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Visual and light detection and ranging-based simultaneous localization and mapping for self-driving cars

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 12 No. 6 (2022)

DOI
10.11591/ijece.v12i6.pp6284-6292
ISSN
2722-2578, 2088-8708
Published
2022-12-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

In recent years, there has been a strong demand for self-driving cars. For safe navigation, self-driving cars need both precise localization and robust mapping. While global navigation satellite system (GNSS) can be used to locate vehicles, it has some limitations, such as satellite signal absence (tunnels and caves), which restrict its use in urban scenarios. Simultaneous localization and mapping (SLAM) are an excellent solution for identifying a vehicle’s position while at the same time constructing a representation of the environment. SLAM-based visual and light detection and ranging (LIDAR) refer to using cameras and LIDAR as source of external information. This paper presents an implementation of SLAM algorithm for building a map of environment and obtaining car’s trajectory using LIDAR scans. A detailed overview of current visual and LIDAR SLAM approaches has also been provided and discussed. Simulation results referred to LIDAR scans indicate that SLAM is convenient and helpful in localization and mapping.

Collaboration network of the authors

26 authors · 73 co-authorship links · 19 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Université Hassan 1er
  • Institut National des Postes et Télécommunications
  • École Supérieure d'Électronique de l'Ouest
  • Universidad de Cantabria
  • University of Hassan II Casablanca
  • Mohammed V University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Ahmed MouhsenUniversité Hassan 1er2
Hicham OuldziraUniversité Hassan 1er2
Mohamed LamhamdiUniversité Hassan 1er2
Jamal ZbitouUniversité Hassan 1er1
Abdelwahed TribakInstitut National des Postes et Télécommunications1
Mohamed LatrachÉcole Supérieure d'Électronique de l'Ouest1
A. M. SanchezUniversidad de Cantabria1
Mostafa ChhibaUniversité Hassan 1er1
Abdelmoumen TabyaouiUniversité Hassan 1er1
Hajar LagrainiUniversité Hassan 1er1
Ezzitouni JarmouniUniversité Hassan 1er1
Elmehdi EnnajihUniversité Hassan 1er1
Naoual AjediouiUniversité Hassan 1er1
Ilias En-NaouiUniversité Hassan 1er1
Alami EssaadouiUniversity of Hassan II Casablanca1
Mustapha El HanineUniversité Hassan 1er1
Hassane Mes-AdiAffiliation unknown1
Azeddine MouhsenUniversité Hassan 1er1
Adil El KassoumiUniversité Hassan 1er1
Mohammed FdailiMohammed V University1
Imad AboudrarAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Visual and light detection and ranging-based simultaneous localization and mapping for self-driving cars</dc:title><dc:creator>Abdelhafid, El Farnane</dc:creator><dc:creator>Abdelkader, Youssefi My</dc:creator><dc:creator>Ahmed, Mouhsen</dc:creator><dc:creator>Rachid, Dakir</dc:creator><dc:creator>Abdelilah, El Ihyaoui</dc:creator><dc:description>&lt;jats:p&gt;&amp;lt;span lang="EN-US"&amp;gt;In recent years, there has been a strong demand for self-driving cars. For safe navigation, self-driving cars need both precise localization and robust mapping. While global navigation satellite system (GNSS) can be used to locate vehicles, it has some limitations, such as satellite signal absence (tunnels and caves), which restrict its use in urban scenarios. Simultaneous localization and mapping (SLAM) are an excellent solution for identifying a vehicle’s position while at the same time constructing a representation of the environment. SLAM-based visual and light detection and ranging (LIDAR) refer to using cameras and LIDAR as source of external information. This paper presents an implementation of SLAM algorithm for building a map of environment and obtaining car’s trajectory using LIDAR scans. A detailed overview of current visual and LIDAR SLAM approaches has also been provided and discussed. Simulation results referred to LIDAR scans indicate that SLAM is convenient and helpful in localization and mapping.&amp;lt;/span&amp;gt;&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2022-12-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v12i6.pp6284-6292</dc:identifier><dc:identifier>10.11591/ijece.v12i6.pp6284-6292</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 12 No 6 (2022); 6284</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>https://ijece.iaescore.com/index.php/IJECE/article/viewFile/28721/16078</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>