Paper
15 January 2024 A PCL-based machine vision fast ranging method
Hongyu Meng, Zhijiang Xie, Ye Lu
Author Affiliations +
Proceedings Volume 12983, Second International Conference on Electrical, Electronics, and Information Engineering (EEIE 2023); 129830Z (2024) https://doi.org/10.1117/12.3017432
Event: Second International Conference on Electrical, Electronics, and Information Engineering (EEIE 2023), 2023, Wuhan, China
Abstract
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.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Hongyu Meng, Zhijiang Xie, and Ye Lu "A PCL-based machine vision fast ranging method", Proc. SPIE 12983, Second International Conference on Electrical, Electronics, and Information Engineering (EEIE 2023), 129830Z (15 January 2024); https://doi.org/10.1117/12.3017432
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Point clouds

Ranging

Data modeling

3D modeling

Visual process modeling

Tunable filters

Distance measurement

Back to Top