Visual Place Recognition based on Multi-level CNN Features

In this paper, we propose a visual place recognition (VPR) detection method which utilizes multi-level CNN features. High-level CNN features contain much semantic information and can deal with the change of viewpoint, middle-level CNN features contain much geometric information and have good robustness to the change of light and so on. Fully integrating the advantages of high-level and middle-level CNN features, the place recognition detection method will own good robustness to challenge the environment with appearance and viewpoint changes. Due to the high dimension of CNN feature vectors, we pre-process the feature vectors before they are used to the detection. And we introduce how to choose the image representation and compute the similarity score in detail. Finally we perform the experiments on three open datasets with viewpoint and appearance changes, which indicate that the performance of multi-level CNN features outperforms any other single-level CNN features and Fab-Map2.0.