HBST: A Hamming Distance Embedding Binary Search Tree for Feature-Based Visual Place Recognition

Reliable and efficient visual place recognition is a major building block of modern SLAM systems. Leveraging on our prior work, in this letter, 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 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.