Predictive Data Acquisition for Lifelong Visual Teach, Repeat and Learn

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00376055" target="_blank" >RIV/68407700:21230/24:00376055 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/LRA.2024.3421193" target="_blank" >https://doi.org/10.1109/LRA.2024.3421193</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/LRA.2024.3421193" target="_blank" >10.1109/LRA.2024.3421193</a>

Result language

angličtina

Original language name

Predictive Data Acquisition for Lifelong Visual Teach, Repeat and Learn

Original language description

Nowadays, robots can operate in environments which are not tailored for them. This allows their deployments in changing and human-populated environments, which recent advances in machine learning methods enabled. The efficiency of these methods is largely determined by the quality of their training data. An up-to-date and well-balanced training dataset is paramount for achieving robust robot operation. To achieve long-term operation, the robot has to deal with perpetual environmental changes, forcing it to keep its models up-to-date. We present an exploration method allowing a mobile robot to gather high-quality data to update its models both while performing its duties and when idle, maximizing effectivity. The robot evaluates the quality of the data gathered in the past and based on that, it creates preferences which influence how often these locations are visited. This exploration method was integrated with a self-supervised visual teach-and-repeat pipeline. We show the precision and robustness of visual-based navigation to improve when using machine-learned models trained by our exploration method. Our research resulted in a robotic navigation system that can not only annotate its training data but also ensure that its training dataset is balanced and up-to-date. The codes, datasets, trained models and examples for our experiments can be found online for better reproducibility at .

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

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

Project

<a href="/en/project/EH22_008%2F0004590" target="_blank" >EH22_008/0004590: Robotics and advanced industrial production</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

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

Name of the periodical

IEEE Robotics and Automation Letters

ISSN

2377-3766

e-ISSN

2377-3766

Volume of the periodical

9

Issue of the periodical within the volume

11

Country of publishing house

US - UNITED STATES

Number of pages

8

Pages from-to

10042-10049

UT code for WoS article

001329045100010

EID of the result in the Scopus database

2-s2.0-85197555308