Algorithms for autonomous navigation in environments without Global Navigation Satellite System (GNSS) coverage mainly rely on onboard perception systems. These systems commonly incorporate sensors like cameras and Light Detection and Rangings (LiDARs), the performance of which may degrade in the presence of aerosol particles. Thus, there is a need of fusing acquired data from these sensors with data from Radio Detection and Rangings (RADARs) which can penetrate through such particles. Overall, this will improve the performance of localization and collision avoidance algorithms under such environmental conditions. This paper introduces a multimodal dataset from the harsh and unstructured underground environment with aerosol particles. A detailed description of the onboard sensors and the environment, where the dataset is collected are presented to enable full evaluation of acquired data. Furthermore, the dataset contains synchronized raw data measurements from all onboard sensors in Robot Operating System (ROS) format to facilitate the evaluation of navigation, and localization algorithms in such environments. In contrast to the existing datasets, the focus of this paper is not only to capture both temporal and spatial data diversities but also to present the impact of harsh conditions on captured data. Therefore, to validate the dataset, a preliminary comparison of odometry from onboard LiDARs is presented.

翻译：在无全球导航卫星系统(GNSS)覆盖环境下进行自主导航的算法主要依赖车载感知系统。这类系统通常集成相机与激光雷达(LiDAR)等传感器，但其性能可能受气溶胶粒子影响而下降。因此，需将这些传感器的采集数据与可穿透此类粒子的无线电探测与测距(RADAR)数据进行融合。这将有效提升在该类环境条件下定位与避碰算法的性能。本文提出一种面向含气溶胶粒子恶劣非结构化地下环境的多模态数据集。为完整评估采集数据，本文详细描述了车载传感器配置及数据采集环境。此外，数据集包含所有车载传感器以机器人操作系统(ROS)格式记录的同步原始测量数据，便于在此类环境中评估导航与定位算法。与现有数据集相比，本文重点不仅在于捕获时空数据多样性，更着重呈现恶劣条件对采集数据的影响。为验证数据集有效性，本文初步对比了基于车载LiDAR的里程计计算结果。