Unsupervised Learning Methods for Visual Place Recognition in Discretely and Continuously Changing Environments

Abstract

Visual place recognition in changing environments is the problem of finding matchings between two sets of observations, a query set and a reference set, despite severe appearance changes. Recently, image comparison using CNNbased descriptors showed very promising results. However, the experiments in the literature typically assume a single distinctive condition within each set (e.g., reference images are captured at daytime and the query sequence is at night). In this paper, we will demonstrate that as soon as the conditions change within one set (e.g., reference is daytime and now the query is a traversal daytime-dusk-night-dawn), different places under the same condition can suddenly look more similar than same places under different conditions. As a consequence, state-of-the-art approaches like CNN-based descriptors fail. This paper discusses this practically very important problem of in-sequence condition changes and defines a hierarchy of problem setups from (1) no in-sequence changes, (2) discrete in-sequence changes, to (3) continuous in-sequence changes. We will experimentally evaluate the effect of in-sequence condition changes on two state-of-the-art CNN-descriptors and investigate unsupervised methods to improve their performance. This includes an evaluation of the importance of statistical normalization (standardization) of descriptors, which is often omitted in existing approaches but can considerably improve results for problems up to discrete in-sequence changes. To address the practical most relevant setup of continuous changes, we investigate the application of unsupervised learning methods using two PCA-based approaches from the literature and propose a novel clustering-based extension of the statistical normalization. We experimentally demonstrate that these approaches can significantly improve place recognition performance in case of continuous in-sequence condition changes. Matlab implementations of the presented approaches are available online: www.tu-chemnitz.de/etit/proaut/cont_changing_envs.