The Obstacle Detection and Avoidance Dataset for Drones

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>

本研究推出面向无人机的障碍物检测与避障数据集（Obstacle Detection and Avoidance Dataset for Drones），旨在提供适配空中机器人平台的真实室内环境原始传感数据。本研究搭载的微小型无人机（micro air vehicle, MAV）配备如下传感器：（i）事件相机（event-based camera），其动态表现专为无人机应用场景优化；（ii）标准RGB相机；（iii）24GHz雷达传感器，用于强化多传感融合方案；（iv）六轴惯性测量单元（Inertial Measurement Unit, IMU）。数据集的真实位置与姿态真值由OptiTrack动作捕捉系统提供。最终生成的数据集包含超过1350组样本，采集自四种不同工况：单障碍物或双障碍物环境、强光或弱光环境。本数据集可用于面向无人机（Unmanned Aerial Vehicle, UAV）的障碍物检测与避障算法、神经网络方案的基准测试，同时也可应用于航迹估计乃至自主导航相关研究。如需进一步了解详情，请访问：https://github.com/tudelft/ODA_Dataset