Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Exploration

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.无全球导航卫星系统（Global Navigation Satellite System, GNSS）覆盖环境下的自主导航算法，主要依赖车载感知系统。此类系统通常集成相机、激光雷达（LiDAR）等传感器，而气溶胶颗粒会降低这类传感器的性能表现。因此，亟需将此类传感器获取的数据与可穿透此类颗粒的雷达（RADAR）数据进行融合，以此提升该环境下定位与避障算法的整体性能。 本文构建了一套面向含气溶胶颗粒的严苛非结构化地下环境的多模态数据集。文中详细阐述了数据集采集所用的车载传感器与采集环境，以便对获取的数据开展全面评估。此外，该数据集包含机器人操作系统（Robot Operating System, ROS）格式的所有车载传感器同步原始测量数据，可助力此类环境下导航与定位算法的评估工作。 与现有数据集相比，本文的研究重点不仅在于捕获时空数据的多样性，还旨在呈现严苛环境对采集数据的影响。为此，本文通过车载激光雷达里程计的初步对比实验对该数据集进行了验证。