Dynamic Planning Stereo Matching Study Based on Improved SGBM Algorithm

This paper describes an improved stereo matching technique aimed at achieving high-precision non-contact measurements of stationary objects. The method is based on the SGBM (Semi-Global Block Matching) algorithm, which utilizes a binocular camera to calibrate the camera to the object and stereo-correct the image. The SGBM algorithm, based on the Census transform, is employed to match the stereo-corrected binocular images, generate parallax maps, and determine the 3D coordinate points. By utilizing parallax data, the depth information of the plane is calculated, and a dense point cloud of the image is created. The relative error of the binocular vision system developed in this paper ranges from 0.54% to 1.30%, confirming the algorithm's excellent accuracy and stability, and providing precise data for applications that require high accuracy.

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Publication Details

Published
2024-08-16
DOI
https://doi.org/10.1109/ccet62233.2024.10837844
Citations
2
Primary Topic
Advanced Vision and Imaging
Type
article
Field-Weighted Citation Impact
0.50
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Dynamic Planning Stereo Matching Study Based on Improved SGBM Algorithm

Yimou Li, Wei Wang, Lei Wang, Guo-Ping Fu et al.
2 citations
Advanced Vision and Imaging
0.50
article

Dynamic Planning Stereo Matching Study Based on Improved SGBM Algorithm

Yimou Li, Wei Wang, Lei Wang, Guo-Ping Fu, Yang Gao
article en
2 citations

Abstract

This paper describes an improved stereo matching technique aimed at achieving high-precision non-contact measurements of stationary objects. The method is based on the SGBM (Semi-Global Block Matching) algorithm, which utilizes a binocular camera to calibrate the camera to the object and stereo-correct the image. The SGBM algorithm, based on the Census transform, is employed to match the stereo-corrected binocular images, generate parallax maps, and determine the 3D coordinate points. By utilizing parallax data, the depth information of the plane is calculated, and a dense point cloud of the image is created. The relative error of the binocular vision system developed in this paper ranges from 0.54% to 1.30%, confirming the algorithm's excellent accuracy and stability, and providing precise data for applications that require high accuracy.

Tianjin Normal University (CN), China Mobile (China) (CN), China Railway Construction Corporation (China) (CN)
Openalex Percentile: Top 32%
Advanced Vision and Imaging
0.50
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