Existing autonomous driving datasets are predominantly oriented towards well-structured urban settings and favorable weather conditions, leaving the complexities of rural environments and adverse weather conditions largely unaddressed. Although some datasets encompass variations in weather and lighting, bad weather scenarios do not appear often. Rainfall can significantly impair sensor functionality, introducing noise and reflections in LiDAR and camera data and reducing the system's capabilities for reliable environmental perception and safe navigation. We introduce the Panoptic-CUDAL dataset, a novel dataset purpose-built for panoptic segmentation in rural areas subject to rain. By recording high-resolution LiDAR, camera, and pose data, Panoptic-CUDAL offers a diverse, information-rich dataset in a challenging scenario. We present analysis of the recorded data and provide baseline results for panoptic and semantic segmentation methods on LiDAR point clouds. The dataset can be found here: https://robotics.sydney.edu.au/our-research/intelligent-transportation-systems/

翻译：现有的自动驾驶数据集主要面向结构良好的城市环境和有利天气条件，而乡村环境的复杂性和恶劣天气条件在很大程度上仍未得到解决。尽管部分数据集涵盖了天气和光照变化，但恶劣天气场景的出现频率较低。降雨会显著削弱传感器功能，在LiDAR和相机数据中引入噪声和反射，降低系统对环境进行可靠感知和安全导航的能力。我们推出了全景-CUDAL数据集，这是一个专为雨天条件下乡村区域全景分割任务构建的新型数据集。通过记录高分辨率LiDAR、相机及位姿数据，全景-CUDAL为这一挑战性场景提供了多样化、信息丰富的数据资源。我们对采集数据进行了分析，并提供了LiDAR点云全景分割与语义分割方法的基准测试结果。数据集访问地址：https://robotics.sydney.edu.au/our-research/intelligent-transportation-systems/