Ignacio Vizzo (Graduated 2023)

PhD Student
Contact:
Email: ivizzo@nulluni-bonn.de
Tel: +49 – 228 – 73 – 27 13
Fax: +49 – 228 – 73 – 27 12
Office: Nussallee 15, 1. OG, room 1.009
Address:
University of Bonn
Photogrammetry, IGG
Nussallee 15
53115 Bonn

Research Interests

  • 3D LiDAR-based SLAM
  • 3D Surface Reconstruction
  • Localization and Mapping in Outdoor Environments
  • High-performance Computing

Short CV

Ignacio Vizzo is a Research Assistant and Ph.D. Student at the University of Bonn since January 2019. He received his Electrical Engineering Degree from Universidad Nacional de Rosario, Argentina in December 2015. In the 2 years preceding his Ph.D. he worked for iRobot (USA) on Software development, developing behaviors, and working on the navigation system for lawn-care consumer robots. Before starting in the robotics world, he worked for 2 years in the Energy Industry, doing field testing in diverse power generation plants (hydroelectric, solar, etc). During this time he developed a novel 3D-SVPWM algorithm to control a 3-phase power inverter.

Teaching

  • Modern C++ for Image Processing, 2019
  • Photogrammetry and remote sensing – 2019/2020
  • Sensors and state estimation – 2019/2020
  • Modern C++ for Image Processing, 2020 (Website/)

Publications

2024

  • T. Guadagnino, B. Mersch, I. Vizzo, S. Gupta, M. V. R. Malladi, L. Lobefaro, G. Doisy, and C. Stachniss, “Kinematic-ICP: Enhancing LiDAR Odometry with Kinematic Constraints for Wheeled Mobile Robots Moving on Planar Surfaces,” arXiv Preprint, vol. arXiv:2410.10277, 2024.
    [BibTeX] [PDF] [Code] 
    @article{guadagnino2024arxiv,
author = {Guadagnino, T. and Mersch, B. and Vizzo, I. and Gupta, S. and Malladi, M.V.R. and Lobefaro, L. and Doisy, G. and Stachniss, C.},
title = {{Kinematic-ICP: Enhancing LiDAR Odometry with Kinematic Constraints for Wheeled Mobile Robots Moving on Planar Surfaces}},
journal = arxiv,
year = {2024},
volume = {arXiv:2410.10277},
url = {https://arxiv.org/pdf/2410.10277},
codeurl = {https://github.com/PRBonn/kinematic-icp},
}

  • D. Casado Herraez, M. Zeller, L. Chang, I. Vizzo, M. Heidingsfeld, and C. Stachniss, “Radar-Only Odometry and Mapping for Autonomous Vehicles,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2024.
    [BibTeX] [PDF] [Video] 
    @inproceedings{casado-herraez2024icra,
author = {Casado Herraez, Daniel and M. Zeller and Chang, Le and I. Vizzo and M. Heidingsfeld and C. Stachniss},
title = {{Radar-Only Odometry and Mapping for Autonomous Vehicles}},
booktitle = icra,
year = 2024,
videourl = {https://youtu.be/_xWDXyyKEok}
}

  • S. Gupta, T. Guadagnino, B. Mersch, I. Vizzo, and C. Stachniss, “Effectively Detecting Loop Closures using Point Cloud Density Maps,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2024.
    [BibTeX] [PDF] [Code] [Video] 
    @inproceedings{gupta2024icra,
author = {S. Gupta and T. Guadagnino and B. Mersch and I. Vizzo and C. Stachniss},
title = {{Effectively Detecting Loop Closures using Point Cloud Density Maps}},
booktitle = icra,
year = 2024,
codeurl = {https://github.com/PRBonn/MapClosures},
videourl = {https://youtu.be/BpwR_aLXrNo},
}

2023

  • I. Vizzo, B. Mersch, L. Nunes, L. Wiesmann, T. Guadagnino, and C. Stachniss, “Toward Reproducible Version-Controlled Perception Platforms: Embracing Simplicity in Autonomous Vehicle Dataset Acquisition,” in Proc. of the Intl. Conf. on Intelligent Transportation Systems Workshops, 2023.
    [BibTeX] [PDF] [Code] 
    @inproceedings{vizzo2023itcsws,
author = {I. Vizzo and B. Mersch and L. Nunes and L. Wiesmann and T. Guadagnino and C. Stachniss},
title = {{Toward Reproducible Version-Controlled Perception Platforms: Embracing Simplicity in Autonomous Vehicle Dataset Acquisition}},
booktitle = {Proc. of the Intl. Conf. on Intelligent Transportation Systems Workshops},
year = 2023,
codeurl = {https://github.com/ipb-car/meta-workspace},
note = {accepted}
}

