld seam detection for robotic welding. We propose to generate weld seams directly from a point cloud as opposed to using 3D models for offline planning of welding paths. For this application, we show a comparison between Harris 3D and our proposed approach on a panel workpiece.