Liu, Shun, Chen, Yixin, Pullin, Rhys  ORCID: https://orcid.org/0000-0002-2853-6099, Ning, Zekun, Grech, Raphael and Ji, Ze ORCID: https://orcid.org/0000-0002-8968-9902
2026.
CardiffNav: A GNSS-centric multisensory dataset for robust localisation in diverse and challenging environments.
Presented at: International Conference on Mechatronics and Automation,
Changchun, China,
2-5 August 2026.
Proceedings of 2026 ICMA.
IEEE,
pp. 1171-1177.
10.1109/icma69663.2026.11647401
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Abstract
Robust state estimation via multi-sensor fusion is strictly constrained in urban environments, driven primarily by Non-Line-of-Sight (NLOS) and multipath interference acting upon Global Navigation Satellite System (GNSS) signals. Current benchmark datasets systematically omit the raw GNSS observables requisite for formulating tightly-coupled mitigation mechanisms. To resolve this, we release CardiffNav, a multi-modal sensor dataset engineered for degraded-environment localisation. The hardware framework synchronises a 128-channel Light Detection and Ranging (LiDAR) sensor, a visual perception array (RGB, stereo, and event cameras), and a 9-axis Inertial Measurement Unit (IMU). Concurrently, the system logs raw multi-constellation, multi-frequency GNSS measurements alongside Intermediate Frequency (IF) signal samples. Recorded trajectories traverse a continuous gradient of signal availability, explicitly documenting the operational transitions across open-sky segments, structural highways, dense urban canyons, and GNSS-denied tunnels. Baseline evaluations indicate that the unconstrained integration of degraded GNSS measurements directly corrupts the coupled state estimate. This dataset consequently provides a rigorous testbed for validating algorithms designed to identify, decouple, and mitigate signal degradation at the measurement level. The complete dataset and benchmark utilities are available at https://github.com/Erika1kuta/CardiffNav.
| Item Type: | Conference or Workshop Item - published (Paper) |
|---|---|
| Date Type: | Published Online |
| Status: | Published |
| Schools: | Schools > Engineering |
| Additional Information: | RRS policy applied |
| Publisher: | IEEE |
| ISBN: | 9798331562847 |
| ISSN: | 2152-7431 |
| Date of First Compliant Deposit: | 25 August 2026 |
| Last Modified: | 25 Aug 2026 09:00 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/189142 |
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