Lorenzo Nardi (Graduated 2020)

PhD Student
Contact:
Email: lorenzo.nardi@nulluni-bonn.de
Tel: +49 – 228 – 73 – 27 11
Fax: +49 – 228 – 73 – 27 12
Office: Nussallee 15, 1. OG, room 1.012
Address:
University of Bonn
Photogrammetry, IGG
Nussallee 15
53115 Bonn

Research Interests

  • Autonomous mobile robots
  • Path planning and decision making for robot navigation
  • Active learning and perception
  • Collision avoidance in dynamic environments

Bio Sketch

Lorenzo is a PhD student at the Photogrammetry & Robotics lab at the University of Bonn.
He studied Artificial Intelligence and Robotics at Sapienza, University of Rome, Italy, and graduated with a thesis on socially-aware robot navigation. Before starting his PhD, he worked as a software engineer in R&D.
His research focuses on path planning and decision making for autonomous robot navigation. He is mainly interested in algorithms that exploit the previous experiences of the robot to improve its navigation over time.

Projects

  • RobDREAM – Optimising Robot Performance while Dreaming (2015 – 2018). EU-funded project in H2020.
    It aims at enhancing the capabilities of industrial mobile manipulators by processing experiences made during the working day and by dreaming of possible future situations in their inactive phases.

Teaching

  • Solving online perception problems in ROS – WS 2016/17
  • Mobile sensing and robotics – 2018/19, 2019/20
  • Photogrammetry and remote sensing – WS 2018/19, WS 2019/20
  • Sensors and state estimation – WS 2019/20

Publications

2021

  • A. Pretto, S. Aravecchia, W. Burgard, N. Chebrolu, C. Dornhege, T. Falck, F. Fleckenstein, A. Fontenla, M. Imperoli, R. Khanna, F. Liebisch, P. Lottes, A. Milioto, D. Nardi, S. Nardi, J. Pfeifer, M. Popovic, C. Potena, C. Pradalier, E. Rothacker-Feder, I. Sa, A. Schaefer, R. Siegwart, C. Stachniss, A. Walter, V. Winterhalter, X. Wu, and J. Nieto, “Building an Aerial-Ground Robotics Systemfor Precision Farming: An Adaptable Solution,” IEEE Robotics & Automation Magazine, vol. 28, iss. 3, 2021.
    [BibTeX] [PDF] 
    @Article{pretto2021ram,
title = {{Building an Aerial-Ground Robotics Systemfor Precision Farming: An Adaptable Solution}},
author = {A. Pretto and S. Aravecchia and W. Burgard and N. Chebrolu and C. Dornhege and T. Falck and F. Fleckenstein and A. Fontenla and M. Imperoli and R. Khanna and F. Liebisch and P. Lottes and A. Milioto and D. Nardi and S. Nardi and J. Pfeifer and M. Popovic and C. Potena and C. Pradalier and E. Rothacker-Feder and I. Sa and A. Schaefer and R. Siegwart and C. Stachniss and A. Walter and V. Winterhalter and X. Wu and J. Nieto},
journal = ram,
volume = 28,
number = 3,
year = {2021},
url={https://www.ipb.uni-bonn.de/pdfs/pretto2021ram.pdf}
}

  • C. Carbone, D. Albani, F. Magistri, D. Ognibene, C. Stachniss, G. Kootstra, D. Nardi, and V. Trianni, “Monitoring and Mapping of Crop Fields with UAV Swarms Based on Information Gain,” in Proc. of the Intl. Symp. on Distributed Autonomous Robotic Systems (DARS), 2021.
    [BibTeX] [PDF] 
    @inproceedings{carbone2021dars,
author = {C. Carbone and D. Albani and F. Magistri and D. Ognibene and C. Stachniss and G. Kootstra and D. Nardi and V. Trianni},
title = {{Monitoring and Mapping of Crop Fields with UAV Swarms Based on Information Gain}},
booktitle = dars,
year = 2021,
}

2020

  • X. Chen, T. Läbe, L. Nardi, J. Behley, and C. Stachniss, “Learning an Overlap-based Observation Model for 3D LiDAR Localization,” in Proc. of the IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), 2020.
    [BibTeX] [PDF] [Code] [Video] 
    @inproceedings{chen2020iros,
author = {X. Chen and T. L\"abe and L. Nardi and J. Behley and C. Stachniss},
title = {{Learning an Overlap-based Observation Model for 3D LiDAR Localization}},
booktitle = iros,
year = {2020},
codeurl = {https://github.com/PRBonn/overlap_localization},
url={https://www.ipb.uni-bonn.de/pdfs/chen2020iros.pdf},
videourl = {https://www.youtube.com/watch?v=BozPqy_6YcE},
}

