To address the challenges of poor robustness and a high rate of mismatches of the ORB algorithm in complex lighting environments, we propose an adaptive ORB feature matching algorithm. The algorithm enhances the selection probability of feature points using an adaptive threshold approach that filters pixels iteratively. In addition, a quadtree uniform distribution of feature points is applied to increase the number of feature points in low-light or high-exposure regions. The MAGSAC++ algorithm is subsequently employed to eliminate erroneous matches, and a novel stopping criterion is introduced to reduce data resampling, which improves both the efficiency and accuracy of feature matching. Experimental results demonstrate that for datasets with significant lighting variations, the proposed algorithm substantially increases the number of extracted features, reduces feature matching time by 61.9% and enhances the matching rate by 35 percentage points compared to traditional ORB. It can efficiently complete feature matching under complex scene variations. Finally, localization experiments were conducted by the proposed algorithm. The results reveal that the proposed algorithm achieves higher localization accuracy compared to other advanced visual SLAM (simultaneous localization and mapping) algorithms and it has certain application value.