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.

本文提出了一种利用卷积神经网络提取特征并用地标定位网络确定区分性地标的方法，用于视觉地点识别问题，该方法在各种外观和视角变化方面均具有优异的性能。