Visual loop closure is important in pose tracking and relocalization in many robotics and Argument Reality (AR) systems. For large and highly repetitive environments, sparse keypoint-based methods face several challenges, especially the discriminability of descriptors. In this paper, we propose an augmented descriptor by combining ORB feature and the context descriptor to increase its discriminability and matching performance. An end-to-end network is adopted to perform simultaneous feature learning and code hashing for the context. In addition, feature position clustering is used to reduce the number of contexts. Besides, hash mapping is adopted to reduce the dimensionality of ORB features. Finally, the context descriptors and ORB features with dimensionality reduction are stacked. Experimental results on the NewCollege and TUM datasets demonstrate that our algorithm achieves higher precision/recall and faster speed than the original algorithm proposed by Antonio et al. [1].