Abstract
In this paper, a method of point cloud recognition and segmentation based on neural network is introduced. This method will be applied to the specific industrial scene to detect whether there are sudden obstacles around the robot during the working process. This method is mainly divided into two parts. The first part is to design an efficient neural network structure, which achieves modification from state of art methods. The second part is to generate the corresponding neural network point cloud training data set for the specific scene. A simulation model is used to generate scene point cloud, and a large number of data are generated randomly. Simulation results verify the effectiveness and practicability of this method.
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Acknowledgements
The authors would like to gratefully acknowledge the reviewers comments. This work is supported by National Key R&D Program of China (Grant Nos. 2019YFB1310200), National Natural Science Foundation of China (Grant Nos. U1713207 and 52075180), Science and Technology Program of Guangzhou (Grant Nos. 201904020020), and the Fundamental Research Funds for the Central Universities.
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Wang, N., Lin, J., Zhong, K., Zhang, X. (2020). Research on Point Cloud Processing Algorithm Applied to Robot Safety Detection. 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_39
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DOI: https://doi.org/10.1007/978-3-030-66645-3_39
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