HBRB-BoW: A Retrained Bag-of-Words Vocabulary for ORB-SLAM via Hierarchical BRB-KMeans

TL;DR

This paper introduces HBRB-BoW, a hierarchical vocabulary training method for ORB-SLAM that preserves descriptor fidelity and improves environment representation, leading to better localization and mapping performance.

Contribution

It proposes a novel hierarchical binary-to-real-and-back (HBRB)-BoW algorithm that enhances the discriminative power of visual vocabularies in SLAM systems.

Findings

HBRB-BoW produces more discriminative vocabularies than traditional methods.

The approach improves loop closing and relocalization accuracy.

Experimental results confirm enhanced environment representation.

Abstract

In visual simultaneous localization and mapping (SLAM), the quality of the visual vocabulary is fundamental to the system's ability to represent environments and recognize locations. While ORB-SLAM is a widely used framework, its binary vocabulary, trained through the k-majority-based bag-of-words (BoW) approach, suffers from inherent precision loss. The inability of conventional binary clustering to represent subtle feature distributions leads to the degradation of visual words, a problem that is compounded as errors accumulate and propagate through the hierarchical tree structure. To address these structural deficiencies, this paper proposes hierarchical binary-to-real-and-back (HBRB)-BoW, a refined hierarchical binary vocabulary training algorithm. By integrating a global real-valued flow within the hierarchical clustering process, our method preserves high-fidelity descriptor information until the final binarization at the leaf nodes. Experimental results demonstrate that the proposed approach yields a more discriminative and well-structured vocabulary than traditional methods, significantly enhancing the representational integrity of the visual dictionary in complex environments. Furthermore, replacing the default ORB-SLAM vocabulary file with our HBRB-BoW file is expected to improve performance in loop closing and relocalization tasks.