Abstract
In this paper, a deep learning-based semantic SLAM (simultaneous localization and mapping) algorithm for dynamic environment is proposed to address the problem that the presence of dynamic objects in the environment has an impact on map building. First, the semantic segmentation network is used to semantically segment the image and obtain the semantic information of the objects in the environment; second, the semantic information is incorporated into the visual odometry and the feature points are tracked using the optical flow method to detect dynamic objects and remove them from the map building process; finally, the two-dimensional semantic information generated by the semantic segmentation network is fused with the three-dimensional geometric information generated by the visual SLAM system to semantic maps are constructed and validated on the TUM dataset, confirming that the algorithm can build static semantic maps without moving objects in dynamic environments.
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
References
Konda, K., Memisevic, R.: Learning visual odometry with a convolutional network. In: Proceedings of the 10th International Conference on Computer Vision Theory and Applications, pp. 486–490. SCITCC Press, Lisbon, Portugal (2015)
Dosovitskiy, A., Fischery, P., Ilg, E., et al.: FlowNet: learning opticalflow with convolutional networks. In: IEEE International Conference on Computer Vision, pp. 2758–2766. IEEE, Piscataway, USA (2015)
Handa, A., Bloesch, M., Pătrăucean, V., et al.: gvnn: Neural net-work library for geometric computer vision . Lecture Notes in Computer Science, vol. 9915, pp. 67–82. Springer-Verlag, Berlin, Germany (2016)
Bai, D.D., Wang, C.Q., Zhang, B.: Matching-range-constrained real-time loop closure detection with CNNs features. Robot. Biomimet. 3(1), 70–75 (2016)
Gao, X., Zhang, T.: Loop closure detection for visual slam systems using deep neural networks. In: 34th Chinese Control Conference, pp. 5851–5856. IEEE, Piscataway, USA (2015)
Nguyen, N.C., Pau, G.S.H., Maday, Y., et al.: A general multipurpose interpolation procedure: the magic points. Commun. Pure Appl. Anal. (S1534-0392) 8(1), 383–404 (2017)
Sharif Razavian, A., Azizpour, H., Sullivan, J., et al.: CNN features off-the-shelf: an astounding baseline for recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 806–813 (2014)
Wang, S., Clark, R., Wen, H., et al.: Deepvo: towards end-to-end visual odometry with deep recurrent convolutional neural networks. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 2043–2050. IEEE (2017)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)
Garcia-Garcia, A., Orts-Escolano, S., Oprea, S.O., et al.: A review on deep learning techniques applied to semantic segmentation. Trans. Pattern Anal. Mach. Intell. (TPAMI) (2017)
Treml, M., Arjona-Medina, J., Unterthiner, T., et al.: Speeding up semantic segmentation for autonomous driving. In: MLITS, NIPS Workshop (2016)
Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings of the 7th International Joint Conference on Artificial Intelligence, Vol. 2. Morgan Kaufmann Publishers Inc. (1981)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zheng, F., Wang, X., Xia, F., Shi, Z. (2022). Algorithm Design of Semantic Map Construction for Dynamic Scene. In: Li, X. (eds) Advances in Intelligent Automation and Soft Computing. IASC 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 80. Springer, Cham. https://doi.org/10.1007/978-3-030-81007-8_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-81007-8_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-81006-1
Online ISBN: 978-3-030-81007-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)