Physics-aware LiDAR degradation modeling and 3D reconstruction pipeline for space target on-orbit servicing 物理感知 LiDAR 退化建模与空间目标在轨服务三维重建流水线
- 低轨在轨服务中，MLI 多径、测距漂移、杂散光与平台微振动会使 LiDAR 点云出现分层、鬼影与边缘拖尾，但缺少可解释的物理退化基准。建立刻画多径、漂移、拖尾、虚警与指向误差的物理机理级退化模型，可在仿真中按需组合生成统一退化输入，支撑可重复对比实验。
- 在强退化点云上直接表面重建会导致离群点放大误差，需构建 LiDAR-only 可恢复流水线。串联体素下采样、SOR/ROR 离群剔除、IMU 运动补偿与多帧融合，优化预处理与重建链路，分层与鬼影显著缓解，点云质量满足后续表面重建与近距感知验证需求。
- 依托高保真平台，利用 Chamfer Distance (CD) 与 RMSE 等指标量化验证流水线效能。实验表明：在极端退化场景下，该流水线可将重建精度恢复至理想基线的 80% 以上，RMSE 达到厘米级（0.0672m），且完备性高达 99.37%。相关数字化结论已反哺至在轨服务仿真平台的退化参数配置，形成了从仿真建模到算法优化的闭环验证支撑。
摘要
Proliferating low Earth orbit (LEO) constellations and rising space debris risks necessitate high-precision perception and 3D reconstruction for on-orbit servicing (OOS). This paper investigates physics-aware LiDAR degradation modeling and reconstruction under constraints, including extreme illumination, specular materials, micro-vibrations, and thermal deformation. We first develop a degradation model characterizing MLI-induced multipath interference, ranging drift, beam-divergence edge trailing, stray light false alarms, and platform pointing drifts. This model establishes a physics-grounded benchmark for algorithm development and evaluation within high-fidelity simulations. Second, we optimize a LiDAR-only 3D reconstruction pipeline with voxel downsampling and tandem Statistical and Radius Outlier Removal (SOR+ROR). Combined with IMU-aided motion compensation, multi-frame fusion effectively mitigates point cloud layering and ghosting. High-fidelity platform evaluations with Chamfer distance, RMSE, and completeness show robust performance under extreme lighting and complex motion. Refinement restores accuracy to over 80% of the ideal baseline, achieving centimeter-level RMSE. These results inform sensor selection and pipeline design for OOS-related simulation studies.