  With the complexity of lunar exploration missions, the moon needs to have a
higher level of autonomy. Environmental perception and navigation algorithms
are the foundation for lunar rovers to achieve autonomous exploration. The
development and verification of algorithms require highly reliable data
support. Most of the existing lunar datasets are targeted at a single task,
lacking diverse scenes and high-precision ground truth labels. To address this
issue, we propose a multi-task, multi-scene, and multi-label lunar benchmark
dataset LuSNAR. This dataset can be used for comprehensive evaluation of
autonomous perception and navigation systems, including high-resolution stereo
image pairs, panoramic semantic labels, dense depth maps, LiDAR point clouds,
and the position of rover. In order to provide richer scene data, we built 9
lunar simulation scenes based on Unreal Engine. Each scene is divided according
to topographic relief and the density of objects. To verify the usability of
the dataset, we evaluated and analyzed the algorithms of semantic segmentation,
3D reconstruction, and autonomous navigation. The experiment results prove that
the dataset proposed in this paper can be used for ground verification of tasks
such as autonomous environment perception and navigation, and provides a lunar
benchmark dataset for testing the accessibility of algorithm metrics. We make
LuSNAR publicly available at: https://github.com/autumn999999/LuSNAR-dataset.
