A Novel Obstacle Detection and Avoidance Dataset for Drones
Julien Dupeyroux (TU Delft - Control & Simulation)
Raoul Dinaux (Student TU Delft)
Nikhil Wessendorp (Student TU Delft)
G. C. H. E. de Croon (TU Delft - Control & Simulation)
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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].
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