Localizing Discriminative Visual Landmarks for Place Recognition
This work addresses the problem of robust place recognition for applications like robotics and autonomous navigation, but it appears incremental as it builds on existing CNN-based methods with a focus on landmark localization.
The paper tackled visual place recognition under perceptual changes by localizing discriminative visual landmarks like buildings and vegetation to improve image representations, achieving superior performance against state-of-the-art methods on open-source datasets.
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.