Slam, Intelligenza Artificiale e Robotica per la mappatura di ambienti affollati

Autori

  • Eleonora Maset
  • Lorenzo Scalera

Parole chiave:

slam, robotica, lidar, rete neurale, intelligenza artificiale

Abstract

Contamination between photogrammetry and computers vision is a process which began at least a decade ago and yes is now in a mature stage, though not yet complete. Also, we are currently assisting at the entrance of technologies arising from robotics in the field of topography, such as i portable mapping systems mobile (MMS – Mobile Mapping Systems).

Riferimenti bibliografici

Adán, A., Quintana, B., Prieto, S. A., 2019. Autonomous mobile scanning systems for the digitization of buildings: A review. Remote Sensing, 11(3), 306.

Tiozzo Fasiolo, D., Maset, E., Scalera, L., Macaulay, S. O., Gasparetto, A., Fusiello, A. (2022). Combining LiDAR SLAM and deep learning-based people detection for autonomous indoor mapping in a crowded environment. The International Archives of Photogrammetry, Remote

Sensing and Spatial Information Sciences, 43, 447-452.

Maset, E., Scalera, L., Beinat, A., Visintini, D., Gasparetto, A. (2022). Performance

investigation and repeatability assessment of a mobile robotic system for 3D mapping. Robotics, 11(3), 54.

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Pubblicato

2023-03-28

Come citare

Maset, E., & Scalera, L. (2023). Slam, Intelligenza Artificiale e Robotica per la mappatura di ambienti affollati. GEOmedia, 26(6). Recuperato da https://www.mediageo.it/ojs/index.php/GEOmedia/article/view/1908

Fascicolo

Sezione

REPORT