Real-time ground marking analysis for safe trajectories of autonomous mobile robots

Bauda, Marie-Anne; Bazot, Cecile; Larnier, Stanislas

doi:10.1109/ecmsm.2017.7945887

Cited by 6 publications

(2 citation statements)

References 35 publications

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“…Air-Cobot is capable of moving safely around an aircraft in an environment that contains obstacles to avoid. This application has led to many works [6,7,8]. This robot is equipped with a Pan-Tilt-Zoom (PTZ) camera and a 3D scanner, both used for inspection.…”

Section: Previous Workmentioning

confidence: 99%

Inspection of mechanical assemblies based on 3D deep learning segmentation

Boughrara

Jovančević

Orteu

et al. 2023

Sixteenth International Conference on Quality Control by Artificial Vision

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We are focused on conformity control of complex aeronautical mechanical assemblies, typically an aircraft engine at the end or in the middle of the assembly process. Our overall system should ensure that all the mechanical parts are present and well-mounted. A 3D scanner carried by a robot arm provides acquisitions of 3D point clouds which are further processed. Computer-Aided Design (CAD) model of the mechanical assembly is available. In this paper, we are concentrating on detecting the absence of mechanical elements. Previously we have developed a rendering pipeline for creating realistic synthetic 3D point cloud data. We do this by using the CAD model and taking into account occlusion and self-occlusion of mechanical parts. In this paper, an existing deep neural network for 3D segmentation is experimentally chosen and trained on these synthetic data. Further, the model is evaluated on real data acquired by a 3D scanner and has shown good quantitative results according to a segmentation metric. Finally, when a threshold is applied to the segmentation result, a final decision is made on the absence/presence problem. The achieved accuracy is 98.7%. Our research work is being carried out within the framework of the joint research laboratory ”Inspection 4.0” between IMT Mines Albi/ICA and the company Diota specialized in the development of numerical tools for Industry 4.0. This research is a continuation of the work presented at the QCAV’2021 conference [1].

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Section: Previous Workmentioning

confidence: 99%

Inspection of mechanical assemblies based on 3D deep learning segmentation

Boughrara

Jovančević

Orteu

et al. 2023

Sixteenth International Conference on Quality Control by Artificial Vision

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“…At the beginning of the 2010s, a wheeled collaborative mobile robot named Air-Cobot was built. It is capable to evolve safely around an aircraft in an environment which contains some obstacles to be avoided (Futterlieb et al, 2014) (Frejaville et al, 2016) (Bauda et al, 2017) (Futterlieb, 2017) (Lakrouf et al, 2017). Two sensors are dedicated to the inspection.…”

Section: Introductionmentioning

confidence: 99%

UAV-based Inspection of Airplane Exterior Screws with Computer Vision

Miranda

Larnier²,

Herbulot

et al. 2019

Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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We propose a new approach to detect and inspect aircraft exterior screws. An Unmanned Aerial Vehicle (UAV) locating itself in the aircraft frame thanks to lidar technology is able to acquire precise images coming with useful metadata. We use a method based on a convolutional neural network (CNN) to characterize zones of interest (ZOI) and to extract screws from images; methods are proposed to create prior model for matching. Classic matching approaches are used to match the screws from this model with the detected ones, to increase screw recognition accuracy and detect missing screws, giving the system a new ability. Computer vision algorithms are then applied to evaluate the state of each visible screw, and detect missing and loose ones.

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