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)

2023