Long-term Place Recognition through Worst-case Graph Matching to Integrate Landmark Appearances and Spatial Relationships

Abstract

Place recognition is an important component for simultaneously localization and mapping in a variety of robotics applications. Recently, several approaches using landmark information to represent a place showed promising performance to address long-term environment changes. However, previous approaches do not explicitly consider changes of the landmarks, i,e., old landmarks may disappear and new ones often appear over time. In addition, representations used in these approaches to represent landmarks are limited, based upon visual or spatial cues only. In this paper, we introduce a novel worst-case graph matching approach that integrates spatial relationships of landmarks with their appearances for long-term place recognition. Our method designs a graph representation to encode distance and angular spatial relationships as well as visual appearances of landmarks in order to represent a place. Then, we formulate place recognition as a graph matching problem under the worst-case scenario. Our approach matches places by computing the similarities of distance and angular spatial relationships of the landmarks that have the least similar appearances (i.e., worst-case). If the worst appearance similarity of landmarks is small, two places are identified to be not the same, even though their graph representations have high spatial relationship similarities. We evaluate our approach over two public benchmark datasets for long-term place recognition, including St. Lucia and CMU-VL. The experimental results have validated that our approach obtains the state-of-the-art place recognition performance, with a changing number of landmarks.