Federica Di Lauro
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Federica Di Lauro has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Simone Fontana
Universita degli Studi di Milano-Bicocca Dipartimento di Giurisprudenza
3 shared publications - Alessandra Savini
Department of Earth and Environmental Sciences, University of Milano-Bicocca, 20126 Milano, Italy
1 shared publication Domenico G. Sorrenti
Department of Informatics, Systems and Communication Università degli Studi di Milano ‐ Bicocca Milano Italy
1 shared publication- Domenico Giorgio Sorrenti
Dipartimento di Informatica, Sistemistica e Comunicazione, Universit degli Studi di Milano - Bicocca, Milan, Italy
1 shared publication - Luca Fallati
Department of Earth and Environmental Sciences, University of Milano-Bicocca, 20126 Milano, Italy
1 shared publication
Latest Publications
- Open Access
- 22 August 2024
- Published by Wiley in Journal of Field Robotics
- Vol. 42 (2) , 406-429
Assessing the practical applicability of neural‐based point clouds registration algorithms: A comparative analysis
Point cloud registration is a vital task in three‐dimensional (3D) perception, with several different applications in robotics. Recent advancements have introduced neural‐based techniques that promise enhanced accuracy and robustness. In this paper, we thoroughly evaluate well‐known neural‐based point cloud registration methods using the Point Clouds Registration Benchmark, which was developed to cover a large variety of use cases. Our evaluation focuses on the performance of these techniques when applied to real‐complex data, which presents a more challenging and realistic scenario than the simpler experiments typically conducted by the original authors. The results reveal considerable variability in performance across different techniques, highlighting the importance of assessing algorithms in realistic settings. Notably, 3DSmoothNet emerges as a standout solution, demonstrating good and consistent results across various data sets. Its efficacy, coupled with a relatively low graphics processing unit (GPU) memory footprint, makes it a promising choice for robotics applications, even if it is not yet suitable for real‐time applications due to its execution time. Fully Convolutional Geometric Features also performs well, albeit with greater variability among data sets. PREDATOR and GeoTransformer are promising, but demand substantial GPU memory, when handling large point clouds from the Point Clouds Registration Benchmark. A notable finding concerns the performance of Fast Point Feature Histograms, which exhibit results comparable to the best approaches while demanding minimal computational resources. Overall, this comparative analysis provides valuable insights into the strengths and limitations of neural‐based registration techniques, both in terms of the quality of the results and the computational resources required. This helps researchers to make informed decisions for robotics applications.
- 22 August 2024
- Published by Wiley in Journal of Field Robotics
- Vol. 42 (2) , 406-429
Open AccessAssessing the practical applicability of neural‐based point clouds registration algorithms: A comparative analysis
Point cloud registration is a vital task in three‐dimensional (3D) perception, with several different applications in robotics. Recent advancements have introduced neural‐based techniques that promise enhanced accuracy and robustness. In this paper, we thoroughly evaluate well‐known neural‐based point cloud registration methods using the Point Clouds Registration Benchmark, which was developed to cover a large variety of use cases. Our evaluation focuses on the performance of these techniques when applied to real‐complex data, which presents a more challenging and realistic scenario than the simpler experiments typically conducted by the original authors. The results reveal considerable variability in performance across different techniques, highlighting the importance of assessing algorithms in realistic settings. Notably, 3DSmoothNet emerges as a standout solution, demonstrating good and consistent results across various data sets. Its efficacy, coupled with a relatively low graphics processing unit (GPU) memory footprint, makes it a promising choice for robotics applications, even if it is not yet suitable for real‐time applications due to its execution time. Fully Convolutional Geometric Features also performs well, albeit with greater variability among data sets. PREDATOR and GeoTransformer are promising, but demand substantial GPU memory, when handling large point clouds from the Point Clouds Registration Benchmark. A notable finding concerns the performance of Fast Point Feature Histograms, which exhibit results comparable to the best approaches while demanding minimal computational resources. Overall, this comparative analysis provides valuable insights into the strengths and limitations of neural‐based registration techniques, both in terms of the quality of the results and the computational resources required. This helps researchers to make informed decisions for robotics applications.
- 24 January 2024
- Published by Springer Nature
- p. 442-453
Automatic Alignment of Multi-scale Aerial and Underwater Photogrammetric Point Clouds: A Case Study in the Maldivian Coral Reef
- 24 January 2024
- Published by Springer Nature
- p. 442-453
Automatic Alignment of Multi-scale Aerial and Underwater Photogrammetric Point Clouds: A Case Study in the Maldivian Coral Reef
- Open Access
An Overview of Sensors for Long Range Missile Defense
Given the increasing tensions between world powers, missile defense is a topic that is more relevant than ever. However, information on the subject is often fragmented, confusing and untrustworthy. On the other hand, we believe that an informed overview of the current status is important for decision makers and citizens alike. A missile is essentially a guided rocket and therefore the term can be used to describe a very wide range of weapon systems. In this paper, we focus on long-range and intercontinental threats, which we believe are more important and problematic to defend against. We provide an overview of the two most common types of sensors, space-based infrared sensors and radars, and highlight their peculiarities and, most importantly, their drawbacks that severely limit their effectiveness.
Open AccessAn Overview of Sensors for Long Range Missile Defense
Given the increasing tensions between world powers, missile defense is a topic that is more relevant than ever. However, information on the subject is often fragmented, confusing and untrustworthy. On the other hand, we believe that an informed overview of the current status is important for decision makers and citizens alike. A missile is essentially a guided rocket and therefore the term can be used to describe a very wide range of weapon systems. In this paper, we focus on long-range and intercontinental threats, which we believe are more important and problematic to defend against. We provide an overview of the two most common types of sensors, space-based infrared sensors and radars, and highlight their peculiarities and, most importantly, their drawbacks that severely limit their effectiveness.