Accurate and Robust Teach and Repeat Navigation by Visual Place Recognition: A CNN Approach

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F20%3A00348826" target="_blank" >RIV/68407700:21730/20:00348826 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/IROS45743.2020.9341764" target="_blank" >https://doi.org/10.1109/IROS45743.2020.9341764</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IROS45743.2020.9341764" target="_blank" >10.1109/IROS45743.2020.9341764</a>

Result language
angličtina
Original language name
Accurate and Robust Teach and Repeat Navigation by Visual Place Recognition: A CNN Approach
Original language description
We propose a visual teach-and-repeat navigation system, SSM-Nav, which is based on the output of the re cently introduced SSM visual place recognition methodology. During the teach phase, a teleoperated wheeled robot stores in a database features of images taken along an arbitrary route. During the repeat phase or navigation, a CNN-based comparison of each captured image is performed against the database. With the help of a particle filter, the best location’s image is selected and its horizontal offset with respect to the current scene used to correct the steering of the robot and to navigate. Indoor tests in our lab show a maximum error of less than 10 cm and excellent robustness to perturbations such as drastic changes in illumination, lateral displacements, different starting positions or even kidnapping. Preliminary outdoor tests on a 0.22 Km route show promising results, with an estimated maximum error of less than 25 cm.
Czech name
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Czech description
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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
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems
ISBN
978-1-7281-6212-6
ISSN
2153-0858
e-ISSN
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Number of pages
7
Pages from-to
6018-6024
Publisher name
IEEE Computer Society
Place of publication
Los Alamitos
Event location
Las Vegas
Event date
Oct 24, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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