cff-version: 1.2.0
abstract: "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 <br>"
authors:
  - family-names: Dupeyroux
    given-names: Julien
  - family-names: Wessendorp
    given-names: Nikhil
  - family-names: Dinaux
    given-names: Raoul
  - family-names: De Croon
    given-names: Guido
title: "The Obstacle Detection and Avoidance Dataset for Drones"
keywords:
version: 1
identifiers:
  - type: doi
    value: 10.4121/14214236.v1
license: CC0
date-released: 2021-03-19