Abstract
In response to the problems of low execution efficiency and poor real-time performance of traditional point cloud clustering algorithms when there is a large amount of point cloud data, which cannot meet the requirements of automatic and efficient segment grabbing, this paper proposes a shield machine segment position recognition algorithm based on improved voxel and seed filling. A multi-objective optimization model for point cloud voxel size, nail height, and nail area was established to achieve point cloud voxel space division by constraining the deviation range of segment hanging space. By setting the voxel point cloud threshold and introducing the seed filling method, point cloud clustering was realized, achieving the goal of quickly identifying the spatial coordinates of segment hanging nails. Engineering verification was carried out based on the construction project of China Railway 1084 shield tunneling, and the results showed that under approximate accuracy, the efficiency of this algorithm is 3.2 times that of European Clustering.
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Zhang, P. et al. (2023). A Shield Machine Segment Position Recognition Algorithm Based on Improved Voxel and Seed Filling. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14272. Springer, Singapore. https://doi.org/10.1007/978-981-99-6480-2_17
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DOI: https://doi.org/10.1007/978-981-99-6480-2_17
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