ESR1 – Laura Ribeiro
Research Project 1: “Vision enhancement in extreme environmental conditions” (TAU)
The project focuses on the development of advanced signal processing methods to extract mission critical information that can be used to augment operator’s awareness in extreme environmental conditions.
Current Status of the Project
Pose Estimation Using Retro Reflective Markers in a Confined Environment
- Development of highly resistant retro reflective markers
- Development of a robust and explainable pipeline for marker detection in challenging images
- In lab work, building a demonstration and test setup using an industrial robotic arm
- Links to Publications:
- Design and Manufacturing of an Optimized Retro Reflective Marker for Photogrammetric Pose Estimation in ITER (preprint)
- ITER-Tag: An Adaptable Framework for Robust Uncoded Fiducial Marker Detection and Identification
- Retro-Reflective-Marker-Aided Target Pose Estimation in a Safety-Critical Environment
- Robust Vision Using Retro Reflective Markers for Remote Handling in ITER
Multi Sensor Calibration Methods:
- Focus on photogrammetric calibration using targets of known geometry
- Use of an industrial robotic arm to provide positional feedback for calibration of non-overlapping sensor setups
- Link to Toolbox: https://github.com/laura-goncalvesribeiro/multi-camera-calibration
- Link to Publication: Photogrammetric Multi-Camera Calibration Using An Industrial Programmable Robotic Arm
Laura has an Integrated Masters degree in Biomedical Engineering (2017) from Faculdade de Cincias e Tecnologia da Universidade Nova de Lisboa, Portugal. Since has been working in the 3D Media Group at Tampere University, conducting research and programming mainly related with stereo matching algorithms and calibration of stereo and multi-camera systems.
Contact information:
Laura Ribeiro
Department of Signal Processing, Tampere University
Mailing address: Korkeakoulunkatu 1, TE309
P. O. Box 553, FIN-33101 Tampere, Finland
Email address: laura.goncalvesribeiro@tuni.fi
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 764951.