Abstract
In order to achieve accurate wheel gripping by truss manipulators, accurate point cloud alignment of train wheels is required. In this paper, the wheels are detected by binocular laser displacement sensors. And accurate point cloud alignment is achieved by using the point cloud denoising and streamlining algorithm, particle swarm-based algorithm, and improved ICP algorithm for 3D point cloud data. The experimental results show that reducing noise and data volume can improve the stitching effect and alignment efficiency of 3D point cloud models. The algorithm of this paper is experimentally verified to be 43% better in error accuracy and 93% faster in time-consuming than the traditional ICP algorithm, which verifies the effectiveness of its method.
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