A Robust Image-Sequence-Based Framework for Visual Place Recognition in Changing Environments.

Abstract

This article proposes a robust image-sequence-based framework to deal with two challenges of visual place recognition in changing environments: 1) viewpoint variations and 2) environmental condition variations. Our framework includes two main parts. The first part is to calculate the distance between two images from a reference image sequence and a query image sequence. In this part, we remove the deep features of nonoverlap contents in these two images and utilize the remaining deep features to calculate the distance. As the deep features of nonoverlap contents are caused by viewpoint variations, removing these deep features can improve the robustness to viewpoint variations. Based on the first part, in the second part, we first calculate the distances of all pairs of images from a reference image sequence and a query image sequence, and obtain a distance matrix. Afterward, we design two convolutional operators to retrieve the distance submatrix with the minimum diagonal distribution. The minimum diagonal distribution contains more environmental information, which is insensitive to environmental condition variations. The experimental results suggest that our framework exhibits better performance than several state-of-the-art methods. Moreover, the analysis of runtime shows that our framework has the potential to satisfy real-time demands.