  • B. Mersch, T. Guadagnino, X. Chen, I. Vizzo, J. Behley, and C. Stachniss, “Building Volumetric Beliefs for Dynamic Environments Exploiting Map-Based Moving Object Segmentation,” IEEE Robotics and Automation Letters (RA-L), vol. 8, iss. 8, pp. 5180-5187, 2023. doi:10.1109/LRA.2023.3292583
    [BibTeX] [PDF] [Code] [Video] 
    @article{mersch2023ral,
author = {B. Mersch and T. Guadagnino and X. Chen and I. Vizzo and J. Behley and C. Stachniss},
title = {{Building Volumetric Beliefs for Dynamic Environments Exploiting Map-Based Moving Object Segmentation}},
journal = ral,
volume = {8},
number = {8},
pages = {5180-5187},
year = 2023,
issn = {2377-3766},
doi = {10.1109/LRA.2023.3292583},
videourl = {https://youtu.be/aeXhvkwtDbI},
codeurl = {https://github.com/PRBonn/MapMOS},
}

  • L. Wiesmann, T. Guadagnino, I. Vizzo, N. Zimmerman, Y. Pan, H. Kuang, J. Behley, and C. Stachniss, “LocNDF: Neural Distance Field Mapping for Robot Localization,” IEEE Robotics and Automation Letters (RA-L), vol. 8, iss. 8, p. 4999–5006, 2023. doi:10.1109/LRA.2023.3291274
    [BibTeX] [PDF] [Code] [Video] 
    @article{wiesmann2023ral-icra,
author = {L. Wiesmann and T. Guadagnino and I. Vizzo and N. Zimmerman and Y. Pan and H. Kuang and J. Behley and C. Stachniss},
title = {{LocNDF: Neural Distance Field Mapping for Robot Localization}},
journal = ral,
volume = {8},
number = {8},
pages = {4999--5006},
year = 2023,
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/wiesmann2023ral-icra.pdf},
issn = {2377-3766},
doi = {10.1109/LRA.2023.3291274},
codeurl = {https://github.com/PRBonn/LocNDF},
videourl = {https://youtu.be/-0idH21BpMI},
}

  • I. Vizzo, T. Guadagnino, B. Mersch, L. Wiesmann, J. Behley, and C. Stachniss, “KISS-ICP: In Defense of Point-to-Point ICP – Simple, Accurate, and Robust Registration If Done the Right Way,” IEEE Robotics and Automation Letters (RA-L), vol. 8, iss. 2, pp. 1-8, 2023. doi:10.1109/LRA.2023.3236571
    [BibTeX] [PDF] [Code] [Video] 
    @article{vizzo2023ral,
author = {Vizzo, Ignacio and Guadagnino, Tiziano and Mersch, Benedikt and Wiesmann, Louis and Behley, Jens and Stachniss, Cyrill},
title = {{KISS-ICP: In Defense of Point-to-Point ICP -- Simple, Accurate, and Robust Registration If Done the Right Way}},
journal = ral,
pages = {1-8},
doi = {10.1109/LRA.2023.3236571},
volume = {8},
number = {2},
year = {2023},
codeurl = {https://github.com/PRBonn/kiss-icp},
videourl = {https://youtu.be/h71aGiD-uxU}
}

2022

  • F. Magistri, E. Marks, S. Nagulavancha, I. Vizzo, T. Läbe, J. Behley, M. Halstead, C. McCool, and C. Stachniss, “Contrastive 3D Shape Completion and Reconstruction for Agricultural Robots using RGB-D Frames,” IEEE Robotics and Automation Letters (RA-L), vol. 7, iss. 4, pp. 10120-10127, 2022.
    [BibTeX] [PDF] [Video] 
    @article{magistri2022ral-iros,
author = {Federico Magistri and Elias Marks and Sumanth Nagulavancha and Ignacio Vizzo and Thomas L{\"a}be and Jens Behley and Michael Halstead and Chris McCool and Cyrill Stachniss},
title = {Contrastive 3D Shape Completion and Reconstruction for Agricultural Robots using RGB-D Frames},
journal = ral,
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/magistri2022ral-iros.pdf},
year = {2022},
volume={7},
number={4},
pages={10120-10127},
videourl = {https://www.youtube.com/watch?v=2ErUf9q7YOI},
}

