Abstract
The primitives used to model objects in semantic maps heavily influence their suitability for certain robot tasks, as well as the computational load required to process them. This paper contributes a semantic mapping framework that incrementally and efficiently builds a voxelized representation of the robot workspace, providing a balanced trade-off between model expressiveness and computational load. Our proposal detects objects in intensity images coming from an RGB-D camera, and uses depth information to retrieve their point cloud representations. These point clouds are then voxelized and enhanced with their probability of belonging to certain object categories. Finally, voxels are fused with the semantic map in a Bayesian probabilistic framework. Efficiency comes from its client-server design, which allows multiple mobile robots to participate as clients and leaves computationally intensive processes to the server. The proposed framework has been evaluated in both simulated and real environments, yielding accurate voxelized representations.
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Acknowledgements
Work partially supported by the research projects ARPEGGIO ([PID2020-117057GB-I00]) and HOUNDBOT ([P20-01302]), funded by the Spanish Government and the Regional Government of Andalusia with support from the ERDF (European Regional Development Funds), respectively.
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Perez-Bazuelo, AJ., Ruiz-Sarmiento, JR., Ambrosio-Cestero, G., Gonzalez-Jimenez, J. (2023). Towards a Voxelized Semantic Representation of the Workspace of Mobile Robots. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2023. Lecture Notes in Computer Science, vol 14135. Springer, Cham. https://doi.org/10.1007/978-3-031-43078-7_16
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