dc.contributor.author |
Van Eden, Beatrice
|
|
dc.contributor.author |
Botha, Natasha
|
|
dc.contributor.author |
Rosman, B
|
|
dc.date.accessioned |
2024-02-05T07:59:12Z |
|
dc.date.available |
2024-02-05T07:59:12Z |
|
dc.date.issued |
2023-11 |
|
dc.identifier.citation |
Van Eden, B., Botha, N. & Rosman, B. 2023. A comparison of visual place recognition methods using a mobile robot in an indoor environment. http://hdl.handle.net/10204/13561 . |
en_ZA |
dc.identifier.uri |
http://hdl.handle.net/10204/13561
|
|
dc.description.abstract |
Spatial awareness is an important competence for a mobile robotic system. A robot needs to localise and perform context interpretation to provide any meaningful service. With the deep learning tools and readily available sensors, visual place recognition is a first step towards identifying the environment to bring a robot closer to spatial awareness. In this paper, we implement place recognition on a mobile robot considering a deep learning approach. For simple place classification, where the task involves classifying images into a limited number of categories, all three architectures; VGG16, Inception-v3 and ResNet50, perform well. However, considering the pros and cons, the choice may depend on available computational resources and deployment constraints. |
en_US |
dc.format |
Fulltext |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.uri |
https://site.rapdasa.org/wp-content/uploads/2023/10/RAPDASA-Annual-Conference-Book-Complete.pdf |
en_US |
dc.source |
RAPDASA-RobMech-PRASA-AMI Conference, CSIR International Convention Centre, Pretoria, South Africa, 30 October – 2 November 2023 |
en_US |
dc.subject |
Mobile robotic system |
en_US |
dc.subject |
Deep learning tools |
en_US |
dc.subject |
Visual place recognition |
en_US |
dc.subject |
VGG16 |
en_US |
dc.subject |
ResNet50 |
en_US |
dc.subject |
Inception-v3 |
en_US |
dc.title |
A comparison of visual place recognition methods using a mobile robot in an indoor environment |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.description.pages |
18 |
en_US |
dc.description.note |
© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/). |
en_US |
dc.description.cluster |
Manufacturing |
en_US |
dc.description.impactarea |
Industrial AI |
en_US |
dc.identifier.apacitation |
Van Eden, B., Botha, N., & Rosman, B. (2023). A comparison of visual place recognition methods using a mobile robot in an indoor environment. http://hdl.handle.net/10204/13561 |
en_ZA |
dc.identifier.chicagocitation |
Van Eden, Beatrice, Natasha Botha, and B Rosman. "A comparison of visual place recognition methods using a mobile robot in an indoor environment." <i>RAPDASA-RobMech-PRASA-AMI Conference, CSIR International Convention Centre, Pretoria, South Africa, 30 October – 2 November 2023</i> (2023): http://hdl.handle.net/10204/13561 |
en_ZA |
dc.identifier.vancouvercitation |
Van Eden B, Botha N, Rosman B, A comparison of visual place recognition methods using a mobile robot in an indoor environment; 2023. http://hdl.handle.net/10204/13561 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Van Eden, Beatrice
AU - Botha, Natasha
AU - Rosman, B
AB - Spatial awareness is an important competence for a mobile robotic system. A robot needs to localise and perform context interpretation to provide any meaningful service. With the deep learning tools and readily available sensors, visual place recognition is a first step towards identifying the environment to bring a robot closer to spatial awareness. In this paper, we implement place recognition on a mobile robot considering a deep learning approach. For simple place classification, where the task involves classifying images into a limited number of categories, all three architectures; VGG16, Inception-v3 and ResNet50, perform well. However, considering the pros and cons, the choice may depend on available computational resources and deployment constraints.
DA - 2023-11
DB - ResearchSpace
DP - CSIR
J1 - RAPDASA-RobMech-PRASA-AMI Conference, CSIR International Convention Centre, Pretoria, South Africa, 30 October – 2 November 2023
KW - Mobile robotic system
KW - Deep learning tools
KW - Visual place recognition
KW - VGG16
KW - ResNet50
KW - Inception-v3
LK - https://researchspace.csir.co.za
PY - 2023
T1 - A comparison of visual place recognition methods using a mobile robot in an indoor environment
TI - A comparison of visual place recognition methods using a mobile robot in an indoor environment
UR - http://hdl.handle.net/10204/13561
ER -
|
en_ZA |
dc.identifier.worklist |
27452 |
en_US |