  • A. Ahmadi, L. Nardi, N. Chebrolu, and C. Stachniss, “Visual Servoing-based Navigation for Monitoring Row-Crop Fields,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2020.
    [BibTeX] [PDF] [Code] [Video] 
    @InProceedings{ahmadi2020icra,
title = {Visual Servoing-based Navigation for Monitoring Row-Crop Fields},
author = {A. Ahmadi and L. Nardi and N. Chebrolu and C. Stachniss},
booktitle = icra,
year = {2020},
url = {https://arxiv.org/pdf/1909.12754},
codeurl = {https://github.com/PRBonn/visual-crop-row-navigation},
videourl = {https://youtu.be/0qg6n4sshHk},
}

  • L. Nardi and C. Stachniss, “Long-Term Robot Navigation in Indoor Environments Estimating Patterns in Traversability Changes,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2020.
    [BibTeX] [PDF] [Video] 
    @InProceedings{nardi2020icra,
title = {Long-Term Robot Navigation in Indoor Environments Estimating Patterns in Traversability Changes},
author = {L. Nardi and C. Stachniss},
booktitle = icra,
year = {2020},
url = {https://arxiv.org/pdf/1909.12733},
videourl = {https://www.youtube.com/watch?v=9lNcA3quzwU},
}

2019

  • A. Pretto, S. Aravecchia, W. Burgard, N. Chebrolu, C. Dornhege, T. Falck, F. Fleckenstein, A. Fontenla, M. Imperoli, R. Khanna, F. Liebisch, P. Lottes, A. Milioto, D. Nardi, S. Nardi, J. Pfeifer, M. Popović, C. Potena, C. Pradalier, E. Rothacker-Feder, I. Sa, A. Schaefer, R. Siegwart, C. Stachniss, A. Walter, W. Winterhalter, X. Wu, and J. Nieto, “Building an Aerial-Ground Robotics System for Precision Farming,” arXiv Preprint, 2019.
    [BibTeX] [PDF] 
    @article{pretto2019arxiv,
author = {A. Pretto and S. Aravecchia and W. Burgard and N. Chebrolu and C. Dornhege and T. Falck and F. Fleckenstein and A. Fontenla and M. Imperoli and R. Khanna and F. Liebisch and P. Lottes and A. Milioto and D. Nardi and S. Nardi and J. Pfeifer and M. Popović and C. Potena and C. Pradalier and E. Rothacker-Feder and I. Sa and A. Schaefer and R. Siegwart and C. Stachniss and A. Walter and W. Winterhalter and X. Wu and J. Nieto},
title = {{Building an Aerial-Ground Robotics System for Precision Farming}},
journal = arxiv,
year = 2019,
eprint = {1911.03098v1},
url = {https://arxiv.org/pdf/1911.03098v1},
keywords = {cs.RO},
}

  • L. Nardi and C. Stachniss, “Uncertainty-Aware Path Planning for Navigation on Road Networks Using Augmented MDPs ,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2019.
    [BibTeX] [PDF] [Video] 
    @InProceedings{nardi2019icra-uapp,
author = {L. Nardi and C. Stachniss},
title = {{Uncertainty-Aware Path Planning for Navigation on Road Networks Using Augmented MDPs }},
booktitle = icra,
year = 2019,
url={https://www.ipb.uni-bonn.de/pdfs/nardi2019icra-uapp.pdf},
videourl = {https://youtu.be/3PMSamgYzi4},
}

  • L. Nardi and C. Stachniss, “Actively Improving Robot Navigation On Different Terrains Using Gaussian Process Mixture Models,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2019.
    [BibTeX] [PDF] [Video] 
    @InProceedings{nardi2019icra-airn,
author = {L. Nardi and C. Stachniss},
title = {{Actively Improving Robot Navigation On Different Terrains Using Gaussian Process Mixture Models}},
booktitle = icra,
year = 2019,
url={https://www.ipb.uni-bonn.de/pdfs/nardi2019icra-airn.pdf},
videourl = {https://youtu.be/DlMbP3u1g2Y},
}

