  We address the problem of visual place recognition with perceptual changes.
The fundamental problem of visual place recognition is generating robust image
representations which are not only insensitive to environmental changes but
also distinguishable to different places. Taking advantage of the feature
extraction ability of Convolutional Neural Networks (CNNs), we further
investigate how to localize discriminative visual landmarks that positively
contribute to the similarity measurement, such as buildings and vegetations. In
particular, a Landmark Localization Network (LLN) is designed to indicate which
regions of an image are used for discrimination. Detailed experiments are
conducted on open source datasets with varied appearance and viewpoint changes.
The proposed approach achieves superior performance against state-of-the-art
methods.
