3D Object Recognition Based on Point Clouds in Underwater Environment with Global Descriptors: A Survey
dc.contributor.author
dc.date.accessioned
2019-10-21T06:52:41Z
dc.date.available
2019-10-21T06:52:41Z
dc.date.issued
2019-10-14
dc.identifier.uri
dc.description.abstract
This paper addresses the problem of object recognition from colorless 3D point clouds in
underwater environments. It presents a performance comparison of state-of-the-art global descriptors,
which are readily available as open source code. The studied methods are intended to assist
Autonomous Underwater Vehicles (AUVs) in performing autonomous interventions in underwater
Inspection, Maintenance and Repair (IMR) applications. A set of test objects were chosen as being
representative of IMR applications whose shape is typically known a priori. As such, CAD models
were used to create virtual views of the objects under realistic conditions of added noise and varying
resolution. Extensive experiments were conducted from both virtual scans and from real data collected
with an AUV equipped with a fast laser sensor developed in our research centre. The underwater
testing was conducted from a moving platform, which can create deformations in the perceived shape
of the objects. These effects are considerably more difficult to correct than in above-water counterparts,
and therefore may affect the performance of the descriptor. Among other conclusions, the testing we
conducted illustrated the importance of matching the resolution of the database scans and test scans,
as this significantly impacted the performance of all descriptors except one. This paper contributes to
the state-of-the-art as being the first work on the comparison and performance evaluation of methods
for underwater object recognition. It is also the first effort using comparison of methods for data
acquired with a free floating underwater platform
dc.description.sponsorship
This work was supported by the Spanish Government through a FPI Ph.D. grant to K.
Himri, as well as by the Spanish Project DPI2017-86372-C3-2-R (TWINBOT-GIRONA1000) and the H2020-INFRAIA-2017-1-twostage-731103 (EUMR)
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
MDPI (Multidisciplinary Digital Publishing Institute)
dc.relation
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-86372-C3-2-R/ES/ROBOT SUBMARINO COOPERATIVO PARA LA INTERVENCION/
dc.relation.isformatof
Reproducció digital del document publicat a: https://doi.org/10.3390/s19204451
dc.relation.ispartof
Sensors, 2019, vol. 19, núm. 20, p. 4451
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Articles publicats (D-ATC)
dc.rights
Attribution 4.0 International
dc.rights.uri
dc.subject
dc.title
3D Object Recognition Based on Point Clouds in Underwater Environment with Global Descriptors: A Survey
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.relation.projectID
info:eu-repo/grantAgreement/EC/H2020/731103/EU/Marine robotics research infrastructure network/EUMarineRobots
info:eu-repo/grantAgreement/EC/H2020/824077/EU/An alliance of European marine research infrastructure to meet the evolving needs of the research and industrial communities./EurofleetsPlus
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.identifier.doi
dc.contributor.funder
dc.type.peerreviewed
peer-reviewed
dc.relation.FundingProgramme
dc.relation.ProjectAcronym
dc.identifier.eissn
1424-8220