The precise estimation of scale in Structure from Motion (SfM) pipelines holds paramount significance for robotic systems, influencing their navigational capabilities, object manipulation, and decision-making processes. This paper presents an innovative prior-knowledge approach designed to address the challenge of scale ambiguity in monocular robots by strategically utilizing beacons positioned at known locations within the environment. Our methodology integrates well-established optimization techniques into a highly modular pipeline, offering adaptability to a spectrum of use cases and requirements. To validate the effectiveness of our approach, we conducted benchmarking experiments utilizing synthetic data (ICL-NUIM) and simulated data. The evaluation of our method on the ICL-NUIM dataset underscores its capability to correct the scale drift with comparable accuracy. The results highlight the potential of our approach to serve as a robust system across diverse scenarios, showcasing its viability for implementation in real-world applications.

Robust Scale Estimation System for Monocular Mobile Robots Using Beacon-Based Structure from Motion / Iacobelli, E.; Ospizio, L.; Tassone, F. R.; Starczewski, J.; Napoli, C.. - 15165:(2025), pp. 270-284. ( 23rd International Conference on Artificial Intelligence and Soft Computing, ICAISC 2024 Zakopane; pol ) [10.1007/978-3-031-84356-3_22].

Robust Scale Estimation System for Monocular Mobile Robots Using Beacon-Based Structure from Motion

Iacobelli E.
Primo
Investigation
;
Napoli C.
Ultimo
Supervision
2025

Abstract

The precise estimation of scale in Structure from Motion (SfM) pipelines holds paramount significance for robotic systems, influencing their navigational capabilities, object manipulation, and decision-making processes. This paper presents an innovative prior-knowledge approach designed to address the challenge of scale ambiguity in monocular robots by strategically utilizing beacons positioned at known locations within the environment. Our methodology integrates well-established optimization techniques into a highly modular pipeline, offering adaptability to a spectrum of use cases and requirements. To validate the effectiveness of our approach, we conducted benchmarking experiments utilizing synthetic data (ICL-NUIM) and simulated data. The evaluation of our method on the ICL-NUIM dataset underscores its capability to correct the scale drift with comparable accuracy. The results highlight the potential of our approach to serve as a robust system across diverse scenarios, showcasing its viability for implementation in real-world applications.
2025
23rd International Conference on Artificial Intelligence and Soft Computing, ICAISC 2024
Iterative Closest Point Optimization; Machine Learning; Monocular Structure from Motion; Robotics Systems; Scale Estimation
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Robust Scale Estimation System for Monocular Mobile Robots Using Beacon-Based Structure from Motion / Iacobelli, E.; Ospizio, L.; Tassone, F. R.; Starczewski, J.; Napoli, C.. - 15165:(2025), pp. 270-284. ( 23rd International Conference on Artificial Intelligence and Soft Computing, ICAISC 2024 Zakopane; pol ) [10.1007/978-3-031-84356-3_22].
File allegati a questo prodotto
File Dimensione Formato  
Iacobelli_Robust-Scale_2025.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 1.26 MB
Formato Adobe PDF
1.26 MB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1743703
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact