对多式联运数据的研究增强了SLAM算法,用于全球地底煤矿的地图绘制
Mingrui Hao1, Jie Ren2, Xiaodong Ji3
1China University of Mining and Technology of Beijing, Beijing, 100083, China. haomingrui@163.com.
Scientific reports
|October 23, 2025
概括
本研究介绍了煤矿的增强同步定位和映射 (SLAM) 方法,提高了自动驾驶的全球映射精度. 多式联网方法融合了LiDAR,惯性和车轮数据,用于精确的位置估计和地铁道路的地图建立.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 地理空间工程是什么?
- 自主系统 自主系统
背景情况:
- 在煤矿中,全球地图的不准确性阻碍了自动驾驶系统的发展.
- 现有的SLAM方法在与煤矿等地下环境的独特挑战作斗争.
研究的目的:
- 开发一种使用多式联络数据,对煤矿进行增强的SLAM全球绘图方法.
- 改进在地下公路上自动驾驶的姿势估计和地图建立.
主要方法:
- 一个前端融合了LiDAR,惯性测量单元 (IMU) 和使用代错误状态卡尔曼波器的轮距测量数据.
- 在多因素姿势图中采用全球位置约束和循环闭合检测的球形目标的后端.
- 集成GNSS/UWB信号以进行基于点的地图校正.
主要成果:
- 拟议的方法通过传感器融合有效地减轻了LiDAR退化.
- 在模拟和现实世界煤矿环境中实现了准确的姿势估计和地图建立.
- 在大型地下绘图中证明了可行性和性能.
结论:
- 增强的多式联运SLAM方法显著提高了煤矿全球绘图的准确性.
- 该方法为地下环境中的自动驾驶系统提供了可靠的基础.
- 该方法在地下采矿操作中具有很强的实际应用潜力.
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