月面场景获取的光学影像存在着显著的弱纹理、低反差、局部阴影干扰等问题，严重影响巡视器立体影像匹配精度。本文提出了一种置信度引导的单双目协同月面巡视影像立体匹配方法，该方法以共享几何语义编码为基础，将单目结构先验与立体匹配过程统一到同一表征空间，并通过置信度门控调制实现跨模态信息的自适应交互。在此基础上，进一步结合视差假设驱动的立体推理与由粗到细的细化机制，在保证几何估计精度的同时有效降低特征冗余和跨模态交互开销。方法在LuSNAR仿真月表数据集上完成模型训练与定量评估，并在嫦娥四号巡视器（CE-4）全景双目相机影像上进行直接推理验证。结果表明，所提方法在LuSNAR验证集上的平均端点误差（EPE）和D1误差分别为0.43和1.42%，优于国内外对比方法，且在CE-4影像上的真实数据实验中也表现出良好的场景适应能力与跨域泛化性能。此外，效率分析表明，与基线默认配置相比，本文方法还进一步将模型参数量由346.39M降至36.69M，FLOPs由42.51G降至11.45G，前向推理时间由4.163s缩短至0.153s。上述结果表明，所提方法在保证匹配精度的同时显著提升了计算效率，具有在轨应用的潜力。

Optical images acquired in lunar surface scenarios suffer from significant weak texture, low contrast, and local shadow interference, which severely affect the stereo image matching accuracy of lunar rovers. This paper proposes a confidence-guided monocular-stereo collaborative stereo matching method for lunar rover images. Built on shared geometric-semantic encoding, the method unifies monocular structural priors and stereo matching in the same representation space, and enables adaptive cross-modal interaction through confidence-gated modulation. On this basis, it further combines disparity-hypothesis-driven stereo reasoning with a coarse-to-fine refinement mechanism, which effectively reduces feature redundancy and cross-modal interaction cost while preserving the accuracy of geometric estimation. The method is trained and quantitatively evaluated on the LuSNAR simulated lunar surface dataset, and is directly tested on images captured by the Chang'e-4 rover CE-4 panoramic binocular camera. The results show that the proposed method achieves an average endpoint error EPE of 0.43 and a D1 error of 1.42% on the LuSNAR validation set, outperforming domestic and international comparison methods. It also shows strong scene adaptability and cross-domain generalization in real-data experiments on CE-4 images. In addition, the efficiency analysis shows that, compared with the default baseline configuration, the proposed method reduces the number of model parameters from 346.39M to 36.69M, FLOPs from 42.51G to 11.45G, and forward inference time from 4.163s to 0.153s. These results indicate that the proposed method significantly improves computational efficiency while maintaining matching accuracy, showing its potential for in-orbit applications.