Appearance-based Loop Closure Detection via Bidirectional Manifold Representation Consensus

Abstract

Loop closure detection (LCD), which aims to deal with the drift emerging when robots travel around the route, plays a key role in a simultaneous localization and mapping system. Unlike most current methods which focus on seeking an appropriate representation of images, we propose a novel two-stage pipeline dominated by the estimation of spatial geometric relationship. When a query image occurs, we select candidates on-line according to the similarity of global semantic features in the first stage, and then conduct robust geometric confirmation to verify true loop-closing pairs in the second stage. To this end, a robust feature matching algorithm, termed as bidirectional manifold representation consensus (BMRC), is proposed. In particular, we utilize manifold representation to construct local neighborhood structures of feature points and formulate the matching problem into an optimization model, enabling linearithmic time complexity via a closed-form solution. Furthermore, we propose a dynamic place partition strategy based on BMRC to segment image streams with similar content into a place, which can mine more valid candidate frames, improving the recall rate of the whole system. Extensive experiments on several publicly available datasets reveal that BMRC has a good performance in the general feature matching task and the proposed pipeline outperforms the current state-of-the-art approaches in the LCD task.