CardiffNav: A GNSS-centric Multisensory Dataset for Robust Localisation in Diverse and Challenging Environments

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.