FRED：面向洪水道路环境的多模态自动驾驶数据集
FRED: A Multi-Modal Autonomous Driving Dataset for Flooded Road Environments
AI总结：
提出首个针对道路水险场景的多模态自动驾驶数据集FRED，包含相机、LiDAR和IMU数据，并提供语义标签以支持水险检测方法训练与评估。
摘要：
洪水道路环境数据集（FRED）是，据我们所知，首个专门针对道路水险场景数据收集的多模态自动驾驶数据集。该数据集包含来自2.3 MP FLIR Blackfly USB3相机的图像、来自Ouster OS1-64 LiDAR的64线360度点云，以及由Geoflex RTK GNSS校正的iXblue ATLANS-C IMU数据，数据采集自五个不同地点，涵盖洪水期间和洪水之后。数据以两种格式发布：KITTI风格格式，便于与现有数据工具集成；以及RTMaps格式，用于直接回放车辆的数据捕获。我们提供语义标签，以支持用于水险检测的单传感器和传感器融合方法的训练与评估。提供位置和速度数据，以及干燥条件下捕获的数据，以支持可能包含地图的基于位置的检测方法开发，并评估其他任务，如定位和SLAM。
英文摘要：
The Flooded Road Environments Dataset (FRED) is, to our knowledge, the first multi-modal autonomous driving dataset specifically targeting the collection of data from scenarios involving water hazards on the road. The dataset contains images from a 2.3 MP FLIR Blackfly USB3 camera, 64-beam 360 degree point clouds from an Ouster OS1-64 LiDAR, and data from an iXblue ATLANS-C IMU corrected by a Geoflex RTK GNSS, from five separate locations captured both during and after flooding events. The data has been released in two formats: a KITTI-style format for easy integration with existing data tools, and the RTMaps format for direct replay of the vehicle's data capture. We provide semantic labels to enable the training and evaluation of both single-sensor and sensor-fusion methods for water hazard detection. Position and velocity, as well as data captured under dry conditions, are provided to enable the development of location-based detection methods that may incorporate maps, and to evaluate other tasks such as localisation and SLAM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
