An integrated 3D vision sensor system with adaptive point cloud processing for robotic workpiece localization in industrial bin picking
TL;DR
A real-time point cloud processing and workpiece localization system that integrates multi-module optimization with a dynamic adaptive framework that sustains reliable performance under Gaussian noise up to 0.6 mm and occlusion levels up to 40%, confirming its viability for high-throughput industrial applications.
Abstract
The transition from automated to intelligent manufacturing increasingly relies on three-dimensional (3D) machine vision for robot guidance. Nevertheless, existing 3D vision systems still suffer from long processing delays, poor adaptability to changing environments, and inadequate pose accuracy in real industrial settings. To address these issues, this paper proposes a real-time point cloud processing and workpiece localization system that integrates multi-module optimization with a dynamic adaptive framework. The system acquires data through binocular stereo vision and incorporates optimized spatial filtering, PCA-based dimensionality reduction, and a machine learning-enhanced FAST feature detector. A hand-eye calibration model that includes distortion compensation achieves sub-millimeter mapping from image coordinates to the robot workspace. Experimental results on the public LineMOD dataset and a custom industrial bin-picking dataset show that the proposed system attains a 99% grasping success rate under favorable lighting and 96% under challenging conditions, with an average cycle time of 520 ms. Translation error reaches 0.38 mm under good lighting (rotation error: 0.61°, ADD: 0.47 mm).Comparative evaluations confirm substantial gains in speed, accuracy, and environmental robustness relative to existing commercial and academic systems. Furthermore, the system sustains reliable performance under Gaussian noise up to 0.6 mm and occlusion levels up to 40%, confirming its viability for high-throughput industrial applications.