2018

  • L. Nardi and C. Stachniss, “Towards Uncertainty-Aware Path Planning for Navigation on Road Networks Using Augmented MDPs,” in 10th Workshop on Planning, Perception and Navigation for Intelligent Vehicles at the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2018.
    [BibTeX] [PDF] [Video] 
    @InProceedings{nardi2018ppniv,
title = {Towards Uncertainty-Aware Path Planning for Navigation on Road Networks Using Augmented MDPs},
author = {L. Nardi and C. Stachniss},
booktitle = {10th Workshop on Planning, Perception and Navigation for Intelligent Vehicles at the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
year = {2018},
videourl = {https://youtu.be/SLp5YVplJAQ}
}

2017

  • L. Nardi and C. Stachniss, “User Preferred Behaviors for Robot Navigation Exploiting Previous Experiences,” in Journal on Robotics and Autonomous Systems (RAS), 2017. doi:10.1016/j.robot.2017.08.014
    [BibTeX] [PDF]

    Industry demands flexible robots that are able to accomplish different tasks at different locations such as navigation and mobile manipulation. Operators often require mobile robots operating on factory floors to follow definite and predictable behaviors. This becomes particularly important when a robot shares the workspace with other moving entities. In this paper, we present a system for robot navigation that exploits previous experiences to generate predictable behaviors that meet user’s preferences. Preferences are not explicitly formulated but implicitly extracted from robot experiences and automatically considered to plan paths for the successive tasks without requiring experts to hard-code rules or strategies. Our system aims at accomplishing navigation behaviors that follow user’s preferences also to avoid dynamic obstacles. We achieve this by considering a probabilistic approach for modeling uncertain trajectories of the moving entities that share the workspace with the robot. We implemented and thoroughly tested our system both in simulation and on a real mobile robot. The extensive experiments presented in this paper demonstrate that our approach allows a robot for successfully navigating while performing predictable behaviors and meeting user’s preferences

    @InProceedings{nardi2017jras,
title = {User Preferred Behaviors for Robot Navigation Exploiting Previous Experiences},
author = {L. Nardi and C. Stachniss},
booktitle = jras,
year = {2017},
doi = {10.1016/j.robot.2017.08.014},
abstract = {Industry demands flexible robots that are able to accomplish different tasks at different locations such as navigation and mobile manipulation. Operators often require mobile robots operating on factory floors to follow definite and predictable behaviors. This becomes particularly important when a robot shares the workspace with other moving entities. In this paper, we present a system for robot navigation that exploits previous experiences to generate predictable behaviors that meet user’s preferences. Preferences are not explicitly formulated but implicitly extracted from robot experiences and automatically considered to plan paths for the successive tasks without requiring experts to hard-code rules or strategies. Our system aims at accomplishing navigation behaviors that follow user’s preferences also to avoid dynamic obstacles. We achieve this by considering a probabilistic approach for modeling uncertain trajectories of the moving entities that share the workspace with the robot. We implemented and thoroughly tested our system both in simulation and on a real mobile robot. The extensive experiments presented in this paper demonstrate that our approach allows a robot for successfully navigating while performing predictable behaviors and meeting user’s preferences},
url = {https://www.ipb.uni-bonn.de/pdfs/nardi17jras.pdf},
}

2016

  • L. Nardi and C. Stachniss, “Experience-Based Path Planning for Mobile Robots Exploiting User Preferences,” in Proc. of the IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), 2016. doi:10.1109/IROS.2016.7759197
    [BibTeX] [PDF]

    The demand for flexible industrial robotic solutions that are able to accomplish tasks at different locations in a factory is growing more and more. When deploying mobile robots in a factory environment, the predictability and reproducibility of their behaviors become important and are often requested. In this paper, we propose an easy-to-use motion planning scheme that can take into account user preferences for robot navigation. The preferences are extracted implicitly from the previous experiences or from demonstrations and are automatically considered in the subsequent planning steps. This leads to reproducible and thus better to predict navigation behaviors of the robot, without requiring experts to hard-coding control strategies or cost functions within a planner. Our system has been implemented and evaluated on a simulated KUKA mobile robot in different environments.

