@inproceedings{e2f12fc107bf4209989819fcf3163261,
title = "A Novel Obstacle Detection and Avoidance Dataset for Drones",
abstract = "In this paper, we introduce the Obstacle Detection and Avoidance (ODA) Dataset for Drones, aiming at providing raw data obtained in a real indoor environment with sensors adapted for aerial robotics in the context of obstacle detection and avoidance. Our micro air vehicle (MAV) is equipped with the following sensors: (i) an event-based camera, the performance of which makes 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 an OptiTrack motion capture system. The resulting dataset consists of more than 1350 sequences 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 in general autonomous navigation. The dataset is available at: https://github.com/tudelft/ODA_Dataset [6]. ",
keywords = "Camera, Event-based Camera, Micro Air Vehicles (MAVs), Neuromorphic Vision, Radar, Robot Operating System (ROS), Unmanned Aerial Vehicles (UAVs)",
author = "Julien Dupeyroux and Raoul Dinaux and Nikhil Wessendorp and {De Croon}, Guido",
year = "2022",
doi = "10.1145/3522784.3522786",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery (ACM)",
pages = "8--13",
booktitle = "Proceedings of System Engineering for Constrained Embedded Systems - DroneSE",
address = "United States",
note = "2022 Workshop on System Engineering for Constrained Embedded Systems - Drone Systems Engineering and Rapid Simulation and Performance Evaluation: Methods and Tools, DroneSE and RAPIDO 2022 - Presented at HiPEAC 2022 Conference ; Conference date: 20-06-2022 Through 22-06-2022",
}