Advertisement

Monocular SLAM System in Dynamic Scenes Based on Semantic Segmentation

  • Chao Sheng
  • Shuguo PanEmail author
  • Pan Zeng
  • Lixiao Huang
  • Tao Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11903)

Abstract

The traditional feature-based visual SLAM algorithm is based on the static environment assumption when recovering scene information and camera motion. The dynamic objects in the scene will affect the positioning accuracy. In this paper, we propose to combine the image semantic segmentation based on deep learning method with the traditional visual SLAM framework to reduce the interference of dynamic objects on the positioning results. Firstly, a supervised Convolutional Neural Network (CNN) is used to segment objects in the input image to obtain the semantic image. Secondly, the feature points are extracted from the original image, and the feature points of the dynamic objects (cars and pedestrians) are eliminated according to the semantic image. Finally, the traditional monocular SLAM method is used to track the camera motion based on the eliminated feature points. The experiments on the Apolloscape datasets show that compared with the traditional method, the proposed method improves the positioning accuracy in dynamic scenes by about 17%.

Keywords

Monocular SLAM Dynamic objects Deep learning Semantic segmentation CNN 

Notes

Acknowledgments

This research was supported by Jiangsu Surveying and Mapping Geographic Information Scientific Research Project (JSCHKY201808), National Key Research and Development Project (2016YFB0502101) and National Natural Science Foundation of China (41574026, 41774027).

References

  1. 1.
    Davison, A.J., Reid, I.D., Molton, N.D., et al.: MonoSLAM: real-time single camera SLAM. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 1052–1067 (2007)CrossRefGoogle Scholar
  2. 2.
    Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: Proceedings of the Sixth IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR 2007), Nara, Japan. IEEE, November 2007Google Scholar
  3. 3.
    Engel, J., Schöps, T., Cremers, D.: LSD-SLAM: large-scale direct monocular SLAM. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8690, pp. 834–849. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-10605-2_54CrossRefGoogle Scholar
  4. 4.
    Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2016)CrossRefGoogle Scholar
  5. 5.
    Mur-Artal, R., Montiel, J.M.M., Tardos, J.D.: ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Trans. Rob. 31(5), 1147–1163 (2015)CrossRefGoogle Scholar
  6. 6.
    Mur-Artal, R., Tardos, J.D.: ORB-SLAM2: an open-source SLAM system for monocular, stereo, and RGB-D cameras. IEEE Trans. Rob. 33(5), 1255–1262 (2017)CrossRefGoogle Scholar
  7. 7.
    Tan, N.W., Liu, N.H., Dong, Z., et al.: Robust monocular SLAM in dynamic environments. In: 2013 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). IEEE Computer Society (2013)Google Scholar
  8. 8.
    Zou, D., Tan, P.: CoSLAM: collaborative visual SLAM in dynamic environments. IEEE Trans. Pattern Anal. Mach. Intell. 35(2), 354–366 (2012)CrossRefGoogle Scholar
  9. 9.
    Chen, W., Fang, M., Liu, Y.H., et al.: Monocular semantic SLAM in dynamic street scene based on multiple object tracking. In: IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), pp. 599–604. IEEE (2017)Google Scholar
  10. 10.
    Zhao, H., Qi, X., Shen, X., Shi, J., Jia, J.: ICNet for real-time semantic segmentation on high-resolution images. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 418–434. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01219-9_25CrossRefGoogle Scholar
  11. 11.
    Huang, X., Cheng, X., Geng, Q., et al.: The apolloscape dataset for autonomous driving. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 954–960 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Chao Sheng
    • 1
  • Shuguo Pan
    • 1
    Email author
  • Pan Zeng
    • 1
  • Lixiao Huang
    • 1
  • Tao Zhao
    • 1
  1. 1.School of Instrument Science and EngineeringSoutheast UniversityNanjingChina

Personalised recommendations