Construction and Verification of SpaceMat-LiDAR Dataset for Spacecraft Material Classification in On-Orbit Servicing 面向在轨服务航天器材料分类的 SpaceMat-LiDAR 数据集构建与验证
- 针对航天材料识别难、LiDAR数据匮乏问题，在模拟太空环境下采集并建立了SpaceMat-LiDAR真实数据集。该数据集涵盖MLI、CFRP等六类典型航天材料，系统覆盖了不同扫描距离与入射角，填补了领域空白，并确立了规范的采集标注流程。
- 针对航天材料几何同质与强反射挑战，构建融合几何与强度的深度学习模型，引入物理先验分支利用距离/角度解耦强度偏移，有效识别 MLI 与 CFRP 等材料。实验验证其性能显著优于单一特征基线，为「天工计划」在轨作业提供了可靠感知支撑。
- 数据集价值需通过基线实验量化验证。实现并对比多种材料分类基线，评估强度辅助与几何先验对消解材料歧义的贡献，形成公开可比的 benchmark 口径；SpaceMat-LiDAR 填补航天材料真实 LiDAR 资源空白，为自主在轨作业中的材料感知系统提供实证基础。
摘要
As on-orbit servicing (OOS) missions transition toward greater autonomy, the precise identification of target materials has become a critical prerequisite for complex operations, including non-cooperative pose estimation, autonomous grasping, and obstacle avoidance. However, spacecraft components frequently exhibit “geometric homogeneity,” where distinct structural elements share identical or similar geometries, rendering shape-based recognition insufficient. Furthermore, the complex optical properties of space coatings—characterized by high specularity and unique backscattering profiles—pose significant challenges for traditional passive sensors. Despite these requirements, the field lacks real-world LiDAR datasets dedicated to space-grade materials.
In this paper, we present SpaceMat-LiDAR, a pioneering real-world aerospace material dataset captured using a solid-state LiDAR in a controlled ground-based darkroom to simulate space illumination conditions. The dataset encompasses six representative categories: Multi-Layer Insulation (MLI), Carbon Fiber Reinforced Polymer (CFRP), matte black paint, solar panels, bare aluminum, and white-painted aluminum. To capture the multi-dimensional scattering characteristics of these materials, data were systematically collected across a diverse range of scanning distances and incidence angles.
Our methodology focuses on the fusion of 3D spatial coordinates and reflection intensity features. Furthermore, we investigate the effects of incorporating geometric priors—specifically distance and incidence angles—as auxiliary inputs to the classification models. Several baseline algorithms are evaluated to benchmark the contribution of intensity-aided and geometric features in resolving material ambiguity. The SpaceMat-LiDAR dataset fills a critical gap in existing data resources and provides an empirical foundation for developing material-aware perception systems for future autonomous on-orbit operations.