| Autor: |
Zuria Bauer, Felix Escalona, Edmanuel Cruz, Miguel Cazorla, Francisco Gomez-Donoso |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 7, Pp 185076-185085 (2019) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2019.2960798 |
| Popis: |
As it is well known, some versions of the Pepper robot provide poor depth perception due to the lenses it has in front of the tridimensional sensor. In this paper, we present a method to improving that faulty 3D perception. Our proposal is based on a combination of the actual depth readings of Pepper and a deep learning-based monocular depth estimation. As shown, the combination of both of them provides a better 3D representation of the scene. In previous works we made an initial approximation of this fusion technique, but it had some drawbacks. In this paper we analyze the pros and cons of the Pepper readings, the monocular depth estimation method and our previous fusion method. Finally, we demonstrate that the proposed fusion method outperforms them all. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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