Field
Value
Language
dc.contributor.author
Labbé, Mathieu
datacite.creator.affiliationIdentifier
https://ror.org/00kybxq39
en_US
datacite.creator.affiliation
Université de Sherbrooke
en_US
datacite.creator.nameIdentifier
https://orcid.org/0000-0003-0778-5595
en_US
dc.date.accessioned
2024-04-05T13:05:09Z
dc.date.available
2024-04-05T13:05:09Z
dc.date.issued
2024-04-05
dc.identifier.uri
https://www.frdr-dfdr.ca/repo/dataset/2cb2a758-f9c4-460a-82b3-c1c206780b74
dc.identifier.uri
https://doi.org/10.20383/103.0931
dc.description
For robots navigating using only a camera, illumination changes in indoor environments can cause re-localization failures during autonomous navigation. In this paper, a multi-session visual SLAM approach is presented to create a map made of multiple variations of the same locations in different illumination conditions. The multi-session map can then be used at any hour of the day for improved re-localization capability. The approach presented is independent of the visual features used, and this is demonstrated by comparing re-localization performance between multi-session maps created using the RTAB-Map library with SURF, SIFT, BRIEF, BRISK, KAZE, DAISY and SuperPoint visual features. The approach is tested on six mapping and six localization sessions recorded at 30 minute intervals during sunset using a Google Tango phone in a real apartment, which is the dataset provided here.
This dataset has been used to evaluate the approach used in this paper:
M. Labbé and F. Michaud, “Multi-Session Visual SLAM for Illumination-Invariant Re-Localization in Indoor Environments,” in Frontiers in Robotics and AI, vol. 9, 2022.
en_US
dc.publisher
Federated Research Data Repository / dépôt fédéré de données de recherche
dc.rights
Creative Commons Public Domain Dedication (CC0 1.0)
en_US
dc.rights.uri
https://creativecommons.org/publicdomain/zero/1.0/
en_US
dc.subject
Localization
en_US
dc.subject
SLAM
en_US
dc.subject
Visual SLAM
en_US
dc.subject
Feature Matching
en_US
dc.subject
Mobile Robotics
en_US
dc.title
Multi-Session Visual SLAM for Illumination-Invariant Re-Localization in Indoor Environments
en_US
globus.shared_endpoint.name
f163c1b3-9c88-42f6-a7bb-5839ed6c4063
globus.shared_endpoint.path
/1/published/publication_926/
datacite.publicationyear
2024
datacite.contributor.ResearchGroup
IntRoLab
datacite.date.Collected
2019-03-01/2021-10-31
datacite.resourcetype
Dataset
en_US
datacite.relatedidentifier.IsCitedBy
https://doi.org/10.3389/frobt.2022.801886
datacite.relatedidentifier.IsSupplementedBy
https://github.com/introlab/rtabmap/tree/master/archive/2022-IlluminationInvariant
datacite.fundingReference.funderIdentifier
https://ror.org/01h531d29
en_US
datacite.fundingReference.funderName
Natural Sciences and Engineering Research Council of Canada
en_US
datacite.fundingReference.awardNumber
RGPIN-2016-05096
en_US
datacite.fundingReference.awardTitle
en_US
datacite.fundingReference.funderIdentifier
https://ror.org/00b9f9778
en_US
datacite.fundingReference.funderName
Fonds de Recherche du Québec – Nature et Technologies
en_US
datacite.fundingReference.awardNumber
en_US
datacite.fundingReference.awardTitle
en_US
datacite.fundingReference.funderIdentifier
en_US
datacite.fundingReference.funderName
INTER Strategic Network
en_US
datacite.fundingReference.awardNumber
2020-RS4-265381, 2018-RS-203302
en_US
datacite.fundingReference.awardTitle
en_US
frdr.crdc.code
RDF2030199
en_US
frdr.crdc.group_en
Electrical engineering, computer engineering, and information engineering
en_US
frdr.crdc.class_en
Computer engineering
en_US
frdr.crdc.field_en
Computer engineering, not elsewhere classified
en_US
frdr.crdc.group_fr
Génie électrique, génie informatique et génie de l'information
fr_CA
frdr.crdc.class_fr
Génie informatique
fr_CA
frdr.crdc.field_fr
Génie informatique, non classé ailleurs
fr_CA
datacite.description.other
IntRoLab: https://github.com/introlab/
en_US