Non-line-of-sight (NLOS) imaging, which utilizes weak photons that diffusely reflect from the visible surfaces (e.g., diffuse walls), can reconstruct hidden objects around the corner. Recently, lots of non-line-of-sight imaging methods have been proposed, such as time-of-flight (ToF)-based methods, coherence-based methods, and intensity-based methods. However, most of these methods are time-consuming for data acquisition and have poor robustness in the reconstruction process. In this paper, the novel application of Generative Adversarial Network is introduced to NLOS imaging. A robust, real-time NLOS imaging method based on autocorrelation mapping Generative Adversarial Network (AMGAN) is proposed, which reconstructs hidden scenes by learning the autocorrelation mapping from speckle-autocorrelation to the hidden target. In order to train the proposed AMGAN, we also analyze the principles of speckle-autocorrelation NLOS imaging and the noise model of the imaging process. Then a speckle-autocorrelation NLOS imaging dataset SANLOS is synthesized in this paper. Finally, our method is compared with other methods based on deep learning quantitatively and qualitatively. The experimental results demonstrate that the proposed approach achieves better NLOS reconstruction quality and is more robust under different exposure times compared with state-of-art methods. © 2022 SPIE.