    @InProceedings{nardi16iros,
title = {Experience-Based Path Planning for Mobile Robots Exploiting User Preferences},
author = {L. Nardi and C. Stachniss},
booktitle = iros,
year = {2016},
doi = {10.1109/IROS.2016.7759197},
abstract = {The demand for flexible industrial robotic solutions that are able to accomplish tasks at different locations in a factory is growing more and more. When deploying mobile robots in a factory environment, the predictability and reproducibility of their behaviors become important and are often requested. In this paper, we propose an easy-to-use motion planning scheme that can take into account user preferences for robot navigation. The preferences are extracted implicitly from the previous experiences or from demonstrations and are automatically considered in the subsequent planning steps. This leads to reproducible and thus better to predict navigation behaviors of the robot, without requiring experts to hard-coding control strategies or cost functions within a planner. Our system has been implemented and evaluated on a simulated KUKA mobile robot in different environments.},
url = {https://www.ipb.uni-bonn.de/pdfs/nardi16iros.pdf},
}

2015

  • F. M. Carlucci, L. Nardi, L. Iocchi, and D. Nardi, “Explicit Representation of Social Norms for Social Robots,” in Proc. of the IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), 2015, pp. 4191-4196. doi:10.1109/IROS.2015.7353970
    [BibTeX] [PDF]

    As robots are expected to become more and more available in everyday environments, interaction with humans is assuming a central role. Robots working in populated environments are thus expected to demonstrate socially acceptable behaviors and to follow social norms. However, most of the recent works in this field do not address the problem of explicit representation of the social norms and their integration in the reasoning and the execution components of a cognitive robot. In this paper, we address the design of robotic systems that support some social behavior by implementing social norms. We present a framework for planning and execution of social plans, in which social norms are described in a domain and language independent form. A full implementation of the proposed framework is described and tested in a realistic scenario with non-expert and non-recruited users.

    @InProceedings{carlucci15iros,
title = {Explicit Representation of Social Norms for Social Robots},
author = {F.M. Carlucci and L. Nardi and L. Iocchi and D. Nardi},
booktitle = iros,
year = {2015},
pages = {4191 - 4196},
abstract = {As robots are expected to become more and more available in everyday environments, interaction with humans is assuming a central role. Robots working in populated environments are thus expected to demonstrate socially acceptable behaviors and to follow social norms. However, most of the recent works in this field do not address the problem of explicit representation of the social norms and their integration in the reasoning and the execution components of a cognitive robot. In this paper, we address the design of robotic systems that support some social behavior by implementing social norms. We present a framework for planning and execution of social plans, in which social norms are described in a domain and language independent form. A full implementation of the proposed framework is described and tested in a realistic scenario with non-expert and non-recruited users.},
doi = {10.1109/IROS.2015.7353970},
timestamp = {2016.04.19},
url = {https://www.ipb.uni-bonn.de/pdfs/Carlucci2015Explicit.pdf},
}

2007

  • G. Grisetti, G. D. Tipaldi, C. Stachniss, W. Burgard, and D. Nardi, “Fast and Accurate SLAM with Rao-Blackwellized Particle Filters,” Journal on Robotics and Autonomous Systems (RAS), vol. 55, iss. 1, p. 30–38, 2007.
    [BibTeX] [PDF]
    [none]
    @Article{grisetti2007,
title = {Fast and Accurate {SLAM} with Rao-Blackwellized Particle Filters},
author = {Grisetti, G. and Tipaldi, G.D. and Stachniss, C. and Burgard, W. and Nardi, D.},
journal = jras,
year = {2007},
number = {1},
pages = {30--38},
volume = {55},
abstract = {[none]},
timestamp = {2014.04.24},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/grisetti07jras.pdf},
}

2006

  • G. Grisetti, G. D. Tipaldi, C. Stachniss, W. Burgard, and D. Nardi, “Speeding-Up Rao-Blackwellized SLAM,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), Orlando, FL, USA, 2006, p. 442–447.
    [BibTeX] [PDF]
    [none]
    @InProceedings{grisetti2006,
title = {Speeding-Up Rao-Blackwellized {SLAM}},
author = {Grisetti, G. and Tipaldi, G.D. and Stachniss, C. and Burgard, W. and Nardi, D.},
booktitle = icra,
year = {2006},
address = {Orlando, FL, USA},
pages = {442--447},
abstract = {[none]},
timestamp = {2014.04.24},
url = {https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/grisetti06icra.pdf},
}