Loop Closure Detection Via Maximization of Mutual Information

An image can be described in terms of appearance frequency of visual words. This representation is implemented in bag-of-visual-words (BoVW)-based loop closure detection for its efficiency and effectiveness. However, traditional BoVW-based approaches are strongly affected by false positive loops due to scene ambiguity caused by redundant words in the vocabulary and fail to detect bidirectional loops in monocular mode. Aiming at overcoming these problems, we propose a novel vocabulary construction algorithm named hierarchical sequential information bottleneck (HsIB) by leveraging the maximization of mutual information (MMI) mechanism. First, feature descriptors are extracted from training images for visual vocabulary construction. Second, HsIB extracts discriminative yet informative visual words through the MMI mechanism in vocabulary construction, which treats feature descriptors clustering as a process of data compression. Finally, the clustering process reaches a tradeoff between compactness and discrimination and improves the performance of traditional BoVW-based loop closure detection. The proposed method is compared with state-of-the-art methods on publicly available datasets. We also create a challenging dataset to further evaluate the performance of HsIB on bidirectional loops. To the best of our knowledge, we are the first to implement information bottleneck (IB) method in visual-SLAM (vSLAM) loop closure detection, and we obtain impressive results.