Abstract
So far, planetary surface exploration depends on various mobile robot platforms. The autonomous navigation and decision-making of these mobile robots in complex terrains largely rely on their terrain-aware perception, localization and mapping capabilities. In this paper we release the TAIL-Plus dataset, a new challenging dataset in deformable granular environments for planetary exploration robots, which is an extension to our previous work, TAIL (Terrain-Aware multI-modaL) dataset. We conducted field experiments on beaches that are considered as planetary surface analog environments for diverse sandy terrains. In TAIL-Plus dataset, we provide more sequences with multiple loops and expand the scene from day to night. Benefit from our sensor suite with modular design, we use both wheeled and quadruped robots for data collection. The sensors include a 3D LiDAR, three downward RGB-D cameras, a pair of global-shutter color cameras that can be used as a forward-looking stereo camera, an RTK-GPS device and an extra IMU. Our datasets are intended to help researchers developing multi-sensor simultaneous localization and mapping (SLAM) algorithms for robots in unstructured, deformable granular terrains. Our datasets and supplementary materials will be available at \url{this https URL}.
Abstract (translated)
到目前为止，行星表面探索主要依赖于各种移动机器人平台。这些移动机器人在复杂的地形环境中进行自主导航和决策，很大程度上取决于它们的地面感知、定位和映射能力。在本文中，我们发布了TAIL-Plus数据集，一种用于行星探索机器人的新型挑战性数据集，它是TAIL (地形感知多模态)数据集的扩展。我们在考虑作为行星表面模拟环境的沙滩上进行了现场实验。在TAIL-Plus数据集中，我们提供了更多具有多个循环的序列，并从白天到黑夜扩大了场景。得益于我们传感器套件的模块化设计，我们使用轮式和四足机器人进行数据采集。传感器包括一个3D激光雷达、三个向下俯视的RGB-D相机、一对可以作为前向立体相机的全球照相机、一个实时全球定位系统（RTK-GPS）设备和一只额外的IMU。我们的数据集旨在帮助研究人员开发用于无结构、变形颗粒地形中的机器人的多传感器同时定位和映射（SLAM）算法。我们的数据集和补充材料将 available at this <https:// this URL >。
URL
https://arxiv.org/abs/2404.13600
