Joint Event Density and Curvature Within Spatio-Temporal Neighborhoods-Based Event Camera Noise Reduction and Pose Estimation Method for Underground Coal Mine

Wenjuan Yang, Jie Jiang, Xuhui Zhang (), Yang Ji, Le Zhu, Yanbin Xie and Zhiteng Ren
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Wenjuan Yang: School of Mechanical Engineering, Xi’an University of Science and Technology, No. 58, Mid-Yanta Road, Xi’an 710054, China
Jie Jiang: School of Mechanical Engineering, Xi’an University of Science and Technology, No. 58, Mid-Yanta Road, Xi’an 710054, China
Xuhui Zhang: School of Mechanical Engineering, Xi’an University of Science and Technology, No. 58, Mid-Yanta Road, Xi’an 710054, China
Yang Ji: School of Mechanical Engineering, Xi’an University of Science and Technology, No. 58, Mid-Yanta Road, Xi’an 710054, China
Le Zhu: School of Mechanical Engineering, Xi’an University of Science and Technology, No. 58, Mid-Yanta Road, Xi’an 710054, China
Yanbin Xie: School of Mechanical Engineering, Xi’an University of Science and Technology, No. 58, Mid-Yanta Road, Xi’an 710054, China
Zhiteng Ren: School of Mechanical Engineering, Xi’an University of Science and Technology, No. 58, Mid-Yanta Road, Xi’an 710054, China

Mathematics, 2025, vol. 13, issue 7, 1-31

Abstract: Aiming at the problems of poor image quality of traditional cameras and serious noise interference of event cameras under complex lighting conditions in coal mines, an event denoising algorithm fusing spatio-temporal information and a method of denoising event target pose estimation is proposed. The denoising algorithm constructs a spherical spatio-temporal neighborhood to enhance the spatio-temporal denseness and continuity of valid events, and combines event density and curvature to achieve event stream denoising. The attitude estimation framework adopts the noise reduction event and global optimal perspective-n-line (OPNL) methods to obtain the initial target attitude, and then establishes the event line correlation model through the robust estimation, and achieves the attitude tracking by minimizing the event line distance. The experimental results show that compared with the existing methods, the noise reduction algorithm proposed in this paper has a noise reduction rate of more than 99.26% on purely noisy data, and the event structure ratio (ESR) is improved by 47% and 5% on DVSNoise20 dataset and coal mine data, respectively. The maximum absolute trajectory error of the localization method is 2.365 cm, and the mean square error is reduced by 2.263% compared with the unfiltered event localization method.

Keywords: event camera; denoise; spatio-temporal neighborhood; event curvature; OPNL; robust estimation; pose estimation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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