Ignacio Vizzo (Graduated 2023)

PhD Student
Contact:
Email: ivizzo@nulluni-bonn.de
Tel: +49 – 228 – 73 – 27 13
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

2025

  • T. Guadagnino, B. Mersch, S. Gupta, I. Vizzo, G. Grisetti, and C. Stachniss, “KISS-SLAM: A Simple, Robust, and Accurate 3D LiDAR SLAM System With Enhanced Generalization Capabilities,” arXiv Preprint, vol. arXiv:2503.12660, 2025.
    [BibTeX] [PDF] [Code]

    Robust and accurate localization and mapping of an environment using laser scanners, so-called LiDAR SLAM, is essential to many robotic applications. Early 3D LiDAR SLAM methods often exploited additional information from IMU or GNSS sensors to enhance localization accuracy and mitigate drift. Later, advanced systems further improved the estimation at the cost of a higher runtime and complexity. This paper explores the limits of what can be achieved with a LiDAR-only SLAM approach while following the Keep It Small and Simple (KISS) principle. By leveraging this minimalistic design principle, our system, KISS-SLAM, archives state-of-the-art performances in pose accuracy while requiring little to no parameter tuning for deployment across diverse environments, sensors, and motion profiles. We follow best practices in graph-based SLAM and build upon LiDAR odometry to compute the relative motion between scans and construct local maps of the environment. To correct drift, we match local maps and optimize the trajectory in a pose graph optimization step. The experimental results demonstrate that this design achieves competitive performance while reducing complexity and reliance on additional sensor modalities. By prioritizing simplicity, this work provides a new strong baseline for LiDAR-only SLAM and a high-performing starting point for future research. Further, our pipeline builds consistent maps that can be used directly for further downstream tasks like navigation. Our open-source system operates faster than the sensor frame rate in all presented datasets and is designed for real-world scenarios.

    @article{guadagnino2025arxiv,
author = {T. Guadagnino and B. Mersch and S. Gupta and I. Vizzo and G. Grisetti and C. Stachniss},
title = {{KISS-SLAM: A Simple, Robust, and Accurate 3D LiDAR SLAM System With Enhanced Generalization Capabilities}},
journal = arxiv,
year = 2025,
volume = {arXiv:2503.12660},
url = {https://arxiv.org/pdf/2503.12660},
codeurl = {https://github.com/PRBonn/kiss-slam},
abstract = {Robust and accurate localization and mapping of an environment using laser scanners, so-called LiDAR SLAM, is essential to many robotic applications. Early 3D LiDAR SLAM methods often exploited additional information from IMU or GNSS sensors to enhance localization accuracy and mitigate drift. Later, advanced systems further improved the estimation at the cost of a higher runtime and complexity. This paper explores the limits of what can be achieved with a LiDAR-only SLAM approach while following the Keep It Small and Simple (KISS) principle. By leveraging this minimalistic design principle, our system, KISS-SLAM, archives state-of-the-art performances in pose accuracy while requiring little to no parameter tuning for deployment across diverse environments, sensors, and motion profiles. We follow best practices in graph-based SLAM and build upon LiDAR odometry to compute the relative motion between scans and construct local maps of the environment. To correct drift, we match local maps and optimize the trajectory in a pose graph optimization step. The experimental results demonstrate that this design achieves competitive performance while reducing complexity and reliance on additional sensor modalities. By prioritizing simplicity, this work provides a new strong baseline for LiDAR-only SLAM and a high-performing starting point for future research. Further, our pipeline builds consistent maps that can be used directly for further downstream tasks like navigation. Our open-source system operates faster than the sensor frame rate in all presented datasets and is designed for real-world scenarios.},
}

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. doi:10.1109/ICRA57147.2024.10610311
    [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,
doi = {10.1109/ICRA57147.2024.10610311},
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. doi:10.1109/ICRA57147.2024.10610962
    [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,
doi = {10.1109/ICRA57147.2024.10610962},
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}
}