In this paper, we address the problem of non-robustness in empirical risk minimization-based learning schemes for indoor localization using radio frequency (RF) fingerprints. To overcome this challenge, we propose a novel solution that leverages the manifold structure of the channel state information (CSI) data, exploiting its geographical similarity. Our approach involves a simple yet effective data augmentation scheme named Geomixup, which utilizes geographic information to synthesize data for training. By incorporating geolocation-based data aug-mentation, we enable robust localization based on CSI, ensuring that the model's decisions are grounded in the highly correlated geolocation feature, rather than weakly correlated ones. To evaluate the performance of the proposed method, we utilized two performance measures, pseudo accuracy and distance error, on the data points that were not encountered during training. They consider the confidence assigned to each location prediction and apply a larger penalty in cases where the model exhibits high confidence in misplacement. Through extensive RF - based localization tests, we demonstrate that our proposed scheme outperforms other learning methods in terms of robustness, all while maintaining a negligible loss of accuracy. This significant improvement in robustness ensures the reliability and stability of our approach, making it highly suitable for real-world indoor localization applieations.