基于多传感器融合的井下巷道激光建图方法

金属矿山 ›› 2026, Vol. 55 ›› Issue (5): 175-182.

基于多传感器融合的井下巷道激光建图方法

江　松^1,2,3　刘建华¹　崔智翔¹　王　维⁴　徐中华⁵　王　靖^2,3

1. 西安建筑科技大学资源工程学院,陕西西安 710055;2. 中钢集团马鞍山矿山研究总院股份有限公司,安徽马鞍山 243000;
3. 金属矿山开采安全与灾害防治全国重点实验室,安徽马鞍山 243000;4. 中钢集团山东矿业有限公司,山东临沂 277700;
5. 中钢集团山东富全矿业有限公司,山东济宁 272000

出版日期:2026-05-15 发布日期:2026-06-02
作者简介:江　松(1990—),男,教授,博士,博士研究生导师。
基金资助:
国家自然科学基金面上项目(编号:52374136)。

Underground Roadway LiDAR SLAM Method Based on Multi-sensor Fusion

JIANG Song^1,2,3　LIU Jianhua¹　CUI Zhixiang¹　WANG Wei⁴　XU Zhonghua⁵　WANG Jing^2,3

1. School of Resources Engineering,Xi′an University of Architecture and Technology,Xi′an 710055,China;
2. Sinosteel Maanshan General Institute of Mining Research Co. ,Ltd. ,Maanshan 243000,China;
3. State Key Laboratory of Metal Mine Mining Safety and Disaster Prevention and Control,Maanshan 243000,China;
4. Sinosteel Shandong Mining Co. ,Ltd. ,Linyi 277700,China;5. Sinosteel Shandong Fuquan Mining Co. ,Ltd. ,Jining 272000,Chi

Online:2026-05-15 Published:2026-06-02

摘要/Abstract

摘要： 智能化时代的来临为诸多领域带来巨大变革,在矿山行业,无人矿卡的普及与应用推动着行业的现代
化转型,其中即时定位与地图构建(Simultaneous Localization and Mapping,SLAM)技术是解决无人矿卡自主运输和导
航的关键方法。在井下环境中,光照条件不均、巷道特征退化、工作面路况复杂等问题均对传统的激光SLAM 算法提
出挑战。针对以上问题,提出了一种基于多传感器融合的井下巷道激光建图方法(ISC-LIWO)。首先,基于扩展卡尔
曼滤波算法(Extended Kalman Filter,EKF)对轮式里程计和惯性测量单元(Inertial Measurement Unit,IMU)数据进行融
合,实现里程计高精度输出,增强了算法在矿井环境中的鲁棒性。其次,提出了强度扫描上下文(Intensity Scan Context,
ISC)描述符进行回环检测,并基于描述符的旋转不变性通过两阶段的检索策略搜索候选点云描述符,有效提升了
检索速度和位置识别的准确性,减小了井下地图的匹配误差。试验数据来源于济宁市某采场实采和公开数据集,结
果反映出,ISC-LIWO 算法较A-LOAM 绝对轨迹误差的均方根降低33. 96%,较LeGO-LOAM 降低44. 17%,较LIO-SAM
降低10. 02%。试验表明:该算法针对井下巷道的特征退化环境具有更高的鲁棒性,可有效减少位姿漂移和建图重
影,为无人矿卡提供可靠的井下地图和状态估计。

关键词: 无人矿卡　, SLAM 技术　, 传感器融合　, 强度信息　, 回环检测

Abstract: The advent of the intelligent era has brought great changes to many fields. In the coal industry,the popularization
and application of autonomous truck have promoted the modernization of the industry. Simultaneous Localization and Mapping
(SLAM) technology is the key method to solve the autonomous transportation and navigation of autonomous truck. In the
underground environment,the uneven illumination conditions,the degradation of roadway characteristics,and the complex road
conditions of the working face all pose challenges to the traditional laser SLAM algorithm. Aiming at the above problems,a Li-
DAR SLAM based on multi-sensor fusion method for under roadway (ISC-LIWO) is proposed. Firstly,based on the Extended
Kalman Filter (EKF) algorithm,the wheel odometer and Inertial Measurement Unit (IMU) data are fused to achieve high-precision
odometer output,which enhances the robustness of the algorithm in underground mine environment. Secondly,the Intensity
Scan Context (ISC) is proposed as a descriptor for loop closure detection,and the candidate point cloud descriptor is
searched through a two-stage retrieval strategy based on the rotation invariance of the descriptor,which effectively improves the
retrieval speed and the accuracy of position recognition,and reduces the matching error of the downhole map. The experimental
data are obtained from the actual mining and public data sets of a coal mine in Jining City. The results reflect that the square
root of the absolute pose error of the ISC-LIWO algorithm is 33. 96 % lower than that of A-LOAM,44. 17% lower than that of
LeGO-LOAM,and 10. 02 % lower than that of LIO-SAM. Experiments show that the algorithm has higher robustness for the characteristic degradation environment of underground roadway,which can effectively reduce pose drift and map ghosting,and
provide reliable underground map and state estimation for autonomous truck.

Key words: autonomous truck,SLAM,sensor fusion,intensity information,loop closure detection

中图分类号:

TD679

江　松, 刘建华, 崔智翔, 王　维, 徐中华, 王　靖. 基于多传感器融合的井下巷道激光建图方法[J]. 金属矿山, 2026, 55(5): 175-182.

JIANG Song, LIU Jianhua, CUI Zhixiang, WANG Wei, XU Zhonghua, WANG Jing. Underground Roadway LiDAR SLAM Method Based on Multi-sensor Fusion[J]. Metal Mine, 2026, 55(5): 175-182.

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