Binocular vision localization method for roadheader based on point-line features in coal mines

Zhang, C, Zhang, X, Zhang, G ORCID: 0000-0002-2351-2661, Yang, W, Du, Y, Wan, J, Lei, M and Dong, Z (2025) Binocular vision localization method for roadheader based on point-line features in coal mines. Measurement Science and Technology, 36 (8). 086307-086307. ISSN 0957-0233

Text Binocular vision localization method for roadheader based on point-line features in coal mines.pdf - Accepted Version Access Restricted until 5 August 2026. Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (1MB)

Abstract

Accurate and robust localization of roadheaders is essential for intelligent control in underground coal mining, where visual perception systems face significant challenges such as low illumination, abrupt lighting changes, dust interference, and complex backgrounds. This paper proposes a stereo vision-based localization method for roadheaders that integrates point and line features to enhance pose estimation accuracy and robustness under complex lighting conditions. To address image degradation caused by the coexistence of low-light and overexposed regions, an adaptive multi-region image enhancement approach is developed. The method dynamically segments the image into high-, medium-, and low-illumination regions based on average brightness and standard deviation, applying region-specific enhancement strategies such as gamma correction, CLAHE, and contrast adjustment. A gradient-based weighted fusion mechanism and bilateral filtering are introduced to improve image quality and feature extraction stability. Line features are extracted using EDLines and represented via Plücker coordinates. A joint point-line optimization model with a dynamic weighting scheme is constructed to improve localization robustness. Experiments on public datasets including EuRoc MAV and KITTI demonstrate that the proposed method achieves an absolute pose error (APE) of 0.10 m and root mean square error (RMSE) of 0.13 m, outperforming existing methods such as PL-SLAM and PL-EDLines-SLAM. Further validation in a dust- and glare-simulated underground environment yields an APE of 0.15 m and RMSE of 0.17 m, confirming the method’s environmental adaptability and practical applicability. This study provides a reliable visual localization solution for underground roadheaders, supporting downstream tasks such as path planning, tracking control, and autonomous operation.

Item Type:	Article
Additional Information:	This is an author-created, un-copyedited version of an article accepted for publication/published in Measurement Science and Technology . IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at https://doi.org/10.1088/1361-6501/adf503
Uncontrolled Keywords:	40 Engineering; 02 Physical Sciences; 09 Engineering; Optics; 40 Engineering; 51 Physical sciences
Subjects:	Q Science > Q Science (General) T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Engineering
Publisher:	IOP Publishing
Date of acceptance:	28 July 2025
Date Deposited:	15 Oct 2025 09:06
Last Modified:	15 Oct 2025 09:15
DOI or ID number:	10.1088/1361-6501/adf503
URI:	https://researchonline.ljmu.ac.uk/id/eprint/27338

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