Augmented Reality for Automatically Generating Robust Manufacturing and Maintenance Logs

Tim J. Schoonbeek, Pierluigi Frisco, Hans Onvlee, Peter H.N. de With, Fons van der Sommen

London Imaging Meeting 2022 (oral presentation)

Image

Illustration of the proposed method to automatically log manufacturing and maintenance tasks. The approach consists of three main modules, namely perception, expectation, and progress monitoring. The latter automatically generates standardized, easily interpretable logs.

Abstract

Logs describing the execution of procedural steps during manufacturing and maintenance tasks are important for quality control and configuration management. Such logs are currently hand-written or typed during a procedure, which requires engineers to frequently step away from their work and results in difficulties for searching and optimizing logs. In this paper, we propose to automatically generate standardized, searchable logs, by visually perceiving and monitoring the progress of the procedure in real-time, and comparing this to the expected procedure. Unlike related work, we propose an approach which does not restrict the engineers to rigid, sequential sequences and instead allows them to execute procedures in a variety of different sequences where possible. The proposed framework is experimentally validated on the task of (dis)assembling a Duplo block model and operates properly when occlusions are absent.

Links

Publication

Bibtex

@inproceedings{schoonbeek2022augmented,
  title={Augmented reality for automatically generating robust manufacturing and maintenance logs},
  author={Schoonbeek, Tim and Frisco, Pierluigi and Onvlee, Hans and van der Sommen, Fons and others},
  booktitle={London Imaging Meeting},
  volume={3},
  pages={65--69},
  year={2022},
  organization={Society for Imaging Science and Technology}
}