# HBST: A Hamming Distance embedding Binary Search Tree for Visual Place Recognition

arXiv: https://arxiv.org/abs/1802.09261 · HTML: https://mlsanity.com/paper/1802.09261/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2018.

## Built on by (most cited first, 4 of 4)

- [AirObject: A Temporally Evolving Graph Embedding for Object Identification](https://mlsanity.com/paper/2111.15150/) (2021)
- [Revisiting Binary Local Image Description for Resource Limited Devices](https://mlsanity.com/paper/2108.08380/) (2021)
- [Cross-Camera View-Overlap Recognition](https://mlsanity.com/paper/2208.11661/) (2022)
- [LSGDDN-LCD: An Appearance-based Loop Closure Detection using Local Superpixel Grid Descriptors and Incremental Dynamic Nodes](https://mlsanity.com/paper/2304.03872/) (2023)

## Abstract (arXiv)

Reliable and efficient Visual Place Recognition is a major building block of modern SLAM systems. Leveraging on our prior work, in this paper we present a Hamming Distance embedding Binary Search Tree (HBST) approach for binary Descriptor Matching and Image Retrieval. HBST allows for descriptor Search and Insertion in logarithmic time by exploiting particular properties of binary Feature descriptors. We support the idea behind our search structure with a thorough analysis on the exploited descriptor properties and their effects on completeness and complexity of search and insertion. To validate our claims we conducted comparative experiments for HBST and several state-of-the-art methods on a broad range of publicly available datasets. HBST is available as a compact open-source C++ header-only library.