首个面向洪水道路场景的多模态自动驾驶数据集，支持水患检测与定位研究。
FRED: A Multi-Modal Autonomous Driving Dataset for Flooded Road Environments
- 融合红外相机、激光雷达与惯导/定位系统，采集五地洪涝前后数据。
- 包含干湿条件对比数据及位置速度信息，支持地图辅助检测方法开发。
- 提供Kitti与RTMaps双格式，适配主流算法训练与实车回放测试。
FRED数据集是目前已知首个专门针对道路积水场景采集的多模态自动驾驶数据集。数据来自2.3 MP FLIR Blackfly USB3红外相机、64束线360度Ouster OS1-64激光雷达，以及经Geoflex RTK GNSS校准的iXblue ATLANS-C惯导系统，涵盖五个不同地点在洪水发生期间及之后的采集数据。数据以Kitti风格和RTMaps两种格式发布，便于集成现有工具或直接回放车辆采集数据。提供语义标签，可用于单传感器与多传感器融合方法的水患检测训练与评估。同时提供位置、速度信息及干燥条件下的数据，支持基于地图的位置相关检测方法开发，并可评估定位与SLAM等任务。
原文摘要 · Abstract (English)
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.
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