Simone Felicioni

Main Projects

🔹 Vision-based Topological Localization

Abstract

Vision-based topological localization is recently emerging as a promising alternative to metric pose estimation techniques in robotic navigation systems. Contrarily to the latter, which suffer from a quick degradation of their performance under non-ideal conditions (e.g., scenes with poor illumination and low amount of textures), topological localization trades off precise metric positioning with a more robust and higher-level location representation. State-of-the-art works in this direction, however, often neglect the spatiotemporal relationships between poses that are naturally induced by robotic navigation. Furthermore, these techniques are nearly unexplored for autonomous flying platforms. Inspired by these considerations, in this work, we propose a vision-based topological localization approach designed for Micro Aerial Vehicles (MAVs) applications. Our strategy exploits the framework of graph recurrent neural networks to model the spatial and temporal dependencies and estimate the location of the robot with respect to a topological graph representing the environment. We demonstrate with experiments on different sets of scenarios, including scenes that considerably differ from those used in the training phase, that our approach is able to outperform state-of-the-art place recognition baselines.

BibTeX

@article{felicioni2024topological,
  title     = {Vision-based Topological Localization for MAVs},
  author    = {Felicioni, Simone and Rizzo, Biagio and Tortorici, Claudio and Costante, Gabriele},
  journal   = {IEEE Robotics and Automation Letters},
  volume    = {9},
  number    = {5},
  pages     = {1234--1241},
  year      = {2024},
  publisher = {IEEE}
  }
  
@inproceedings{felicioni2021goln,
  author    = {Felicioni, Simone and Legittimo, Marco and Fravolini, Mario Luca and Costante, Gabriele},
  title     = {GOLN: Graph Object-based Localization Network},
  booktitle = {2021 20th International Conference on Advanced Robotics (ICAR)},
  year      = {2021},
  pages     = {849--856},
  publisher = {IEEE}
}

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🔹 Data-driven Structural Health Monitoring

Abstract

Bridge monitoring is crucial for ensuring the safety of these infrastructures, as they are constantly exposed to environmental stress, aging, harsh weather conditions, and intense traffic loads. Traditional inspection methods are often labor-intensive and prone to human errors, motivating the development of automated and data-driven approaches based on sensor networks directly mounted on the bridges. Additionally, the growing interest in machine learning has significantly impacted various fields, including infrastructure monitoring and maintenance. This study presents a novel datadriven approach for sensor fault detection in bridge monitoring application, based on a combination of an encoder-decoder architecture and a Siamese network. The former aims to reconstruct the spectrograms computed from the accelerometer signals, whereas the latter aims to learn a similarity metrics to better discriminate faulty readings from healthy ones. The experimental results of this study demonstrate the potential of the proposed approach in enhancing sensor fault detection performance compared to several baselines, providing more accurate predictions even with small fault intensities.

BibTeX

@article{felicioni2025enhanced,
  title     = {Enhanced sensor fault detection in bridge monitoring using Siamese-based encoder-decoder},
  author    = {Felicioni, Simone and Castellini, Luca and Tinti, Luca and Giorgetti, Folco and Fravolini, Mario Luca},
  journal   = {IEEE 21st International Conference on Automation Science and Engineering},
  volume    = {},
  number    = {},
  pages     = {},
  year      = {2025},
  publisher = {IEEE}
  }

Publications

Full list on Google Scholar.

Journal Papers

• Felicioni S., Rizzo B., Tortorici C., Costante G., "Vision-based Topological Localization for MAVs", 2024 IEEE Robotics and Automation Letters (RA-L)

• Dionigi A., Felicioni S., Leomanni M., Costante G., "D-VAT: End-to-End Visual Active Tracking for Micro Aerial Vehicles", 2024 IEEE Robotics and Automation Letters (RA-L)

• Crocetti F., Bellocchio E., Dionigi A., Felicioni S., Costante G., Fravolini M.L., Valigi P., "ARD-VO: Agricultural Robot Dataset of Vineyards and Olive groves", 2023 Journal of Field Robotics

• Legittimo M., Felicioni S., Bagni F., Tragliavini A., Dionigi A., Gatti, F., Verucchi M., Costante G., Bertogna M., "A Benchmark Analysis of Data-driven and Geometric Approaches for Robot Ego-Motion Estimation", 2023 Journal of Field Robotics

• Mollica G., Felicioni S., Legittimo M., Meli L., Costante G., Valigi P., "MA-VIED: A Multisensor Automotive Visual Inertial Event Dataset", 2023 IEEE Transactions on Intelligent Transportation Systems

Conference Papers

• Felicioni S., Castellini L., Tinti, L., Giorgetti F., Fravolini M.L., "Enhanced sensor fault detection in bridge monitoring using Siamese-based encoder-decoder", 2025 IEEE 21st International Conference on Automation Science and Engineering (CASE)

• Felicioni S., Castellani M., Costante G., Montecchiani F., Cavalagli N., Venanzi I., "Integrating robotics and immersive technologies for automated structural monitoring", submitted to 2025 XX Italian National Association of Earthquake Engineering Conference (ANIDIS)

• Felicioni S., Castellini L., Tinti L., D’Antoni F., Fravolini M. L., "Sensor Fault Detection in Bridge Monitoring Applications", submitted to 2025 Automatica.it

• Felicioni S., Burani E., Leomanni M., Fravolini M.L., Valigi P., Costante G., "Integrating Occupancy Grid with Semantic Road Information for Autonomous Navigation in Urban Scenarios: A Benchmark Study", 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)

• Felicioni S., Legittimo M., Fravolini M.L., Costante G., "GOLN: Graph Object-based Localization Network", 2021 20th International Conference on Advanced Robotics (ICAR)

• Felicioni S., Dimiccoli M., "InteractionGCN: A Graph Convolutional Network based framework for social interaction recognition in egocentric videos", 2021 IEEE International Conference on Image Processing (ICIP)

Theses

• Felicioni S., "Vision-based Robot Localization: from model-based and data-driven strategies to topological approaches". Doctoral (Ph.D.) Dissertation (Supervisor: Prof. Gabriele Costante), University of Perugia, Italy, 2023.

• Felicioni S., "Analysing social interactions through a wearable camera: a first-person point of view". Master's (M.Sc.) Thesis (Supervisor: Prof. Gabriele Costante, Advisor: Dr. Mariella Dimiccoli), University of Perugia, Italy, 2020.

Latest News

• My latest work "Enhanced Sensor Fault Detection in Bridge Monitoring using Siamese-based Encoder-Decoder" has been accepted for presentation at the IEEE 21st International Conference on Automation Science and Engineering (CASE 2025) in Los Angeles, California!
Presentation details: Tue, 19 Aug 2025 — 15:57–16:15 (local time)
Room: Moroccan  |  Session: TuCT9.5

• My RA-L paper "Vision-Based Topological Localization for MAVs"> has been accepted for presentation at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024) in Abu Dhabi, UAE!
👉 Check this out on Linkedin 👈