High-accuracy planetary mapping products are increasingly required for landing-site assessment, precision navigation, and future surface operations on the Moon and Mars. Although massive orbital remote sensing images are available, the geometric accuracy and spatial resolution of many existing mapping products is still insufficient for engineering applications. A major bottleneck is large-scale bundle adjustment, whose reliability is strongly affected by data quality, control network strength, as well as engineering experience. Compared with Earth observation photogrammetry, planetary mapping faces great challenges such as heterogeneous sensor models, complex illumination, sparse absolute control. This paper summarizes a practical quality control framework for large-scale bundle adjustment of planetary remote sensing images. The workflow is divided into four coupled stages: data preprocessing, control network construction, parameter setting, and accuracy evaluation. The framework is distilled from previous planetary mapping studies, open-source software platforms and our practical experience in processing tens of thousands of planetary images. Experiments using LRO NAC datasets demonstrate that satisfactory bundle adjustment results can be achieved when the proposed strategy is applied. The framework improves the overall efficiency, controllability, and reliability of large-scale planetary photogrammetric processing.