iBoW-LCD: An Appearance-based Loop Closure Detection Approach using Incremental Bags of Binary Words

TL;DR

iBoW-LCD is a new appearance-based loop closure detection method that uses incremental binary Bag-of-Words and dynamic islands to efficiently and accurately identify previously seen locations without requiring vocabulary training.

Contribution

It introduces an incremental BoW scheme with binary descriptors and a dynamic islands mechanism, eliminating the need for vocabulary training and reducing computational costs.

Findings

High accuracy in indoor and outdoor datasets

Outperforms state-of-the-art solutions

Efficient detection with reduced computational time

Abstract

In this paper, we introduce iBoW-LCD, a novel appearance-based loop closure detection method. The presented approach makes use of an incremental Bag-of-Words (BoW) scheme based on binary descriptors to retrieve previously seen similar images, avoiding any vocabulary training stage usually required by classic BoW models. In addition, to detect loop closures, iBoW-LCD builds on the concept of dynamic islands, a simple but effective mechanism to group similar images close in time, which reduces the computational times typically associated to Bayesian frameworks. Our approach is validated using several indoor and outdoor public datasets, taken under different environmental conditions, achieving a high accuracy and outperforming other state-of-the-art solutions.