Matteo Sodano

PhD Student
Contact:
Email: matteo.sodano@nulligg.uni-bonn.de
Tel: +49 – 228 – 73 – 60 190
Fax: +49 – 228 – 73 – 27 12
Office: Nussallee 15, 1. OG, room 1.009
Address:
University of Bonn
Photogrammetry, IGG
Nussallee 15
53115 Bonn

Profiles: LinkedIn, Google Scholar

Research Interests

  • Semantic Scene Understanding
  • Open-World Segmentation
  • Perception for Agricultural Robotics

Short CV

Matteo Sodano is a Ph.D. student at the Photogrammetry and Robotics Lab at the Rheinische Friedrich-Wilhelms-Universität Bonn since February 2021. He received his M.Sc. in Control Engineering from University “La Sapienza”, Rome. His thesis focused on legged locomotion and motion planning, and it was developed in collaboration with the Istituto Italiano di Tecnologia (IIT). Currently, his research interests are about semantic scene understanding, especially in open-world settings.

Projects

HARMONY – Enhancing Healthcare with Assistive Robotic Mobile Manipulation

Teaching

Mobile Sensing and Robotics I (Winter Semester) – 2021, 2022

Mobile Sensing and Robotics II (Summer Semester) – 2021

Master Project supervision:

  • LiDAR-based Panoptic Segmentation (2022)
  • Open-World Instance Segmentation of LiDAR Point Clouds (2023)
  • Grouping and Identifying Road Signs for Enhanced Geospatial Mapping (2024)

Master Project Course Organization – 2023/24, 2024/25

Lectures of the course on Basics of Control Systems – 2024/25 (coming soon)

Publications


2024

  • M. Sodano, F. Magistri, L. Nunes, J. Behley, and C. Stachniss, “Open-World Semantic Segmentation Including Class Similarity,” in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), 2024.
    [BibTeX] [PDF] 
    @inproceedings{sodano2024cvpr,
author = {M. Sodano and F. Magistri and L. Nunes and J. Behley and C. Stachniss},
title = {{Open-World Semantic Segmentation Including Class Similarity}},
booktitle = cvpr,
year = 2024,
}
  • F. Magistri, R. Marcuzzi, E. A. Marks, M. Sodano, J. Behley, and C. Stachniss, “Efficient and Accurate Transformer-Based 3D Shape Completion and Reconstruction of Fruits for Agricultural Robots,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2024.
    [BibTeX] [PDF] [Video] 
    @inproceedings{magistri2024icra,
author = {F. Magistri and R. Marcuzzi and E.A. Marks and M. Sodano and J. Behley and C. Stachniss},
title = {{Efficient and Accurate Transformer-Based 3D Shape Completion and Reconstruction of Fruits for Agricultural Robots}},
booktitle = icra,
year = 2024,
videourl = {https://youtu.be/U1xxnUGrVL4},
}

2023

  • N. Zimmerman, M. Sodano, E. Marks, J. Behley, and C. Stachniss, “Constructing Metric-Semantic Maps using Floor Plan Priors for Long-Term Indoor Localization,” in Proc. of the IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), 2023.
    [BibTeX] [PDF] [Code] [Video] 
    @inproceedings{zimmerman2023iros,
author = {N. Zimmerman and M. Sodano and E. Marks and J. Behley and C. Stachniss},
title = {{Constructing Metric-Semantic Maps using Floor Plan Priors for Long-Term Indoor Localization}},
booktitle = iros,
year = 2023,
codeurl = {https://github.com/PRBonn/SIMP},
videourl = {https://youtu.be/9ZGd5lJbG4s}
}
  • 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}
}
  • E. Marks, M. Sodano, F. Magistri, L. Wiesmann, D. Desai, R. Marcuzzi, J. Behley, and C. Stachniss, “High Precision Leaf Instance Segmentation in Point Clouds Obtained Under Real Field Conditions,” IEEE Robotics and Automation Letters (RA-L), vol. 8, iss. 8, pp. 4791-4798, 2023. doi:10.1109/LRA.2023.3288383
    [BibTeX] [PDF] [Code] [Video] 
    @article{marks2023ral,
author = {E. Marks and M. Sodano and F. Magistri and L. Wiesmann and D. Desai and R. Marcuzzi and J. Behley and C. Stachniss},
title = {{High Precision Leaf Instance Segmentation in Point Clouds Obtained Under Real Field Conditions}},
journal = ral,
pages = {4791-4798},
volume = {8},
number = {8},
issn = {2377-3766},
year = {2023},
doi = {10.1109/LRA.2023.3288383},
codeurl = {https://github.com/PRBonn/plant_pcd_segmenter},
videourl = {https://youtu.be/dvA1SvQ4iEY}
}
  • M. Sodano, F. Magistri, T. Guadagnino, J. Behley, and C. Stachniss, “Robust Double-Encoder Network for RGB-D Panoptic Segmentation,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2023.
    [BibTeX] [PDF] [Code] [Video] 
    @inproceedings{sodano2023icra,
author = {Matteo Sodano and Federico Magistri and Tiziano Guadagnino and Jens Behley and Cyrill Stachniss},
title = {{Robust Double-Encoder Network for RGB-D Panoptic Segmentation}},
booktitle = icra,
year = 2023,
codeurl = {https://github.com/PRBonn/PS-res-excite},
videourl = {https://youtu.be/r1pabV3sQYk}
}
  • 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}
}

2022

  • T. Guadagnino, X. Chen, M. Sodano, J. Behley, G. Grisetti, and C. Stachniss, “Fast Sparse LiDAR Odometry Using Self-Supervised Feature Selection on Intensity Images,” IEEE Robotics and Automation Letters (RA-L), vol. 7, iss. 3, pp. 7597-7604, 2022. doi:10.1109/LRA.2022.3184454
    [BibTeX] [PDF] 
    @article{guadagnino2022ral,
author = {T. Guadagnino and X. Chen and M. Sodano and J. Behley and G. Grisetti and C. Stachniss},
title = {{Fast Sparse LiDAR Odometry Using Self-Supervised Feature Selection on Intensity Images}},
journal = ral,
year = 2022,
volume = {7},
number = {3},
pages = {7597-7604},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/guadagnino2022ral-iros.pdf},
issn = {2377-3766},
doi = {10.1109/LRA.2022.3184454}
}