This study addresses the challenges of stereo matching algorithms in planetary surface environments, characterized by unstable lighting conditions, high noise interference, and limited ground scene diversity. Traditional algorithms often perform poorly in regions with weak texture and parallax discontinuity. To overcome these limitations, this study introduces a noise-resistant stereo matching algorithm featuring an improved adaptive weighting AD-Census transform. The algorithm implements a Gaussian-weighted circular matching template within the Census transform to establish distance relationships between neighborhood pixels and the center pixel, fusing this with Canny gradient information to generate an improved Census cost. The algorithm combines this improved Census transform with AD transform, utilizing adaptive synthesis weights. Additionally, the crosshatch domain adaptive window is improved by incorporating elevated threshold conditions for pixel grayscale differences, enabling better distinction between weak texture regions and edge regions. Experimental results demonstrate that the improved AD-Census algorithm achieves an 8.7% average mismatching rate on the Middlebury test image set, reduces average processing time by 30%, and exhibits improved robustness to lighting variations and noise. The algorithm's effectiveness is further validated through simulation analysis in a simulated planetary surface environment.