The Obstacle Detection and Avoidance Dataset for Drones

doi:10.4121/14214236.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/14214236
Datacite citation style:
Julien Dupeyroux; Nikhil Wessendorp; Raoul Dinaux; Guido De Croon (2021): The Obstacle Detection and Avoidance Dataset for Drones. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/14214236.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
We introduce the Obstacle Detection and Avoidance Dataset for Drones, aiming at providing raw data obtained in a real indoor environment with sensors adapted for aerial robotics. Our micro air vehicle (MAV) is equipped with the following sensors: (i) an event-based camera, the dynamic performance of which make it optimized for drone applications; (ii) a standard RGB camera; (iii) a 24-GHz radar sensor to enhance multi-sensory solutions; and (iv) a 6-axes IMU. The ground truth position and attitude are provided by the OptiTrack motion capture system. The resulting dataset consists in more than 1350 samples obtained in four distinct conditions (one or two obstacles, full or dim light). It is intended for benchmarking algorithmic and neural solutions for obstacle detection and avoidance with UAVs, but also course estimation and therefore autonomous navigation. For further information, please visit: https://github.com/tudelft/ODA_Dataset
history
  • 2021-03-19 first online, published, posted
publisher
4TU.ResearchData
funding
  • Framework of key enabling technologies for safe and autonomous drones' applications (COMP4DRONES) (grant code 826610) [more info...] Ministry of Education and Science
organizations
TU Delft, Faculty of Aerospace Engineering

DATA

files (1)