Efficient real-time loop closure detection using GMM and tree structure

Abstract

In this work, fast and efficient appearance-based methods for visual loop-closure detection are proposed. The widely used technique based on the Bag-of-Words image representation has shown some limitations especially with aliasing problem. In this work, however, an appearance-based approach for loop closure detection using local invariant and colour features is proposed. The first technique uses Bayes Decision Theory for loop closure detection based on Gaussian Mixture Model (GMM). A new technique based on the combination of GMM with the KD-Tree data structure is presented as well. The techniques have been validated using monocular image sequences from several environments.