The rapid development of Uncrewed Aerial Vehicles (UAVs) and their unique vantage points present both new opportunities and challenges for person Re-Identification (ReID). Uncertain rotations and scale variations of targets in UAV images, coupled with complex environmental factors, hinder existing methods from extracting robust feature representations. Some methods either make minor modifications to the traditional model architecture or apply simple image rotations but still fail to effectively address the challenges of UAV person ReID. To overcome these limitations, we propose a novel Data Augmentation and Rotation Invariance (DARI) algorithm. First, rotation-invariant convolution is introduced to adaptively extract features, mitigating the uncertainty caused by target rotation. Second, a refined data augmentation correction strategy is employed to reduce noise interference by increasing the richness of global features at different stages. Additionally, considering that multiple features of the same identity should yield consistent recognition result, invariant constraints are designed to enhance the clustering effect. We conducted extensive experiments on both UAV and fixed-camera datasets. The results on PRAI-1581 demonstrate a 5.6% and 6.1% improvement in mAP and Rank-1, respectively, compared to baseline. These findings highlight the model’s effectiveness in addressing the challenges of UAV ReID, demonstrating its robustness and superiority.