© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.Place recognition is an important topic in many vision-based applications. For this purpose, many traditional and artificial intelligence-based methods have been proposed. Despite years of expertise accrued in this area, it still remains a challenging problem due to the various ways in which the appearance of places in the real world can take place. Recently, rapid improvements have been made in image processing and recognition using deep neural networks. In particular, convolutional neural frameworks (CNN) are widely used in object detection, image classification, as well as in place recognition. The advantage of CNN-based place recognition is that CNN methods can automatically learn image patterns using sample images without any pre-processing and can handle appearance variations better than traditional methods. In this paper, we review and discuss traditional and deep learning-based place recognition algorithms, as well as existing datasets that can be used for performance measurement.