Construction and Verification of SpaceMat-LiDAR Dataset for Spacecraft Material Classification in On-Orbit Servicing
- To address difficult spacecraft material recognition and scarce LiDAR data, SpaceMat-LiDAR was collected and established under simulated space conditions. The dataset covers six typical aerospace materials including MLI and CFRP, with systematic coverage of scanning distance and incidence angle, filling a domain gap and establishing standardized acquisition and labeling procedures.
- For geometric homogeneity and strong reflections in aerospace materials, a deep learning model fusing geometry and intensity was built, with a physics-prior branch using distance and angle to decouple intensity bias for reliable MLI and CFRP recognition. Experiments show clear gains over single-feature baselines, providing dependable perception for SpaceCrafter on-orbit operations.
- Value was benchmarked with multiple material-classification baselines quantifying intensity and geometric priors against ambiguity, forming a publicly comparable benchmark protocol. SpaceMat-LiDAR closes a real-data gap for aerospace materials and provides an empirical foundation for material-aware perception in autonomous on-orbit operations.
Abstract
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.