Self-supervised Learning for Fusion of IR and RGB Images in Visual Teach and Repeat Navigation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00369475" target="_blank" >RIV/68407700:21230/23:00369475 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/ECMR59166.2023.10256333" target="_blank" >https://doi.org/10.1109/ECMR59166.2023.10256333</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ECMR59166.2023.10256333" target="_blank" >10.1109/ECMR59166.2023.10256333</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Self-supervised Learning for Fusion of IR and RGB Images in Visual Teach and Repeat Navigation

Popis výsledku v původním jazyce

With increasing computation power, longer battery life and lower prices, mobile robots are becoming a viable option for many applications. When the application requires long-term autonomy in an uncontrolled environment, it is necessary to equip the robot with a navigation system robust to environmental changes. Visual Teach and Repeat (VT&R) is one such navigation system that is lightweight and easy to use. Similarly, as other methods rely on camera input, the performance of VT&R can be highly influenced by changes in the scene's appearance. One way to address this problem is to use machine learning or/and add redundancy to the sensory input. However, it is usually complicated to collect long-term datasets for given sensory input, which can be exploited by machine learning methods to extract knowledge about possible changes in the environment from the data. In this paper, we show that we can use a dataset not containing the environmental changes to train a model processing infrared images and improve the robustness of the VT&R framework by fusion with the classic method based on RGB images. In particular, our experiments show that the proposed training scheme and fusion method can alleviate the problems arising from adverse illumination changes. Our approach can broaden the scope of possible VT&R applications that require deployment in environments with significant illumination changes.

Název v anglickém jazyce

Self-supervised Learning for Fusion of IR and RGB Images in Visual Teach and Repeat Navigation

Popis výsledku anglicky

With increasing computation power, longer battery life and lower prices, mobile robots are becoming a viable option for many applications. When the application requires long-term autonomy in an uncontrolled environment, it is necessary to equip the robot with a navigation system robust to environmental changes. Visual Teach and Repeat (VT&R) is one such navigation system that is lightweight and easy to use. Similarly, as other methods rely on camera input, the performance of VT&R can be highly influenced by changes in the scene's appearance. One way to address this problem is to use machine learning or/and add redundancy to the sensory input. However, it is usually complicated to collect long-term datasets for given sensory input, which can be exploited by machine learning methods to extract knowledge about possible changes in the environment from the data. In this paper, we show that we can use a dataset not containing the environmental changes to train a model processing infrared images and improve the robustness of the VT&R framework by fusion with the classic method based on RGB images. In particular, our experiments show that the proposed training scheme and fusion method can alleviate the problems arising from adverse illumination changes. Our approach can broaden the scope of possible VT&R applications that require deployment in environments with significant illumination changes.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of 11th European Conference on Mobile Robots

ISBN

979-8-3503-0704-7

ISSN

2639-7919

e-ISSN

—

Počet stran výsledku

7

Strana od-do

57-63

Název nakladatele

Institute of Electrical and Electronics Engineers

Místo vydání

Brighton

Místo konání akce

Coimbra

Datum konání akce

4. 9. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001082260500009