Physics-aware LiDAR degradation modeling and 3D reconstruction pipeline for space target on-orbit servicing 物理感知 LiDAR 退化建模與太空目標在軌服務三維重建流水線
- 低軌在軌服務中，MLI 多徑、測距漂移、雜散光與平台微振動會使 LiDAR 點雲出現分層、鬼影與邊緣拖尾，但缺少可解釋的物理退化基準。建立刻畫多徑、漂移、拖尾、虛警與指向誤差之物理機理級退化模型，可於模擬中按需組合生成統一退化輸入，支撐可重複對比實驗。
- 於強退化點雲上直接表面重建會導致離群點放大誤差，需建構 LiDAR-only 可恢復流水線。串聯體素下採樣、SOR/ROR 離群剔除、IMU 運動補償與多幀融合，最佳化預處理與重建鏈路，分層與鬼影顯著緩解，點雲品質滿足後續表面重建與近距感知驗證需求。
- 依托高保真平台，利用 Chamfer Distance (CD) 與 RMSE 等指標量化驗證流水線效能。實驗表明：於極端退化場景下，該流水線可將重建精度恢復至理想基線之 80% 以上，RMSE 達到厘米級（0.0672m），且完備性高達 99.37%。相關數位化結論已反哺至在軌服務模擬平台之退化參數配置，形成了從模擬建模到演算法優化之閉環驗證支撐。
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.