StachnissLab

Research Interests

• Autonomous Navigation and Exploration
• Change Detection, Map Maintenance
• Localization, Mapping, SLAM

Short CV

Emanuele Palazzolo is a PhD student at the University of Bonn. He received his Master of Science in Artificial Intelligence and Robotics at the University of Rome “La Sapienza” in Italy in 2015. During this time, he spent a semester at Tohoku University, in Sendai, Japan, where he conducted the research that resulted in his Master’s thesis, entitled “Simultaneous Localization and Mapping on a Rover Prototype for the Google Lunar XPRIZE”. He received his Bachelor of Science in Computer Engineering in 2013 at Polytechnic University of Turin, Italy.

His recent research is mainly focused on autonomous exploration on unmanned aerial veichles, change detection on 3D models, and RGB-D SLAM. He is currently working at the Mapping on Demand project.

Awards

• Excellence Path Completion at Sapienza University of Rome (2015)

Teaching

• Advanced Perception in Mobile Robotics, 2018/2019
• Master Project: Mobile Sensing and Robotics, 2018/2019
• Solving Online Perception Problems in ROS, 2017/2018

Publications

2019

• E. Palazzolo, “Active 3D Reconstruction for Mobile Robots,” PhD Thesis, 2019.
  [BibTeX] [PDF]

  @PhdThesis{palazzolo2019phd,
  author = {Palazzolo, E.},
  title = {Active 3D Reconstruction for Mobile Robots},
  year = 2019,
  school = {University of Bonn},
  URL = {https://www.ipb.uni-bonn.de/pdfs/palazzolo2019phd.pdf}
  }

• E. Palazzolo, J. Behley, P. Lottes, P. Giguère, and C. Stachniss, “ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals,” in Proc. of the IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), 2019.
  [BibTeX] [PDF] [Code] [Video]

  @InProceedings{palazzolo2019iros,
  author = {E. Palazzolo and J. Behley and P. Lottes and P. Gigu\`ere and C. Stachniss},
  title = {{ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals}},
  booktitle = iros,
  year = {2019},
  url = {https://www.ipb.uni-bonn.de/pdfs/palazzolo2019iros.pdf},
  codeurl = {https://github.com/PRBonn/refusion},
  videourl = {https://youtu.be/1P9ZfIS5-p4},
  }

• X. Chen, A. Milioto, E. Palazzolo, P. Giguère, J. Behley, and C. Stachniss, “SuMa++: Efficient LiDAR-based Semantic SLAM,” in Proc. of the IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), 2019.
  [BibTeX] [PDF] [Code] [Video]

  @inproceedings{chen2019iros,
  author = {X. Chen and A. Milioto and E. Palazzolo and P. Giguère and J. Behley and C. Stachniss},
  title = {{SuMa++: Efficient LiDAR-based Semantic SLAM}},
  booktitle = iros,
  year = 2019,
  codeurl = {https://github.com/PRBonn/semantic_suma/},
  videourl = {https://youtu.be/uo3ZuLuFAzk},
  }

2018

• E. Palazzolo and C. Stachniss, “Effective Exploration for MAVs Based on the Expected Information Gain,” Drones, vol. 2, iss. 1, 2018. doi:10.3390/drones2010009
  [BibTeX] [PDF]
  

  Micro aerial vehicles (MAVs) are an excellent platform for autonomous exploration. Most MAVs rely mainly on cameras for buliding a map of the 3D environment. Therefore, vision-based MAVs require an efficient exploration algorithm to select viewpoints that provide informative measurements. In this paper, we propose an exploration approach that selects in real time the next-best-view that maximizes the expected information gain of new measurements. In addition, we take into account the cost of reaching a new viewpoint in terms of distance and predictability of the flight path for a human observer. Finally, our approach selects a path that reduces the risk of crashes when the expected battery life comes to an end, while still maximizing the information gain in the process. We implemented and thoroughly tested our approach and the experiments show that it offers an improved performance compared to other state-of-the-art algorithms in terms of precision of the reconstruction, execution time, and smoothness of the path.

