REMODEL. WP4. Vision Based Perception. T4-4-2. Functional component detection. Sister Experimental Dataset. v0

De Gregorio, Daniele ; Poggi, Matteo ; Zama Ramirez, Pierluigi ; Palli, Gianluca ; Mattoccia, Stefano ; Di Stefano, Luigi (2022) REMODEL. WP4. Vision Based Perception. T4-4-2. Functional component detection. Sister Experimental Dataset. v0. University of Bologna. DOI 10.6092/unibo/amsacta/7060. [Dataset]
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Abstract

This dataset contains experimental data related to object 3D shape reconstruction from multiple viewpoints, produced in the framework of REMODEL project. Self-aware robots rely on depth sensing to interact with the surrounding environment, e.g. to pursue object grasping. Yet, dealing with tiny items, often occurring in industrial robotics scenarios, may represent a challenge due to lack of sensors yielding sufficiently accurate depth measurements. Existing active sensors fail at measuring details of small objects (<; 1cm) because of limitations in the working range, e.g. usually beyond 50 cm away, while off-the-shelf stereo cameras are not suited to close-range acquisitions due to the need for extremely short baselines. Therefore, we propose a framework designed for accurate depth sensing and particularly amenable to reconstruction of miniature objects. By leveraging on a single camera mounted in eye-on-hand configuration and the high repeatability of a robot, we acquire multiple images and process them through a stereo algorithm revised to fully exploit multiple vantage points. This dataset addresses performance evaluation in industrial applications using Single camera Stereo Robot (SiSteR), which delivers high accuracy even when dealing with miniature objects. The data are presented in the publication: D. De Gregorio, M. Poggi, P. Z. Ramirez, G. Palli, S. Mattoccia and L. Di Stefano, "Beyond the Baseline: 3D Reconstruction of Tiny Objects With Single Camera Stereo Robot," in IEEE Access, vol. 9, pp. 119755-119765, 2021, doi: 10.1109/ACCESS.2021.3108626.

Abstract
Tipologia del documento
Dataset
Autori
AutoreAffiliazioneORCID
De Gregorio, DanieleEyecan.ai Srl0000-0001-8203-9176
Poggi, MatteoUniversity of Bologna0000-0002-3337-2236
Zama Ramirez, PierluigiUniversity of Bologna
Palli, GianlucaUniversity of Bologna0000-0001-9457-4643
Mattoccia, StefanoUniversity of Bologna0000-0002-3681-7704
Di Stefano, LuigiUniversity of Bologna0000-0001-6014-6421
Settori scientifico-disciplinari
DOI
Contributors
Contributor
Affiliazione
ORCID
Tipo
Palli, Gianluca
University of Bologna
Contact person
Data di deposito
26 Ott 2022 10:19
Ultima modifica
26 Ott 2022 10:19
Risorse collegate
Tipologia
Relazione
Identificativo
Nome del Progetto
REMODEL - Robotic tEchnologies for the Manipulation of cOmplex DeformablE Linear objects
Programma di finanziamento
EC - H2020
URI

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