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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

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

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

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

Publication year

2023

Confidentiality

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

Article name in the collection

Proceedings of 11th European Conference on Mobile Robots

ISBN

979-8-3503-0704-7

ISSN

2639-7919

e-ISSN

—

Number of pages

7

Pages from-to

57-63

Publisher name

Institute of Electrical and Electronics Engineers

Place of publication

Brighton

Event location

Coimbra

Event date

Sep 4, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001082260500009