Publikationer från Malmö universitet
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Creating navigation map in semi-open scenarios for intelligent vehicle localization using multi-sensor fusion
Jiangsu Univ, Automot Engn Res Inst, Zhenjiang 212013, Jiangsu, Peoples R China.;Wuhan Univ Technol, Hubei Key Lab Transportat Internet Things, Wuhan 430063, Peoples R China..
Jiangsu Univ, Automot Engn Res Inst, Zhenjiang 212013, Jiangsu, Peoples R China..
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).ORCID-id: 0000-0002-2763-8085
Jiangsu Univ, Automot Engn Res Inst, Zhenjiang 212013, Jiangsu, Peoples R China..
Vise andre og tillknytning
2021 (engelsk)Inngår i: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 184, artikkel-id 115543Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

In order to pursue high-accuracy localization for intelligent vehicles (IVs) in semi-open scenarios, this study proposes a new map creation method based on multi-sensor fusion technique. In this new method, the road scenario fingerprint (RSF) was employed to fuse the visual features, three-dimensional (3D) data and trajectories in the multi-view and multi-sensor information fusion process. The visual features were collected in the front and downward views of the IVs; the 3D data were collected by the laser scanner and the downward camera and a homography method was proposed to reconstruct the monocular 3D data; the trajectories were computed from the 3D data in the downward view. Moreover, a new plane-corresponding calibration strategy was developed to ensure the fusion quality of sensory measurements of the camera and laser. In order to evaluate the proposed method, experimental tests were carried out in a 5 km semi-open ring route. A series of nodes were found to construct the RSF map. The experimental results demonstrate that the mean error of the nodes between the created and actual maps was 2.7 cm, the standard deviation of the nodes was 2.1 cm and the max error was 11.8 cm. The localization error of the IV was 10.8 cm. Hence, the proposed RSF map can be applied to semi-open scenarios in practice to provide a reliable basic for IV localization.

sted, utgiver, år, opplag, sider
Elsevier, 2021. Vol. 184, artikkel-id 115543
Emneord [en]
Intelligent vehicles, Road scenario fingerprint, Multi-view representation, Multi-sensor fusion, Map-based localization
HSV kategori
Identifikatorer
URN: urn:nbn:se:mau:diva-46270DOI: 10.1016/j.eswa.2021.115543ISI: 000697191700005Scopus ID: 2-s2.0-85109448854OAI: oai:DiVA.org:mau-46270DiVA, id: diva2:1602615
Tilgjengelig fra: 2021-10-13 Laget: 2021-10-13 Sist oppdatert: 2024-02-05bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekstScopus

Person

Malekian, Reza

Søk i DiVA

Av forfatter/redaktør
Malekian, Reza
Av organisasjonen
I samme tidsskrift
Expert systems with applications

Søk utenfor DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 65 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf