Abstract
The application of intelligent robots to perform the depalletizing task is a common requirement in warehouse automation. To solve the problem of identification and localization caused by the disorderly stacking of boxes in pallet, and to eliminate the interference of the reflective material contained in the stacks, this paper proposes an edge extraction algorithm that combines 3D and 2D data. The algorithm firstly obtains the plane position data through three-dimensional point cloud, secondly uses an edge detection algorithm to extract edges in the two-dimensional image. Finally, an optimal segmentation strategy is performed, which is based on the results of point cloud segmentation, edge extraction, and the size information of boxes. Therefore, we can determine the position of each box in the space accurately. Compared with algorithms that only use 2D and 3D data, our method can effectively filter interference. The accuracy rate is close to 100%, which meets the requirements of industrial applications.
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
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI 8, 679–698 (1986)
Martin, D.R., Fowlkes, C.C., Malik, J.: Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans. Pattern Anal. Mach. Intell. 26, 530–549 (2004)
Bertasius, G., Shi, J., Torresani, L.: DeepEdge: a multi-scale bifurcated deep network for top-down contour detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07–12 June, pp. 4380–4389 (2015)
Xie, S., Tu, Z.: Holistically-nested edge detection. Int. J. Comput. Vis. 125, 3–18 (2017)
Liu, Y., et al.: Richer convolutional features for edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 41, 1939–1946 (2019)
Arbeláez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33, 898–916 (2011)
Chen, X., Shi, J., Jia, R.: Application of 3D machine vision in intelligent robot destacking. Electrotech. Appl. S1, 31–35 (2019)
Xu, K., Liang, L., Wang, F., et al.: Research on depalletizing analysis and localization method of mixed-loaded pallet based on three-dimensional vision technology. Harbin Institute of Technology (2019)
Zuo, L.: Research on automatic identification and location of scattered parts for robot picking. Harbin Institute of Technology (2015)
Acknowledgement
This research was supported by the National Key R&D Program of China 2018YFB1309300. The authors would like to personally thank all the team members.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, W., Gao, Y., Wang, Y., Liu, Z., Chen, D. (2020). A Novel Edge Detection and Localization Method of Depalletizing Robot. In: Chan, C.S., et al. Intelligent Robotics and Applications. ICIRA 2020. Lecture Notes in Computer Science(), vol 12595. Springer, Cham. https://doi.org/10.1007/978-3-030-66645-3_43
Download citation
DOI: https://doi.org/10.1007/978-3-030-66645-3_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-66644-6
Online ISBN: 978-3-030-66645-3
eBook Packages: Computer ScienceComputer Science (R0)