Terrain-aware perception holds the potential to improve the robustness and accuracy of autonomous robot navigation in the wilds, thereby facilitating effective off-road traversals. However, the lack of multi-modal perception across various motion patterns hinders the solutions of Simultaneous Localization And Mapping (SLAM), especially when confronting non-geometric hazards in demanding landscapes. In this paper, we first propose a Terrain-Aware multI-modaL (TAIL) dataset tailored to deformable and sandy terrains. It incorporates various types of robotic proprioception and distinct ground interactions for the unique challenges and benchmark of multi-sensor fusion SLAM. The versatile sensor suite comprises stereo frame cameras, multiple ground-pointing RGB-D cameras, a rotating 3D LiDAR, an IMU, and an RTK device. This ensemble is hardware-synchronized, well-calibrated, and self-contained. Utilizing both wheeled and quadrupedal locomotion, we efficiently collect comprehensive sequences to capture rich unstructured scenarios. It spans the spectrum of scope, terrain interactions, scene changes, ground-level properties, and dynamic robot characteristics. We benchmark several state-of-the-art SLAM methods against ground truth and provide performance validations. Corresponding challenges and limitations are also reported. All associated resources are accessible upon request at \url{https://tailrobot.github.io/}.

翻译：地形感知能够提升自主机器人在野外导航的鲁棒性与准确性，从而促进有效的越野通行。然而，由于缺乏跨多种运动模式的多模态感知，特别是在复杂地形中面临非几何危险时，同步定位与地图构建（SLAM）的解决方案受到制约。本文首次提出面向可变形及沙质地形的感知地形多模态（TAIL）数据集。该数据集融合了多种机器人本体感知与不同的地面交互特性，为多传感器融合SLAM的独特挑战与基准测试提供了支持。其多用途传感器套件包括立体帧相机、多台地面指向RGB-D相机、旋转式三维激光雷达、惯性测量单元及RTK设备。该套件实现了硬件同步、精确标定且自包含。利用轮式与四足运动模式，我们高效采集了涵盖丰富非结构化场景的完整序列，覆盖了范围、地形交互、场景变化、地面属性及动态机器人特性等维度。我们基于真值对多种主流SLAM方法进行了基准测试，并提供了性能验证，同时报告了相应的挑战与局限性。所有相关资源可通过\url{https://tailrobot.github.io/}申请获取。