Authors:
Clément Dubosq
and
Andréa Guerrero
Affiliation:
Capgemini Engineering R&I France, 4 avenue Didier Daurat, Blagnac, France
Keyword(s):
Point Cloud Registration, 3D Model, 3D Matching, ICP, Monte-Carlo Metropolis Hastings, Point Cloud Processing, Defect Detection, Automatized Inspection.
Abstract:
Currently in industry, inspection tasks are essential to ensure a product efficacity and reliability. Some automated tools to inspect, i.e. to detect defect exist, but they are not adapted to an industrial inspection application. Most of industrial inspection is human made. In this article, we propose a new algorithm to match a 3D point-cloud to its 3D reference to track visual defects. First, we reconstruct a 3D model of an object using Iterative Closest Points (ICP) algorithm. Then, we propose an ICP initialization based on a Monte Carlo Metropolis-Hasting optimization to match a partial point-cloud to its model. We applied our algorithm to the data measured from a Time-of-Flight sensor and a RGB camera. We present the results and performance of this approach for objects of different complexities and sizes. The proposed methodology shows good results and adaptability compared to a state-of-the-art method called Go-ICP.