About the Project
| Project Name: | PACMAN (Prognostics And Computer Aided Maintenance) |
| Project ID: | 686782 |
| From / To: | 01 October 2016 / 30 September 2020 |
| Funded under: | H2020-EU.3.4.5.1. - IADP Large Passenger Aircraft |
| Funding scheme: | CS2-RIA - Research and Innovation action |
| Call for proposal: | H2020-CS2-CFP01-2014-01 |
| Topic(s): | JTI-CS2-2014-CFP01-LPA-03-02 - Aircraft System Prognostic solutions integrated into an airline E2E maintenance operational context |
| Coordinator: | Honeywell International s.r.o., Prague, Czech Republic |
| Participant: | VSB-TU Ostrava, Ostrava, Czech Republic |
The main focus of VSB-TUO in the project is to develop computer vision and augmented reality (AR) algorithms for use in prognostics and maintenance. These algorithms will be the core technology behind the whole AR system that help technicians to easily orient in the working environment of large passenger aircrafts (LPA) maintenance by means of instant and accurate guidance, including parts highlighting, access to global network of experts in non-nominal situations, and finally enabling more efficient supervision and knowledge sharing. The general problem of almost all AR applications is to quickly and reliably determine object’s 3D pose. Most of the cutting edge toolkits offer the detection and tracking of simple black-and-white planar markers or planar images. However, only some of them offer the latest emerging algorithms for the 3D object detection and pose estimation. Such algorithms reduce the need of placing the artificial markers on the object surface which may be a complication in real-life conditions. In our research, we are addressing this problem using variety of approaches utilizing wide area of devices, namely, regular color cameras, depth sensors, and variety of near-to-eye (NTE) headsets.
Dissemination
- Fusek, R.: Object Recognition/Detection. Invited lecture to Data Science Summer School 2018 (International Summer School on "Deep Learning and Visual Data Analysis"), the presentation is available here
- Sojka, E.: Recognition from point clouds and 3D models. Invited lecture to ISCAMI 2018, the presentation is available here
- Fusek, R.: Object Recognition. Invited lecture to CASS 2017 (Czech-Austrian Summer School on "Deep Learning and Visual Data Analysis"), the presentation is available here
- Fabian, T.: Object Recognition in Augmented Reality. Invited lecture to CASS 2017 (Czech-Austrian Summer School on "Deep Learning and Visual Data Analysis"), the presentation is available here
- Sojka, E.: Recognition from point clouds and 3D models. Invited lecture to CASS 2017 (Czech-Austrian Summer School on "Deep Learning and Visual Data Analysis"), the presentation is available here