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CONTEST dataset

Collaborative pOsitioning and NavigaTion bEtween ground and uaS plaTforms


GNSS allows positioning and navigation of ground and aerial platforms/agets almost everywhere and has been widely used in a large variety of devices and applications.

But, despite developments in hardware technologies and Radio-Frequency (RF) signal processing, GNSS cannot be fully available or reliable in certain environments (e.g. dense forestry, tunnels, urban canyons, etc.) or when RF interference is present (e.g. spoofing or jamming).

Therefore, alternative positioning approaches were studied, including collaborative navigation, where platforms navigating in close vicinity can share position and navigation information (vehicle-to-infrastructure - V2I and vehicle-to-vehicle - V2V communications) and a joint navigation solution can potentially provide better results for all platforms with respect to individual ones.

The CONTEST dataset wants to provide multiple data streams to test collaborative positioning approaches, involving both terrestrial and aerial (Unmanned Aerial Systems / Vehicles) platforms, and using several sensors, such as Ultra-Wide Band (UWB) transceivers, cameras, LiDARs, GNSS. The CONTEST data can therefore support the development, testing and comparison of individual or integrated use of imaging data, LiDAR and UWB ranging for collaborative positioning and navigation purposes. This includes the possibility to execute and validate visual and LiDAR odometry or SLAM approaches, hybrid adjustment, UWB trilateration algorithms, etc. To ensure reliable validation results, accurate GNSS-based reference solutions are provided for all the platforms.

The CONTEST dataset is joint work bewtween the University of Florence (Italy), The Ohio State University (USA) and Bruno Kessler Foundation - FBK (Italy).


Related publications:

If you use this dataset for your research, please cite the data source (https://github.com/3DOM-FBK/Collaborative_Navigation).


Link to dataset grouped by Agent:

Agent Link Agent Link Agent Link
UAS1 Link CAR0 Link Pedestrian1 Link
UAS2 Link CAR1 Link Pedestrian2 Link
UAS3 Link CAR2 Link Cyclist1 Link
UAS4 Link CAR3 Link Cyclist2 Link

For Static LiDAR: Link

All collected data are listed below.

Static Infrastructure

Sensor

Collected Data

Shared Data

12 UWB Pozyx - reference positions
LiDAR: Velodyne VLP16 raw profiles raw profiles with timestamp

Agents

Onboard sensor

Collected Data

Shared Data

UAS1 UWB: Pozyx ranges ranges wrt static and moving agents
UAS1 embedded GNSS positions reference trajectory
UAS1 embedded camera images images
UAS2 UWB: Pozyx ranges ranges wrt static and moving agents
UAS2 GNSS: Emlid M2 positions reference trajectory
UAS2 embedded camera images images
UAS3 UWB: Pozyx ranges ranges wrt static and moving agents
UAS3 GNSS: Emlid M2 positions reference trajectory
UAS3 embedded camera images, videos images, videos
UAS4 UWB: Pozyx ranges ranges wrt static and moving agents
UAS4 embedded GNSS positions reference trajectory
UAS4 Camera: DJI FC6310S images, videos images, videos
CAR0 UWB: Pozyx ranges ranges wrt static and moving agents
CAR0 GNSS: Leica GS25, Septentrio PolRx5 positions reference trajectory from GNSS and IMU integration and correction
CAR0 IMU: Honeywell H764G inertial info reference trajectory from GNSS and IMU integration and correction
CAR0 LiDAR Velodyne VLP16 raw profiles raw profiles with timestamp
CAR1 UWB: Pozyx ranges ranges wrt static and moving agents
CAR1 GNSS: Topcon Hyper VR, Novatel PwrPak7 positions reference trajectory from GNSS and IMU integration and correction
CAR1 IMU: SPAN-IGM-S1 inertial info reference trajectory from GNSS and IMU integration and correction
CAR1 LiDAR Velodyne VLP16 raw profiles raw profiles with timestamp
CAR1 Camera: Sony Alpha 6000 video video
CAR2 UWB: Pozyx - ranges wrt static and moving agents
CAR2 GNSS: 1 Topcon Hyper VR, 1 Novatel PwrPak7 positions reference trajectory from GNSS and IMU integration and correction
CAR2 IMU: Epson G320 MEMS (built-in) inertial info reference trajectory from GNSS and IMU integration and correction
CAR2 LiDAR: Velodyne VLP16 raw profiles raw profiles with timestamp
CAR2 Camera: GoPro HERO5 video video
CAR3 UWB: Pozyx ranges ranges wrt static and moving agents
CAR3 GNSS: 1 Topcon Hyper VR, 1 Novatel PwrPak7 positions reference trajectory from GNSS and IMU integration and correction
CAR3 IMU: Epson G320 MEMS (built-in) inertial info reference trajectory from GNSS and IMU integration and correction
CAR3 LiDAR: Velodyne VLP16 raw profiles raw profiles with timestamp
CAR3 Camera: GoPro HERO5 video video
Pedestrian1 GNSS: Topcon Hyper VR positions reference trajectory
Pedestrian2 GNSS: Topcon Hyper VR positions reference trajectory
Cyclist1 GNSS: Topcon Hyper VR positions reference trajectory
Cyclist2 GNSS: Topcon Hyper VR positions reference trajectory
Static LiDAR

License

The data provided here is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.