Gianmarco Roggiolani

Ph.D. student
Contact:
Email: gianmarco.roggiolani@nulligg.uni-bonn.de
Tel: +49 – 228 – 73 – 29 05
Fax: +49 – 228 – 73 – 27 12
Office: Nussallee 15, 1. OG, room 1.003
Address:
University of Bonn
Photogrammetry, IGG
Nussallee 15
53115 Bonn

LinkedIn

Research Interests

  • Computer Vision
  • Self-Supervised Learning
  • SLAM
  • Agricultural Robotics

Short CV

Gianmarco Roggiolani is a PhD student at the University of Bonn. He received a Bachelor’s degree in Computer and Automatic Engineering (2018) and a Master’s degree in Artificial Intelligence and Robotics (2021) from La Sapienza University in Rome, Italy. His thesis was focused on the integration of the IMU sensor into a SLAM pipeline.

Projects

  • PhenoRob – Robotics and Phenotyping for Sustainable Crop Production (DFG Cluster of Excellence)

Teaching

  • Sensors and State Estimation – Winter Semester 2021
  • Advanced Techniques for Sensors and State Estimation – Summer Semester 2022
  • Master Project “Self-Supervised Contrastive Learning in Traffic Scenes” – Winter Semester 2022
  • MSc Thesis “Multi-Modal Fine-Grained Pre-Training for Autonomous Driving” – Summer Semester 2023
  • MSc Thesis “Geometry-Aware Self-Supervised Leaf Instance Segmentation in 3D” – Winter Semester 2023
  • MSc Thesis “The Effect of Noisy Labels as Data Augmentation” – Winter Semester 2023

Publications

2023

  • G. Roggiolani, F. Magistri, T. Guadagnino, J. Behley, and C. Stachniss, “Unsupervised Pre-Training for 3D Leaf Instance Segmentation,” IEEE Robotics and Automation Letters (RA-L), vol. 8, pp. 7448-7455, 2023. doi:10.1109/LRA.2023.3320018
    [BibTeX] [PDF] [Code] 
    @article{roggiolani2023ral,
author = {G. Roggiolani and F. Magistri and T. Guadagnino and J. Behley and C. Stachniss},
title = {{Unsupervised Pre-Training for 3D Leaf Instance Segmentation}},
journal = ral,
year = {2023},
volume = {8},
issue = {11},
codeurl = {https://github.com/PRBonn/Unsupervised-Pre-Training-for-3D-Leaf-Instance-Segmentation},
pages = {7448-7455},
doi = {10.1109/LRA.2023.3320018},
issn = {2377-3766},
}
  • J. Weyler, F. Magistri, E. Marks, Y. L. Chong, M. Sodano, G. Roggiolani, N. Chebrolu, C. Stachniss, and J. Behley, “PhenoBench –- A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain,” Arxiv preprint, vol. arXiv:2306.04557, 2023.
    [BibTeX] [PDF] [Code] 
    @article{weyler2023arxiv,
author = {Jan Weyler and Federico Magistri and Elias Marks and Yue Linn Chong and Matteo Sodano
and Gianmarco Roggiolani and Nived Chebrolu and Cyrill Stachniss and Jens Behley},
title = {{PhenoBench --- A Large Dataset and Benchmarks for Semantic Image Interpretation
in the Agricultural Domain}},
journal = {arXiv preprint},
volume = {arXiv:2306.04557},
year = {2023},
codeurl = {https://github.com/PRBonn/phenobench}
}
  • G. Roggiolani, M. Sodano, F. Magistri, T. Guadagnino, J. Behley, and C. Stachniss, “Hierarchical Approach for Joint Semantic, Plant Instance, and Leaf Instance Segmentation in the Agricultural Domain,” in Proc. of the ieee intl. conf. on robotics & automation (icra), 2023.
    [BibTeX] [PDF] [Code] [Video] 
    @inproceedings{roggiolani2023icra-hajs,
author = {G. Roggiolani and M. Sodano and F. Magistri and T. Guadagnino and J. Behley and C. Stachniss},
title = {{Hierarchical Approach for Joint Semantic, Plant Instance, and Leaf Instance Segmentation in the Agricultural Domain}},
booktitle = icra,
year = {2023},
codeurl = {https://github.com/PRBonn/HAPT},
videourl = {https://youtu.be/miuOJjxlJic}
}
  • G. Roggiolani, F. Magistri, T. Guadagnino, J. Weyler, G. Grisetti, C. Stachniss, and J. Behley, “On Domain-Specific Pre-Training for Effective Semantic Perception in Agricultural Robotics,” in Proc. of the ieee intl. conf. on robotics & automation (icra), 2023.
    [BibTeX] [PDF] [Code] [Video] 
    @inproceedings{roggiolani2023icra-odsp,
author = {G. Roggiolani and F. Magistri and T. Guadagnino and J. Weyler and G. Grisetti and C. Stachniss and J. Behley},
title = {{On Domain-Specific Pre-Training for Effective Semantic Perception in Agricultural Robotics}},
booktitle = icra,
year = 2023,
codeurl= {https://github.com/PRBonn/agri-pretraining},
videourl = {https://youtu.be/FDWY_UnfsBs}
}