Abstract
The semantic segmentation problem has been widely studied in the computer vision community. However, state-of-the-art solutions based on deep learning are only available for 2D images. The lack of large annotated datasets makes more difficult the training of models with 3D images. In this work we propose to use the already available 2D deep learning based solutions to semantically segment the 3D environment for robotic applications. Concretely, deep learning applications provide the semantic labeling, and the geometrical information from RGB-D cameras along with the robot pose provides the 3D position.
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Acknowledgments
This work has been partially funded by FEDER funds and the Spanish Government (MICINN) through project TIN2015-65686-C5-3-R. We also want to acknowledge the Red de Agentes Físicos TIN2015-71693-REDT.
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Romero-González, C., Martínez-Gómez, J., García-Varea, I. (2018). 3D Semantic Maps for Scene Segmentation. In: Ollero, A., Sanfeliu, A., Montano, L., Lau, N., Cardeira, C. (eds) ROBOT 2017: Third Iberian Robotics Conference. ROBOT 2017. Advances in Intelligent Systems and Computing, vol 693. Springer, Cham. https://doi.org/10.1007/978-3-319-70833-1_49
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