Rapid Deployment of Visual Path Following via Average Representation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00385497" target="_blank" >RIV/68407700:21230/25:00385497 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/s10846-025-02302-8" target="_blank" >https://doi.org/10.1007/s10846-025-02302-8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10846-025-02302-8" target="_blank" >10.1007/s10846-025-02302-8</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Rapid Deployment of Visual Path Following via Average Representation

Popis výsledku v původním jazyce

The advances in deep learning for image processing greatly impacted mobile robots' ability to navigate using vision-based techniques. There are many applications in which the robot is required to follow a specific visual cue. One newly emerging method for autonomous navigation along a consistent visual cue is Visual Teach and Generalise (VTAG). It is a unified approach to extract suitable features for path following in repetitive and structured environments. However, a relatively large number of data samples is required for the time-consuming training of a detector tied to a specific visual cue. We introduce an improved version of VTAG that does not need any neural network training and significantly reduces the requirements for the number of data samples. The core idea is to use an average representation of images collected during short supervised traversals, capturing characteristics of repetitive paths. The average representation is created in a latent space crafted by contrastive learning with the linear matching scheme and can be computed in real time, even on a CPU. The capability of rapid deployment, precision, and robustness of the presented method is evaluated in multiple field experiments performed in different environments.

Název v anglickém jazyce

Rapid Deployment of Visual Path Following via Average Representation

Popis výsledku anglicky

The advances in deep learning for image processing greatly impacted mobile robots' ability to navigate using vision-based techniques. There are many applications in which the robot is required to follow a specific visual cue. One newly emerging method for autonomous navigation along a consistent visual cue is Visual Teach and Generalise (VTAG). It is a unified approach to extract suitable features for path following in repetitive and structured environments. However, a relatively large number of data samples is required for the time-consuming training of a detector tied to a specific visual cue. We introduce an improved version of VTAG that does not need any neural network training and significantly reduces the requirements for the number of data samples. The core idea is to use an average representation of images collected during short supervised traversals, capturing characteristics of repetitive paths. The average representation is created in a latent space crafted by contrastive learning with the linear matching scheme and can be computed in real time, even on a CPU. The capability of rapid deployment, precision, and robustness of the presented method is evaluated in multiple field experiments performed in different environments.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

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)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Journal of Intelligent and Robotic Systems

ISSN

0921-0296

e-ISSN

1573-0409

Svazek periodika

111

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

20

Strana od-do

1-20

Kód UT WoS článku

001564682800002

EID výsledku v databázi Scopus

2-s2.0-105015419482