Visual place recognition based on multi-level feature difference map

Perceptual aliasing and perceptual variability caused by drastically appearance changing in the scene bring great challenge to visual place recognition. Many existing visual place recognition methods using CNN directly adopted the distance of the CNN features and set thresholds to measure the similarity between the two images, which had shown a poor performance when drastically appearance changing in the scene. A novel multi-level feature difference map based visual place recognition method was proposed. Firstly, a CNN pretrained on scene-centric dataset was adopted to extract features for perceptually different images of same place and aliased images of different places. Then, according to the different properties of different CNN layers, multi-level feature difference map was constructed on the multi-level CNN features to represent the difference between the two images. Finally, visual place recognition was regarded as a binary classification task. The feature difference maps were used to train a new CNN classification model for determining whether the two images are from the same place. Experimental results demonstrated that the feature difference map constructed by multi-level CNN features can well represent the difference between two images, and the proposed method can effectively overcome perceptual aliasing and perceptual variability, and achieve a better recognition performance when drastically appearance changing in the scene.