VIO-GNSS Dataset: Benchmarking Dataset for Sensor Fusion of Visual Inertial Odometry and GNSS Positioning

This upload contains datasets for benchmarking and improving different Sensor Fusion implementations/algorithms. The documentation for these datasets can be found on GitHub.

The upload contains two datasets (version 1.0.0):

• urban_with_gnss_dead_zones (7.0 GB, ~16 minutes)
  • City streets
  • A building is passed through on two occasions which makes the GNSS location signal unavailable at times.
  • RTK Fix is acquired at times
• suburban_nature (10.6 GB, ~19 minutes)
  • The route begins on a suburban street but quickly turns into a nature trail. Lots of vegetation
  • The RTK solution is only Float or None most of the route.

Details on collecting the data:

• Software
  • The data was collected using this open-source recorder.
    • Can be easily replayed using SpectacularAI's SDK (sdk-examples/python/oak/vio_replay.py)
  • Each dataset contains a map of the travelled route in Otaniemi, Espoo, Finland.
  • Necessary files to implement SLAM are included in the dataset.
  • Use of NTRIP and the high precision GNSS antenna enables global positioning accuracy of only few centimeters.
• Hardware
  • OAK-D stereo depth + color camera (Luxonis)
  • C099-F9P GNSS module (u-blox)
  • ANN-MB-00 high precision GNSS antenna (u-blox)