  @Article{palazzolo2018drones,
  author = {E. Palazzolo and C. Stachniss},
  title = {{Effective Exploration for MAVs Based on the Expected Information Gain}},
  journal = {Drones},
  volume = {2},
  year = {2018},
  number = {1},
  article-number= {9},
  url = {https://www.ipb.uni-bonn.de/pdfs/palazzolo2018drones.pdf},
  issn = {2504-446X},
  abstract = {Micro aerial vehicles (MAVs) are an excellent platform for autonomous exploration. Most MAVs rely mainly on cameras for buliding a map of the 3D environment. Therefore, vision-based MAVs require an efficient exploration algorithm to select viewpoints that provide informative measurements. In this paper, we propose an exploration approach that selects in real time the next-best-view that maximizes the expected information gain of new measurements. In addition, we take into account the cost of reaching a new viewpoint in terms of distance and predictability of the flight path for a human observer. Finally, our approach selects a path that reduces the risk of crashes when the expected battery life comes to an end, while still maximizing the information gain in the process. We implemented and thoroughly tested our approach and the experiments show that it offers an improved performance compared to other state-of-the-art algorithms in terms of precision of the reconstruction, execution time, and smoothness of the path.},
  doi = {10.3390/drones2010009},
  }

• E. Palazzolo and C. Stachniss, “Fast Image-Based Geometric Change Detection Given a 3D Model,” in Proc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA), 2018.
  [BibTeX] [PDF] [Code] [Video]

  @InProceedings{palazzolo2018icra,
  title = {{Fast Image-Based Geometric Change Detection Given a 3D Model}},
  author = {E. Palazzolo and C. Stachniss},
  booktitle = icra,
  year = {2018},
  url = {https://www.ipb.uni-bonn.de/pdfs/palazzolo2018icra.pdf},
  codeurl = {https://github.com/PRBonn/fast_change_detection},
  videourl = {https://youtu.be/DEkOYf4Zzh4},
  }

• F. Langer, L. Mandtler, A. Milioto, E. Palazzolo, and C. Stachniss, “Geometrical Stem Detection from Image Data for Precision Agriculture,” arXiv Preprint, 2018.
  [BibTeX] [PDF]

  @article{langer2018arxiv,
  author = {F. Langer and L. Mandtler and A. Milioto and E. Palazzolo and C. Stachniss},
  title = {{Geometrical Stem Detection from Image Data for Precision Agriculture}},
  journal = arxiv,
  year = 2018,
  eprint = {1812.05415v1},
  url = {https://arxiv.org/pdf/1812.05415v1},
  keywords = {cs.RO},
  }

2017

• E. Palazzolo and C. Stachniss, “Information-Driven Autonomous Exploration for a Vision-Based MAV,” in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017.
  [BibTeX] [PDF]

  @InProceedings{palazzolo2017uavg,
  title = {Information-Driven Autonomous Exploration for a Vision-Based MAV},
  author = {E. Palazzolo and C. Stachniss},
  booktitle = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
  year = {2017},
  url = {https://www.ipb.uni-bonn.de/pdfs/palazzolo2017uavg.pdf},
  }

• E. Palazzolo and C. Stachniss, “Change Detection in 3D Models Based on Camera Images,” in 9th Workshop on Planning, Perception and Navigation for Intelligent Vehicles at the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2017.
  [BibTeX] [PDF]

  @InProceedings{palazzolo2017irosws,
  title = {Change Detection in 3D Models Based on Camera Images},
  author = {E. Palazzolo and C. Stachniss},
  booktitle = {9th Workshop on Planning, Perception and Navigation for Intelligent Vehicles at the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
  year = {2017},
  url = {https://www.ipb.uni-bonn.de/pdfs/palazzolo2017irosws},
  }

2016

• M. Laîné, S. Cruciani, E. Palazzolo, N. J. Britton, X. Cavarelli, and K. Yoshida, “Navigation System for a Small Size Lunar Exploration Rover with a Monocular Omnidirectional Camera,” in Proc. SPIE, 2016. doi:10.1117/12.2242871
  [BibTeX]

  @InProceedings{laine16spie,
  author = { M. La{\^{i}}n{\'{e}} and S. Cruciani and E. Palazzolo and N.J. Britton and X. Cavarelli and K. Yoshida},
  title = {Navigation System for a Small Size Lunar Exploration Rover with a Monocular Omnidirectional Camera},
  booktitle = {Proc. SPIE},
  volume = {10011},
  year = {2016},
  doi = {10.1117/12.2242871},
  }