On Model-Free Re-ranking for Visual Place Recognition with Deep Learned Local Features

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00376777" target="_blank" >RIV/68407700:21230/24:00376777 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21730/24:00376777

Výsledek na webu

<a href="https://doi.org/10.1109/TIV.2024.3404564" target="_blank" >https://doi.org/10.1109/TIV.2024.3404564</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TIV.2024.3404564" target="_blank" >10.1109/TIV.2024.3404564</a>

Jazyk výsledku

angličtina

Název v původním jazyce

On Model-Free Re-ranking for Visual Place Recognition with Deep Learned Local Features

Popis výsledku v původním jazyce

Re-ranking is the second stage of a visual place recognition task, in which the system chooses the best-matching images from a pre-selected subset of candidates. Model-free approaches compute the image pair similarity based on a spatial comparison of corresponding local visual features, eliminating the need for computationally expensive estimation of a model describing transformation between images. The article focuses on model-free re-ranking based on standard local visual features and their applicability in long-term autonomy systems. It introduces three new model-free re-ranking methods that were designed primarily for deep-learned local visual features. These features evince high robustness to various appearance changes, which stands as a crucial property for use with long-term autonomy systems. All the introduced methods were employed in a new visual place recognition system together with the D2-net feature detector (Dusmanu, 2019) and experimentally tested with diverse, challenging public datasets. The obtained results are on par with current state-of-the-art methods, affirming that model-free approaches are a viable and worthwhile path for long-term visual place recognition.

Název v anglickém jazyce

On Model-Free Re-ranking for Visual Place Recognition with Deep Learned Local Features

Popis výsledku anglicky

Re-ranking is the second stage of a visual place recognition task, in which the system chooses the best-matching images from a pre-selected subset of candidates. Model-free approaches compute the image pair similarity based on a spatial comparison of corresponding local visual features, eliminating the need for computationally expensive estimation of a model describing transformation between images. The article focuses on model-free re-ranking based on standard local visual features and their applicability in long-term autonomy systems. It introduces three new model-free re-ranking methods that were designed primarily for deep-learned local visual features. These features evince high robustness to various appearance changes, which stands as a crucial property for use with long-term autonomy systems. All the introduced methods were employed in a new visual place recognition system together with the D2-net feature detector (Dusmanu, 2019) and experimentally tested with diverse, challenging public datasets. The obtained results are on par with current state-of-the-art methods, affirming that model-free approaches are a viable and worthwhile path for long-term visual place recognition.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

20204 - Robotics and automatic control

Projekt

<a href="/cs/project/EH22_008%2F0004590" target="_blank" >EH22_008/0004590: Robotika a pokročilá průmyslová výroba</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Transactions on Intelligent Vehicles

ISSN

2379-8858

e-ISSN

2379-8904

Svazek periodika

9

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

7900-7911

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85194062375