  • I. Vizzo, B. Mersch, R. Marcuzzi, L. Wiesmann, J. Behley, and C. Stachniss, “Make it Dense: Self-Supervised Geometric Scan Completion of Sparse 3D LiDAR Scans in Large Outdoor Environments,” IEEE Robotics and Automation Letters (RA-L), vol. 7, iss. 3, pp. 8534-8541, 2022. doi:10.1109/LRA.2022.3187255
    [BibTeX] [PDF] [Code] [Video] 
    @article{vizzo2022ral,
author = {I. Vizzo and B. Mersch and R. Marcuzzi and L. Wiesmann and J. Behley and C. Stachniss},
title = {Make it Dense: Self-Supervised Geometric Scan Completion of Sparse 3D LiDAR Scans in Large Outdoor Environments},
journal = ral,
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/vizzo2022ral-iros.pdf},
codeurl = {https://github.com/PRBonn/make_it_dense},
year = {2022},
volume = {7},
number = {3},
pages = {8534-8541},
doi = {10.1109/LRA.2022.3187255},
videourl = {https://youtu.be/NVjURcArHn8},
}

  • B. Mersch, X. Chen, I. Vizzo, L. Nunes, J. Behley, and C. Stachniss, “Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions,” IEEE Robotics and Automation Letters (RA-L), vol. 7, iss. 3, p. 7503–7510, 2022. doi:10.1109/LRA.2022.3183245
    [BibTeX] [PDF] [Code] [Video] 
    @article{mersch2022ral,
author = {B. Mersch and X. Chen and I. Vizzo and L. Nunes and J. Behley and C. Stachniss},
title = {{Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions}},
journal = ral,
year = 2022,
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/mersch2022ral.pdf},
volume = {7},
number = {3},
pages = {7503--7510},
doi = {10.1109/LRA.2022.3183245},
codeurl = {https://github.com/PRBonn/4DMOS},
videourl = {https://youtu.be/5aWew6caPNQ},
}

  • L. Wiesmann, T. Guadagnino, I. Vizzo, G. Grisetti, J. Behley, and C. Stachniss, “DCPCR: Deep Compressed Point Cloud Registration in Large-Scale Outdoor Environments,” IEEE Robotics and Automation Letters (RA-L), vol. 7, iss. 3, pp. 6327-6334, 2022. doi:10.1109/LRA.2022.3171068
    [BibTeX] [PDF] [Code] [Video] 
    @article{wiesmann2022ral-iros,
author = {L. Wiesmann and T. Guadagnino and I. Vizzo and G. Grisetti and J. Behley and C. Stachniss},
title = {{DCPCR: Deep Compressed Point Cloud Registration in Large-Scale Outdoor Environments}},
journal = ral,
year = 2022,
volume = 7,
number = 3,
pages = {6327-6334},
issn = {2377-3766},
doi = {10.1109/LRA.2022.3171068},
codeurl = {https://github.com/PRBonn/DCPCR},
videourl = {https://youtu.be/RqLr2RTGy1s},
}

  • X. Chen, B. Mersch, L. Nunes, R. Marcuzzi, I. Vizzo, J. Behley, and C. Stachniss, “Automatic Labeling to Generate Training Data for Online LiDAR-Based Moving Object Segmentation,” IEEE Robotics and Automation Letters (RA-L), vol. 7, iss. 3, pp. 6107-6114, 2022. doi:10.1109/LRA.2022.3166544
    [BibTeX] [PDF] [Code] [Video] 
    @article{chen2022ral,
author = {X. Chen and B. Mersch and L. Nunes and R. Marcuzzi and I. Vizzo and J. Behley and C. Stachniss},
title = {{Automatic Labeling to Generate Training Data for Online LiDAR-Based Moving Object Segmentation}},
journal = ral,
year = 2022,
volume = 7,
number = 3,
pages = {6107-6114},
url = {https://arxiv.org/pdf/2201.04501},
issn = {2377-3766},
doi = {10.1109/LRA.2022.3166544},
codeurl = {https://github.com/PRBonn/auto-mos},
videourl = {https://youtu.be/3V5RA1udL4c},
}

  • I. Vizzo, T. Guadagnino, J. Behley, and C. Stachniss, “VDBFusion: Flexible and Efficient TSDF Integration of Range Sensor Data,” Sensors, vol. 22, iss. 3, 2022. doi:10.3390/s22031296
    [BibTeX] [PDF] [Code] 
    @article{vizzo2022sensors,
author = {Vizzo, I. and Guadagnino, T. and Behley, J. and Stachniss, C.},
title = {VDBFusion: Flexible and Efficient TSDF Integration of Range Sensor Data},
journal = {Sensors},
volume = {22},
year = {2022},
number = {3},
article-number = {1296},
url = {https://www.mdpi.com/1424-8220/22/3/1296},
issn = {1424-8220},
doi = {10.3390/s22031296},
codeurl = {https://github.com/PRBonn/vdbfusion},
}

