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Open Spatial Dataset for GNSS and Autonomous Navigation
  • +4
  • Timothy Pelham,
  • Y Chen,
  • S Liu,
  • Z Ji,
  • E Anyaegbu,
  • R Wong,
  • R Grech
Timothy Pelham
University of Bristol

Corresponding Author:[email protected]

Author Profile
Y Chen
Cardiff University
S Liu
Cardiff University
Z Ji
Cardiff University
E Anyaegbu
Spirent Communications
R Wong
Spirent Communications
R Grech
Spirent Communications


Global Navigation Satellite System (GNSS) data has become a stable of modern life, whether delivered through a smartphone, vehicle, or as part of an industrial dataset. The expectation of reliable position and time information, irrespective of the environment, becomes challenging in tunnels and canyons with significant multipath, whether natural or urban. An open dataset of Lidar, Computer Vision, Inertial measurement, and GNSS data is presented, combined with preliminary analysis of the dataset on a selected recording, considering GPS coarse acquisition, lidar and computer vision based navigation together with model based channel reconstruction. Lidar and video based techniques demonstrate positioning error as low as 3.8m root mean square error, together with GNSS acquisition and signal blockage alignment with computer vision and lidar maps.
30 Mar 2024Submitted to TechRxiv
01 Apr 2024Published in TechRxiv