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 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 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：在没有全球导航卫星系统（GNSS）覆盖的环境中进行自主导航的算法主要依赖于机载感知系统。这些系统通常包含摄像头和激光雷达等传感器，但其性能可能在存在气溶胶颗粒时降低。因此，需要将这些传感器获取的数据与能够穿透此类颗粒的雷达数据融合。总体而言，这将提升此类环境条件下定位与避障算法的性能。本文介绍了一种来自存在气溶胶颗粒的恶劣非结构化地下环境的多模态数据集。文中详细描述了机载传感器及数据采集环境，以支持对采集数据的全面评估。此外，该数据集以机器人操作系统（ROS）格式提供了所有机载传感器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，本文不仅关注捕捉时空数据多样性，还着重呈现恶劣条件对采集数据的影响。因此，为验证该数据集，本文对机载激光雷达的里程计进行了初步比较分析。