Automatic Vocabulary and Graph Verification for Accurate Loop Closure Detection

TL;DR

This paper introduces an automatic vocabulary construction method and a graph verification technique to enhance the accuracy of loop closure detection in SLAM, addressing issues of scale determination and perceptual aliasing.

Contribution

It proposes a natural convergence criterion for automatic vocabulary building and a topological graph verification method to improve loop closure detection accuracy.

Findings

Automatic vocabulary construction improves feature association.

Graph verification reduces false positives in loop detection.

Enhanced accuracy demonstrated on public datasets.

Abstract

Localizing pre-visited places during long-term simultaneous localization and mapping, i.e. loop closure detection (LCD), is a crucial technique to correct accumulated inconsistencies. As one of the most effective and efficient solutions, Bag-of-Words (BoW) builds a visual vocabulary to associate features and then detect loops. Most existing approaches that build vocabularies off-line determine scales of the vocabulary by trial-and-error, which often results in unreasonable feature association. Moreover, the accuracy of the algorithm usually declines due to perceptual aliasing, as the BoW-based method ignores the positions of visual features. To overcome these disadvantages, we propose a natural convergence criterion based on the comparison between the radii of nodes and the drifts of feature descriptors, which is then utilized to build the optimal vocabulary automatically. Furthermore, we present a novel topological graph verification method for validating candidate loops so that geometrical positions of the words can be involved with a negligible increase in complexity, which can significantly improve the accuracy of LCD. Experiments on various public datasets and comparisons against several state-of-the-art algorithms verify the performance of our proposed approach.