Exploring high-latitude lunar regions presents an extremely challenging visual environment for robots. The low sunlight elevation angle and minimal light scattering result in a visual field dominated by a high dynamic range featuring long, dynamic shadows. Reproducing these conditions on Earth requires sophisticated simulators and specialized facilities. We introduce a unique dataset recorded at the LunaLab from the SnT - University of Luxembourg, an indoor test facility designed to replicate the optical characteristics of multiple lunar latitudes. Our dataset includes images, inertial measurements, and wheel odometry data from robots navigating seven distinct trajectories under multiple illumination scenarios, simulating high-latitude lunar conditions from dawn to nighttime with and without the aid of headlights, resulting in 88 distinct sequences containing a total of 1.3M images. Data was captured using a stereo RGB-inertial sensor, a monocular monochrome camera, and for the first time, a novel single-photon avalanche diode (SPAD) camera. We recorded both static and dynamic image sequences, with robots navigating at slow (5 cm/s) and fast (50 cm/s) speeds. All data is calibrated, synchronized, and timestamped, providing a valuable resource for validating perception tasks from vision-based autonomous navigation to scientific imaging for future lunar missions targeting high-latitude regions or those intended for robots operating across perceptually degraded environments. The dataset and all supplementary material can be accessed from and found at https://github.com/spaceuma/spice-hl3.

翻译：探索高纬度月球区域对机器人而言构成了一个极具挑战性的视觉环境。低太阳高度角与微弱的光线散射导致视野呈现出以长动态阴影为特征的高动态范围。在地球上复现这些条件需要复杂的模拟器和专用设施。我们介绍一个在卢森堡大学SnT的LunaLab室内测试设施中采集的独特数据集，该设施旨在模拟多种月球纬度的光学特性。我们的数据集包含机器人在多种光照场景下沿七条不同轨迹行进时采集的图像、惯性测量数据和轮式里程计数据，这些场景模拟了从黎明到夜间的高纬度月球条件，包括启用和未启用车头灯的情况，共产生88个独立序列，总计130万张图像。数据采集使用了立体RGB-惯性传感器、单目黑白相机，并首次使用了一种新型单光子雪崩二极管（SPAD）相机。我们记录了静态和动态图像序列，机器人分别以慢速（5厘米/秒）和快速（50厘米/秒）速度行进。所有数据均经过校准、同步并带有时间戳，为验证从基于视觉的自主导航到未来针对高纬度区域或旨在在感知退化环境中运行的机器人的月球任务科学成像等感知任务提供了宝贵资源。数据集及所有补充材料可通过 https://github.com/spaceuma/spice-hl3 访问获取。