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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Blanco, J.L., González, J., Fernández-Madrigal, J.A.: Subjective local maps for hybrid metric-topological slam. Robot. Auton. Syst. 57(1), 64–74 (2009)
Chatila, R., Laumond, J.: Position referencing and consistent world modeling for mobile robots. In: Proceedings. 1985 IEEE International Conference on Robotics and Automation, vol. 2, pp. 138–145. IEEE (1985)
Ester, M., Kriegel, H.P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, pp. 226–231 (1996)
Fernandez-Chaves, D., Ruiz-Sarmiento, J.R., Petkov, N., Gonzalez-Jimenez, J.: Vimantic, a distributed robotic architecture for semantic mapping in indoor environments. Int. J. Knowl.-Based Syst. 232, 107440 (2021)
Fernandez-Chaves, D., Ruiz-Sarmiento, J.R., Jaenal, A., Petkov, N., Gonzalez-Jimenez, J.: Robot@VirtualHome, an ecosystem of virtual environments and tools for realistic indoor robotic simulation. Expert Syst. Appl. 208, 117970 (2022)
Fox, D., Burgard, W., Dellaert, F., Thrun, S.: Monte carlo localization: efficient position estimation for mobile robots. AAAI/IAAI 1999(343–349), 2–2 (1999)
Galindo, C., Fernández-Madrigal, J.A., González, J., Saffiotti, A.: Robot task planning using semantic maps. Robot. Auton. Syst. 56(11), 955–966 (2008)
González-Jiménez, J., Galindo, C., Ruiz-Sarmiento, J.: Technical improvements of the giraff telepresence robot based on users’ evaluation. In: 2012 IEEE RO-MAN, pp. 827–832 (2012)
Grinvald, M., et al.: Volumetric instance-aware semantic mapping and 3D object discovery. IEEE Robot. Autom. Lett. 4(3), 3037–3044 (2019)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)
Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics (2023). https://github.com/ultralytics/ultralytics
Juliani, A., et al.: Unity: a general platform for intelligent agents. arXiv preprint arXiv:1809.02627 (2018)
Kuipers, B.: Modeling spatial knowledge. Cogn. Sci. 2(2), 129–153 (1978)
Lam, L., Lee, S.W., Suen, C.Y.: Thinning methodologies-a comprehensive survey. IEEE Trans. Pattern Anal. Mach. Intell. 14(09), 869–885 (1992)
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: ICCV, pp. 10012–10022 (2021)
Macenski, S., Tsai, D., Feinberg, M.: Spatio-temporal voxel layer: a view on robot perception for the dynamic world. Int. J. Adv. Robot. Syst. 17(2) (2020)
Macenski, S., Foote, T., Gerkey, B., Lalancette, C., Woodall, W.: Robot operating system 2: design, architecture, and uses in the wild. Sci. Robot. 7(66), eabm6074 (2022)
Matez-Bandera, J.L., Fernandez-Chaves, D., Ruiz-Sarmiento, J.R., Monroy, J., Petkov, N., Gonzalez-Jimenez, J.: LTC-Mapping, enhancing long-term consistency of object-oriented semantic maps in robotics. Sensors 22(14), 5308 (2022)
Milstein, A.: Occupancy grid maps for localization and mapping. Motion Plann. 381–408 (2008)
Mutlu, B., Roy, N., Šabanović, S.: Cognitive human–robot interaction. In: Siciliano, B., Khatib, O. (eds.) Springer Handbook of Robotics, pp. 1907–1934. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32552-1_71
Nakajima, Y., Saito, H.: Efficient object-oriented semantic mapping with object detector. IEEE Access 7, 3206–3213 (2018)
Narita, G., Seno, T., Ishikawa, T., Kaji, Y.: Panopticfusion: online volumetric semantic mapping at the level of stuff and things. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4205–4212. IEEE (2019)
Nüchter, A., Hertzberg, J.: Towards semantic maps for mobile robots. Robot. Auton. Syst. 56(11), 915–926 (2008)
Rosinol, A., Abate, M., Chang, Y., Carlone, L.: Kimera: an open-source library for real-time metric-semantic localization and mapping. In: ICRA (2020)
Ruiz-Sarmiento, J.R., Galindo, C., Gonzalez-Jimenez, J.: Building multiversal semantic maps for mobile robot operation. Knowl.-Based Syst. 119, 257–272 (2017)
Ruiz-Sarmiento, J.R., Galindo, C., González-Jiménez, J.: Robot@ home, a robotic dataset for semantic mapping of home environments. Int. J. Robot. Res. 36(2), 131–141 (2017)
Song, S., Yu, F., Zeng, A., Chang, A.X., Savva, M., Funkhouser, T.: Semantic scene completion from a single depth image. In: CVPR, pp. 1746–1754 (2017)
Wu, Y., Kirillov, A., Massa, F., Lo, W., Girshick, R.: Detectron2 repository (2023). https://github.com/facebookresearch/detectron2/. Accessed March 30 2023
Xiang, Y., Choi, W., Lin, Y., Savarese, S.: Data-driven 3D voxel patterns for object category recognition. In: ICVPR
Zhou, Q.Y., Park, J., Koltun, V.: Open3D: a modern library for 3D data processing. arXiv:1801.09847 (2018)
Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable DETR: deformable transformers for end-to-end object detection. In: ICLR 2021 (2021)
Zuñiga-Noël, D., Ruiz-Sarmiento, J.R., Gomez-Ojeda, R., Gonzalez-Jimenez, J.: Automatic multi-sensor extrinsic calibration for mobile robots. IEEE Robot. Autom. Lett. 4(3), 2862–2869 (2019)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-031-43078-7_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43077-0
Online ISBN: 978-3-031-43078-7
eBook Packages: Computer ScienceComputer Science (R0)