The development of deep learning has rapidly updated image-based localization techniques. This paper presents a review and comparison of the current state-of-the-art methods for image-based localization using deep learning in the indoor environment. Traditional Global Structure from Motion (SfM) pipeline and learning-based pipeline from the recent techniques have been analyzed. Based on the pipeline, the methods are categorized into three groups: learned features and matching, learned relative pose estimation, and learned absolute pose estimation. Since multiple sensors are used in many applications, sensor fusion techniques including image information, have been briefly reviewed in this paper as well. Furthermore, the paper discusses challenges in these methods and concludes learned features and matching is the more competitive method for indoor localization.