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.

通过构建多任务并具备多场景、多标签的月球基准数据集 LuSNAR，我们可以全面评估自主感知和导航系统，包括高分辨率的立体图像对、全景语义标签、密集深度地图、LiDAR点云和漫游者的位置等，验证了该数据集在自主环境感知和导航等任务的可行性，并提供了用于测试算法指标的月球基准数据集。

LuSNAR: 一个基于多传感器的月球分割、导航和重建数据集用于自主探测