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Optimal Truncated MobileNet-Based Image Binarization for Pose-Based Visual Servoing of Autonomous Mobile Robot

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Technologies and Applications of Artificial Intelligence (TAAI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2075))

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Abstract

Pose-based visual servoing (PBVS) can complement the frequent drift issue of light detection and ranging (LiDAR) coordinate of LiDAR-based simultaneous localization and mapping (SLAM), navigation, and servoing technology, especially when autonomous mobile robot (AMR) works on the automatic docking alignment missions for automatic pallet engaging or automatic battery charging whose alignment precision requirement is extremely stricter. But PBVS occasionally suffers from the poor detected image quality of ARTag landmark to cause the reading drift or error. This paper proposes a light-weight deep-learning image binarization method based on optimal truncated MobileNet model to preprocess the image quality of ARTag landmarks so that PBVS can evaluate the distance and pose between the ARTag landmark and the camera sensor more accurately, promptly, and steadily, for better feasibility of PBVS on the automatic docking alignment missions. Experimental results show, against conventional image-processing-based image binarization, conventional computer-vision-based image binarization, and conventional deep-learning-based image binarization, the proposed optimal truncated MobileNet-based image binarization not only raises the accuracy and reliability of ARTag’s reading, but also apparently improves the effectiveness and efficiency of PBVS’s operation, especially under environmental conditions of shadow occlusion, image blurring, low contrast, uneven illumination, or complex background.

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References

  1. Azadeh, K., Koster, R., Roy, D.: Robotized and automated warehouse systems: review and recent developments. Transp. Sci. 53(4), 917–945 (2019)

    Article  Google Scholar 

  2. Hossain, S.G.M., Jamil, H., Ali, M.Y., Haq, M.Z.: Automated guided vehicles for industrial logistics - development of intelligent prototypes using appropriate technology. In: Proceedings of International Conference on Computer and Automation Engineering (ICCAE), pp. 237–241 (2010)

    Google Scholar 

  3. Zhao, L., Ding, Y.: Design of AGV low-delay visual servo system. In: Proceedings of International Conference on Audio, Language and Image Processing (ICALIP), pp. 6–10 (2018)

    Google Scholar 

  4. Kelly, A., Nagy, B., Stager, D., Unnikrishnan, R.: Field and service applications - an infrastructure-free automated guided vehicle based on computer vision - an effort to make an industrial robot vehicle that can operate without supporting infrastructure. IEEE Robot. Autom. Mag. 14(3), 24–34 (2007)

    Article  Google Scholar 

  5. Fiala, M.: ARTag, a fiducial marker system using digital techniques. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 2, pp. 590–596 (2005)

    Google Scholar 

  6. Niekum, S.: ar_track_alvar. ROS Wiki [Online]. http://www.ros.org/wiki/ar_track_alvar

  7. Wang, H., Wei, S., Chen, Y.: An improved Rao-Blackwellized particle filter for SLAM. In: Proceedings of International Symposium on Intelligent Information Technology Application Workshops, pp. 515–518 (2008)

    Google Scholar 

  8. Collins, T., Collins, J.J., Ryan, D.: Occupancy grid mapping: an empirical evaluation. In: Proceedings of Mediterranean Conference on Control and Automation, pp. 1–6 (2007)

    Google Scholar 

  9. Hanten, R., et al.: Vector-AMCL: vector based adaptive Monte Carlo localization for indoor maps. Intell. Auton. Syst. 14, 403–416 (2017)

    Google Scholar 

  10. Deng, Y., et al.: Fuzzy Dijkstra algorithm for shortest path problem under uncertain environment. Appl. Soft Comput. 12, 1231–1237 (2012)

    Google Scholar 

  11. Keller, M., et al.: Planning of optimal collision avoidance trajectories with timed elastic bands. IFAC Proc. Volumes 47, 9822–9827 (2014)

    Article  Google Scholar 

  12. Nor, R.M., et al.: Mobile robot stable-target navigation control via encoder data feedback. In: Proceedings of International Malaysia-Ireland Joint Symposium on Engineering, Science and Business (2012)

    Google Scholar 

  13. Wang, K., Liu, Y., Li, L.: Visual servoing trajectory tracking of nonholonomic mobile robots without direct position measurement. IEEE Trans. Robot. 30, 1026–1035 (2014)

    Google Scholar 

  14. Al-amri, S.S., Kalyankar, N.V., Khamitkar, S.D.: Image segmentation by using threshold techniques. J. Comput. 2, 83–86 (2010)

    Google Scholar 

  15. Eyupoglu, C.: Implementation of Bernsen’s locally adaptive binarization method for gray scale images. Online J. Sci. Technol. 7, 68–72 (2017)

    Google Scholar 

  16. Huang, D.-Y., Wang, C.-H.: Optimal multi-level thresholding using a two-stage Otsu optimization approach. Pattern Recogn. Lett. 30, 275–284 (2009)

    Article  Google Scholar 

  17. Zhou, S.-F., et al.: An improved adaptive document image binarization method. In: Proceedings of International Congress on Image and Signal Processing, pp. 1–5 (2009)

    Google Scholar 

  18. Calvo-Zaragoza, J., Gallego, A.-J.: A selectional auto-encoder approach for document image binarization. Pattern Recogn. 86, 37–47 (2019)

    Article  Google Scholar 

  19. Tensmeyer, C., Martinez, T.: Document image binarization with fully convolutional neural networks. In: Proceedings of IAPR International Conference on Document Analysis and Recognition (ICDAR), pp. 99–104 (2017)

    Google Scholar 

  20. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4510–4520 (2018)

    Google Scholar 

  21. Gavish, M., Donoho, D.L.: The optimal hard threshold for singular values is 4/√3. IEEE Trans. Inf. Theory 60(8), 5040–5053 (2014)

    Article  MathSciNet  Google Scholar 

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Acknowledgement

This work was financially supported by the “Intelligent Recognition Industry Service Center” from The Featured Areas Research Center Program within the framework of Higher Education Sprout Project, Ministry of Education (MOE), Taiwan.

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Authors and Affiliations

  1. Department of Electrical Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan

    Chian C. Ho & Cian-Duo Lin

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  1. Chian C. Ho
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  2. Cian-Duo Lin
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Correspondence to Chian C. Ho .

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Editors and Affiliations

  1. National Yunlin University of Science and Technology, Douliu, Taiwan

    Chao-Yang Lee

  2. National Yunlin University of Science and Technology, Douliou, Taiwan

    Chun-Li Lin

  3. National Yunlin University of Science and Technology, Douliou, Taiwan

    Hsuan-Ting Chang

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Ho, C.C., Lin, CD. (2024). Optimal Truncated MobileNet-Based Image Binarization for Pose-Based Visual Servoing of Autonomous Mobile Robot. In: Lee, CY., Lin, CL., Chang, HT. (eds) Technologies and Applications of Artificial Intelligence. TAAI 2023. Communications in Computer and Information Science, vol 2075. Springer, Singapore. https://doi.org/10.1007/978-981-97-1714-9_17

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  • DOI: https://doi.org/10.1007/978-981-97-1714-9_17

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-1713-2

  • Online ISBN: 978-981-97-1714-9

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