  • R. Marcuzzi, L. Nunes, L. Wiesmann, I. Vizzo, J. Behley, and C. Stachniss, “Contrastive Instance Association for 4D Panoptic Segmentation using Sequences of 3D LiDAR Scans,” IEEE Robotics and Automation Letters (RA-L), vol. 7, iss. 2, pp. 1550-1557, 2022. doi:10.1109/LRA.2022.3140439
    [BibTeX] [PDF] 
    @article{marcuzzi2022ral,
author = {R. Marcuzzi and L. Nunes and L. Wiesmann and I. Vizzo and J. Behley and C. Stachniss},
title = {{Contrastive Instance Association for 4D Panoptic Segmentation using Sequences of 3D LiDAR Scans}},
journal = ral,
year = 2022,
doi = {10.1109/LRA.2022.3140439},
issn = {2377-3766},
volume = 7,
number = 2,
pages = {1550-1557},
}

2021

  • I. Vizzo, X. Chen, N. Chebrolu, J. Behley, and C. Stachniss, “Poisson Surface Reconstruction for LiDAR Odometry and Mapping,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2021.
    [BibTeX] [PDF] [Code] [Video] 
    @inproceedings{vizzo2021icra,
author = {I. Vizzo and X. Chen and N. Chebrolu and J. Behley and C. Stachniss},
title = {{Poisson Surface Reconstruction for LiDAR Odometry and Mapping}},
booktitle = icra,
year = 2021,
url = {https://www.ipb.uni-bonn.de/pdfs/vizzo2021icra.pdf},
codeurl = {https://github.com/PRBonn/puma},
videourl = {https://youtu.be/7yWtYWaO5Nk}
}

  • X. Chen, I. Vizzo, T. Läbe, J. Behley, and C. Stachniss, “Range Image-based LiDAR Localization for Autonomous Vehicles,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2021.
    [BibTeX] [PDF] [Code] [Video] 
    @inproceedings{chen2021icra,
author = {X. Chen and I. Vizzo and T. L{\"a}be and J. Behley and C. Stachniss},
title = {{Range Image-based LiDAR Localization for Autonomous Vehicles}},
booktitle = icra,
year = 2021,
url = {https://www.ipb.uni-bonn.de/pdfs/chen2021icra.pdf},
codeurl = {https://github.com/PRBonn/range-mcl},
videourl = {https://youtu.be/hpOPXX9oPqI},
}

2020

  • C. Stachniss, I. Vizzo, L. Wiesmann, and N. Berning, How To Setup and Run a 100\% Digital Conf.: DIGICROP 2020, 2020.
    [BibTeX] [PDF]

    The purpose of this record is to document the setup and execution of DIGICROP 2020 and to simplify conducting future online events of that kind. DIGICROP 2020 was a 100\% virtual conference run via Zoom with around 900 registered people in November 2020. It consisted of video presentations available via our website and a single-day live event for Q&A. We had around 450 people attending the Q&A session overall, most of the time 200-250 people have been online at the same time. This document is a collection of notes, instructions, and todo lists. It is not a polished manual, however, we believe these notes will be useful for other conference organizers and for us in the future.

    @misc{stachniss2020digitalconf,
author = {C. Stachniss and I. Vizzo and L. Wiesmann and N. Berning},
title = {{How To Setup and Run a 100\% Digital Conf.: DIGICROP 2020}},
year = {2020},
url = {https://www.ipb.uni-bonn.de/pdfs/stachniss2020digitalconf.pdf},
abstract = {The purpose of this record is to document the setup and execution of DIGICROP 2020 and to simplify conducting future online events of that kind. DIGICROP 2020 was a 100\% virtual conference run via Zoom with around 900 registered people in November 2020. It consisted of video presentations available via our website and a single-day live event for Q&A. We had around 450 people attending the Q&A session overall, most of the time 200-250 people have been online at the same time. This document is a collection of notes, instructions, and todo lists. It is not a polished manual, however, we believe these notes will be useful for other conference organizers and for us in the future.},
}

2019

  • A. Milioto, I. Vizzo, J. Behley, and C. Stachniss, “RangeNet++: Fast and Accurate LiDAR Semantic Segmentation,” in Proc. of the IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), 2019.
    [BibTeX] [PDF] [Code] [Video] 
    @inproceedings{milioto2019iros,
author = {A. Milioto and I. Vizzo and J. Behley and C. Stachniss},
title = {{RangeNet++: Fast and Accurate LiDAR Semantic Segmentation}},
booktitle = iros,
year = 2019,
codeurl = {https://github.com/PRBonn/lidar-bonnetal},
videourl = {https://youtu.be/wuokg7MFZyU},
}