Field
Value
Language
dc.contributor.author
Marques de Araujo, Paulo Ricardo
datacite.creator.affiliationIdentifier
https://ror.org/02y72wh86
en_US
datacite.creator.affiliation
Queen's University
en_US
datacite.creator.nameIdentifier
https://orcid.org/0000-0002-8027-5578
en_US
dc.contributor.author
Mounier, Eslam
datacite.creator.affiliationIdentifier
https://ror.org/02y72wh86;https://ror.org/00cb9w016
en_US
datacite.creator.affiliation
Queen's University;Ain Shams University
en_US
datacite.creator.nameIdentifier
https://orcid.org/0000-0002-7020-2842
en_US
dc.contributor.author
Bader, Qamar
datacite.creator.affiliationIdentifier
https://ror.org/02y72wh86
en_US
datacite.creator.affiliation
Queen's University
en_US
datacite.creator.nameIdentifier
https://orcid.org/0000-0002-4667-1710
en_US
dc.contributor.author
Dawson, Emma
datacite.creator.affiliationIdentifier
https://ror.org/02y72wh86
en_US
datacite.creator.affiliation
Queen's University
en_US
datacite.creator.nameIdentifier
https://orcid.org/0000-0002-8321-5124
en_US
dc.contributor.author
Ismail Kaoud Abdelaziz, Shaza
datacite.creator.affiliationIdentifier
https://ror.org/02y72wh86
en_US
datacite.creator.affiliation
Queen's University
en_US
datacite.creator.nameIdentifier
https://orcid.org/0000-0002-5664-5735
en_US
dc.contributor.author
Zekry, Ahmed
datacite.creator.affiliationIdentifier
https://ror.org/02y72wh86
en_US
datacite.creator.affiliation
Queen's University
en_US
datacite.creator.nameIdentifier
https://orcid.org/0000-0002-0702-3331
en_US
dc.contributor.author
Elhabiby, Mohamed
datacite.creator.affiliationIdentifier
https://ror.org/00cb9w016
datacite.creator.affiliation
Ain Shams University
datacite.creator.nameIdentifier
https://orcid.org/0000-0002-1909-7506
dc.contributor.author
Noureldin, Aboelmagd
datacite.creator.affiliationIdentifier
https://ror.org/04yr71909;https://ror.org/02y72wh86
datacite.creator.affiliation
Royal Military College of Canada;Queen's University
datacite.creator.nameIdentifier
https://orcid.org/0000-0001-6614-7783
dc.date.accessioned
2024-11-18T20:01:37Z
dc.date.available
2024-11-18T20:01:37Z
dc.date.issued
2024-11-18
dc.identifier.uri
https://doi.org/10.20383/103.01089
dc.description
This is a comprehensive multi-sensory dataset from various road trajectories in Kingston, ON, and Calgary, AB, Canada. This dataset features diverse lighting conditions and includes data from two indoor garages, with corresponding 3D maps. The dataset includes data from multiple commercial-grade IMUs and a tactical-grade IMU, along with a wide range of perception sensors. Notably, it is one of the first datasets to feature a solid-state LiDAR, alongside a mechanical LiDAR, four electronically scanning RADARs, a monocular camera, and two stereo cameras. Additionally, the dataset contains forward speed measurements from the vehicle's odometer and accurately post-processed high-end GNSS/IMU data, providing precise ground truth positioning and navigation information. The NavINST dataset is designed to facilitate advanced research in high-precision positioning, navigation, mapping, computer vision, and multi-sensor fusion, offering rich data for developing and validating robust autonomous vehicle algorithms. Finally, the dataset is fully integrated with ROS to ensure ease of use and accessibility for the research community. It also includes a toolkit for users working offline.
en_US
dc.publisher
Federated Research Data Repository / dépôt fédéré de données de recherche
dc.rights
Creative Commons Attribution 4.0 International (CC BY 4.0)
en_US
dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
en_US
dc.subject
Autonomous vehicles
en_US
dc.subject
multi-sensor system dataset
en_US
dc.subject
indoor navigation
en_US
dc.subject
urban navigation
en_US
dc.subject
solid-state lidar
en_US
dc.subject
3D maps
en_US
dc.title
The NavINST Dataset for Multi-Sensor Autonomous Navigation
en_US
globus.shared_endpoint.name
f163c1b3-9c88-42f6-a7bb-5839ed6c4063
globus.shared_endpoint.path
/1/published/publication_1084/
datacite.publicationYear
2024
datacite.resourceType
Dataset
en_US
datacite.relatedIdentifier.IsSupplementedBy
https://navinst.github.io/
datacite.fundingReference.funderIdentifier
https://ror.org/01h531d29
en_US
datacite.fundingReference.funderName
Natural Sciences and Engineering Research Council
en_US
datacite.fundingReference.awardNumber
RGPIN-2020-03900
en_US
datacite.fundingReference.awardTitle
en_US
datacite.fundingReference.funderIdentifier
https://ror.org/01h531d29
en_US
datacite.fundingReference.funderName
Natural Sciences and Engineering Research Council
en_US
datacite.fundingReference.awardNumber
ALLRP-560898-20
en_US
datacite.fundingReference.awardTitle
en_US
frdr.crdc.code
RDF2029999
en_US
frdr.crdc.group_en
Industrial, systems and processes engineering
en_US
frdr.crdc.class_en
Other industrial, systems and processes engineering
en_US
frdr.crdc.field_en
Other industrial, systems and processes engineering
en_US
frdr.crdc.group_fr
Génie industriel, des systèmes et des procédés
fr_CA
frdr.crdc.class_fr
Autre génie industriel, des systèmes et des procédés
fr_CA
frdr.crdc.field_fr
Autre génie industriel, des systèmes et des procédés
fr_CA
