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3D Mapping of X-Ray Images in Inspections of Aerospace Parts

Authors: Daniele; Matteo; Alberto; Michele; Carlo; Emanuele

Journal: 2020 25TH IEEE INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA)

DOI: 10.1109/ETFA46521.2020.9212135

In this work we present an industrial system for the inspection of composite parts in the aerospace industry, based on X-ray sensors and robotic manipulators. Such system is designed to identify any type of defects such as, missing gluing, core cell deformation, cracks or foreign objects, which may occur between layers of which these objects are composed. The inspection process involves back-projection of X-ray images onto the 3D CAD model of the inspected part, to directly locate the defects on the part itself. The complete system has been implemented in a real industrial workcell that involves two synchronized robots equipped with a X-ray source-detector system. The two robots move autonomously along a pre-computed trajectory without any human intervention, and the back-projection of the acquired images is efficiently performed at run-time using the proposed algorithm. The experiments demonstrate that the X-ray images back-projection is successful and can effectively replace standard manually guided inspections. This has a high impact on the factory automation cycle since it helps to reduce the effort and time needed for each inspection task. This work is part of a EU funded project called SPIRIT.

Volume: 2020- Pages: 1219-1222

Keywords: 3D metrol-ogy; Industrial inspection; Inspection of composite parts; X-ray imaging;

3D robot perception with Point Cloud Library

Authors: Matteo; Radu B.; Emanuele

Journal: ROBOTICS AND AUTONOMOUS SYSTEMS

DOI: 10.1016/j.robot.2015.12.008

Volume: 78 Pages: 97-99

3D Reconstruction of Freely Moving Persons for Re-Identification with a Depth Sensor

Authors: Matteo; Alberto; Andrea; Luc; Emanuele

Journal: 2014 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)

DOI: 10.1109/ICRA.2014.6907518

In this work, we describe a novel method for creating 3D models of persons freely moving in front of a consumer depth sensor and we show how they can be used for long-term person re-identification. For overcoming the problem of the different poses a person can assume, we exploit the information provided by skeletal tracking algorithms for warping every point cloud frame to a standard pose in real time. Then, the warped point clouds are merged together to compose the model. Re-identification is performed by matching body shapes in terms of whole point clouds warped to a standard pose with the described method. We compare this technique with a classification method based on a descriptor of skeleton features and with a mixed approach which exploits both skeleton and shape features. We report experiments on two datasets we acquired for RGB-D re-identification which use different skeletal tracking algorithms and which are made publicly available to foster research in this new research branch.

Pages: 4512-4519

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