KEYWORDS: Point clouds, Ranging, Data modeling, 3D modeling, Visual process modeling, Tunable filters, Distance measurement, Error control coding, Detection and tracking algorithms, Covariance matrices
Aiming at the problems of unorganization and low computational efficiency in the traditional ranging method based on point cloud, in order to realize the rapid measurement of object spacing, we propose a machine vision fast ranging method based on PCL (Point Cloud Library). We propose an improved point cloud filtering algorithm based on k-neighborhood density and a point cloud simplification algorithm based on relative position, which reduce the number of redundant point clouds by more than 80% and improves the computational efficiency by more than 90%. Through error correction, the relative error rate of ranging results is controlled within 3%. The performance analysis and experimental simulation results show that this method is superior to other methods in terms of timeliness and accuracy of ranging, which can effectively improve the timeliness of point cloud calculation and realize the rapid determination of object spacing.
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