Research/ SPACE AI & AUTONOMY
SPICE-HL3: Single-Photon, Inertial, and Stereo Camera dataset for Exploration of High-Latitude Lunar Landscapes
David Rodríguez-Martínez, Dave van der Meer, Junlin Song, Abishek Bera, C.J. Pérez-del-Pulgar, Miguel Angel Olivares-Mendez
Perception & State EstimationOpenAlex topic: Robotics and Sensor-Based Localization · Simulation, Digital Twins & Benchmarkstext match
arXivcs.ROOpenAlexRobotics and Sensor-Based Localization · Advanced Vision and Imaging · Autonomous Vehicle Technology and Safety
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.
Comments: 12 pages, 7 figures, dataset
Journal ref: Scientific Data, 13, 374 (2026)