Mining visual phrases for long-term visual SLAM

Abstract

We propose a discriminative and compact scene descriptor for single-view place recognition that facilitates long-term visual SLAM in familiar, semi-dynamic and partially changing environments. In contrast to popular bag-of-words scene descriptors, which rely on a library of vector quantized visual features, our proposed scene descriptor is based on a library of raw image data (such as an available visual experience, images shared by other colleague robots, and publicly available image data on the web) and directly mine it to find visual phrases (VPs) that discriminatively and compactly explain an input query / database image. Our mining approach is motivated by recent success in the field of common pattern discovery-specifically mining of common visual patterns among scenes-and requires only a single library of raw images that can be acquired at different time or day. Experimental results show that even though our scene descriptor is significantly more compact than conventional descriptors it has a relatively higher recognition performance.