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. <strong>Necessary files to implement SLAM are included</strong> 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)