Szczegóły publikacji
Opis bibliograficzny
Scene recognition for indoor localization of mobile robots using deep CNN / Piotr Wozniak, Hadha Afrisal, Rigel Galindo Esparza, Bogdan KWOLEK // W: Computer vision and graphics : international conference : ICCVG 2018 : Warsaw, Poland, September 17–19, 2018 : proceedings / eds. Leszek J. Chmielewski [et al.]. — [Cham] : Springer International Publishing, cop. 2018. — ( Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 11114. Image Processing, Computer Vision, Pattern Recognition, and Graphics ). — ISBN: 978-3-030-00691-4; e-ISBN: 978-3-030-00692-1. — S. 137–147. — Bibliogr. s. 145–147, Abstr. — Publikacja dostępna online od: 2018-09-14
Autorzy (4)
- Woźniak Piotr
- Afrisal Hadha
- Galindo Esparza Rigel
- AGHKwolek Bogdan
Dane bibliometryczne
| ID BaDAP | 117104 |
|---|---|
| Data dodania do BaDAP | 2018-10-15 |
| Tekst źródłowy | URL |
| DOI | 10.1007/978-3-030-00692-1_13 |
| Rok publikacji | 2018 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference on Computer Vision and Graphics 2018 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
In this paper we propose a deep neural network based algorithm for indoor place recognition. It uses transfer learning to retrain VGG-F, a pretrained convolutional neural network to classify places on images acquired by a humanoid robot. The network has been trained as well as evaluated on a dataset consisting of 8000 images, which were recorded in sixteen rooms. The dataset is freely accessed from our website. We demonstrated experimentally that the proposed algorithm considerably outperforms BoW algorithms, which are frequently used in loop-closure. It also outperforms an algorithm in which features extracted by FC-6 layer of the VGG-F are classified by a linear SVM.
