Rapid Deployment of Visual Path Following via Average Representation

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Rapid Deployment of Visual Path Following via Average Representation
Original language description
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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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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
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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