Positioning of Mine Loaders Based on UWB and IMU

IF 3.6 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Access Pub Date : 2025-09-03 DOI:10.1109/ACCESS.2025.3605669

Zhonghua Zhu;Ying Zhao;Wenxuan He;Zhang Peng;Haiying Fu;Yueyue Wu

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引用次数: 0

Abstract

Aiming at the difficulty of precise positioning of load-haul-dump (LHD) machines in complex mine environments, due to the constraints of satellite-denied environments on single positioning technologies and the severe impact of non-line-of-sight (NLOS) conditions, this paper proposes a positioning method suitable for LHD machines in mine environments. This method integrates ultra-wideband (UWB) and inertial measurement unit (IMU) measurement information through an improved error state Kalman filter (ESKF) algorithm. A Dynamic Covariance Robust Method (DCRM) is proposed for ESKF to improve it by constructing a residual covariance matrix and dynamically adjusting the measurement noise covariance matrix through covariance matching technology, achieving adaptive suppression of NLOS error. The experimental results demonstrate that in complex environments, compared to using UWB positioning alone, the improved ESKF positioning algorithm effectively reduces the ultra-wideband positioning error under NLOS conditions by providing prior information of the positioning target. Compared to LS-Taylor, the Extended Kalman Filter algorithm and the Motion Inertia Estimation Adaptive Kalman Filtering, the positioning accuracy is improved by 61%, 50% and 31%, respectively.

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基于超宽带和IMU的矿用装载机定位

针对复杂矿山环境下卸荷机精确定位困难、无星环境对单一定位技术的制约以及非视距条件的严重影响，提出了一种适用于矿山环境下卸荷机的定位方法。该方法通过改进的误差状态卡尔曼滤波（ESKF）算法集成超宽带（UWB）和惯性测量单元（IMU）的测量信息。针对ESKF，提出了一种动态协方差鲁棒方法（DCRM），通过构造残差协方差矩阵，并通过协方差匹配技术动态调整测量噪声协方差矩阵，实现对NLOS误差的自适应抑制。实验结果表明，在复杂环境下，与单独使用超宽带定位相比，改进的ESKF定位算法通过提供定位目标的先验信息，有效降低了NLOS条件下的超宽带定位误差。与LS-Taylor、扩展卡尔曼滤波和运动惯量估计自适应卡尔曼滤波相比，定位精度分别提高了61%、50%和31%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Access COMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC

CiteScore

9.80

自引率

7.70%

发文量

6673

审稿时长

6 weeks

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