A Bag of Relevant Regions for Visual Place Recognition in Challenging Environments

In this paper, we present a method for vision-based place recognition in environments with a high content of similar features and that are prone to variations in illumination. The high similarity of features makes difficult the disambiguation between two different places. The novelty of our method relies on using the Bag of Words (BoW) approach to derive an image descriptor from a set of relevant regions, which are extracted using a visual attention algorithm. We name our approach Bag of Relevant Regions (BoRR). The descriptor of each relevant region is built by using a 2D histogram of the chromatic channels of the CIE-Lab color space. We have compared our results with those using state of the art descriptors that include the BoW and demonstrate that our approach performs better in most of the cases.