Abstract
In social robotics, it is important that a mobile robot knows where it is because it provides a starting point for other activities such as moving from one room to another. As a contribution to solving this problem in the field of the semantic location of the mobile robot, we pro- pose to implement a methodology of recognition and scene learning in a real domestic environment. For this purpose, we used images from five different residences to create a dataset with which the base model was trained. The effectiveness of the implemented base model is evaluated in different scenarios. When the accuracy of the site identification decreases, the user provides feedback to the robot so that it can process the information collected from the new environment and re-identify the current location. The results obtained reinforce the need to acquire more knowledge when the environment is not recognizable by the pre-trained model.
Keywords
- Robotics
- Deep learning
- Semantic localization
- CNN training
- Neural networks
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Acknowledgements
This work has been supported by the Spanish Government TIN2016-76515R Grant, supported with Feder funds. Edmanuel Cruz is funded by a Panamenian grant for PhD studies IFARHU & SENACYT 270-2016-207. This work has also been supported by a Spanish grant for PhD studies ACIF/2017/243. Thanks to Nvidia also for the generous donation of a Titan Xp and a Quadro P6000.
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Cruz, E., Bauer, Z., Rangel, J.C., Cazorla, M., Gomez-Donoso, F. (2019). Semantic Localization of a Robot in a Real Home. In: Fuentetaja Pizán, R., García Olaya, Á., Sesmero Lorente, M., Iglesias Martínez, J., Ledezma Espino, A. (eds) Advances in Physical Agents. WAF 2018. Advances in Intelligent Systems and Computing, vol 855. Springer, Cham. https://doi.org/10.1007/978-3-319-99885-5_1
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