LunaPolaris: A stereo camera, point cloud and IMU dataset for future lunar exploration in polar regions

Space missions shift their focus to the lunar surface, for which they require autonomous robots that work in extreme lighting conditions. Most existing datasets used for developing advanced Simultaneous Localisation And Mapping (SLAM) algorithms focus on urban environments like office buildings or cities. Only a limited number of datasets show deserted landscapes without vegetation, and they are usually captured during daylight. Simulations are frequently used in the absence of real or analogue data, but they have fidelity limitations compared to complex planetary environments. The LunaPolaris dataset addresses this issue by providing twelve sequences recorded in a lunar analogue facility using a small rover with a stereo camera, a solid-state LiDAR, and an IMU. These datasets can be used for validating the robustness of visual SLAM or Light Detecting And Ranging (LiDAR)-SLAM in challenging, lunar-like environments, highlighting the challenges around the extreme lighting conditions on the Moon. In this paper, we assess current gaps in existing datasets, present an overview of LunaPolaris, elaborate on the environment and technical details of how the data collection was conducted, and provide initial validation results using stereo and LiDAR SLAM.