HBRB-BoW: A Retrained Bag-of-Words Vocabulary for ORB-SLAM via Hierarchical BRB-KMeans
Minjae Lee, Sang-Min Choi, Gun-Woo Kim, Suwon Lee

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRobotics and Sensor-Based Localization · Multimodal Machine Learning Applications · Advanced Image and Video Retrieval Techniques
