煤矿井下环境具有空间非结构化、相对封闭狭窄、光照不均等特点，此类场景给煤矿井下移动机器人的高精度地图构建和自主定位技术带来新的挑战。由于煤矿井下无法接收全球定位系统GPS(Global Positioning System)信号，导致传统定位方法失效，且井下光照不均会影响建图效果，难以直接应用室内外场景下普遍使用的地图构建和定位技术，亟需构建能够适应煤矿井下特殊环境下的移动机器人建图和自主定位系统方案。为此，本文引入同时定位与建图SLAM (Simultaneous Localization and Mapping)技术，构建三维激光雷达3D LiDAR和九轴姿态传感器IMU (Inertial Measurement Unit)紧耦合环境感知系统。主要研究内容如下：

         针对煤矿井下封闭复杂的特殊环境，分析煤矿井下移动机器人的工作环境与功能需求，研制一台煤矿井下移动机器人。该机器人的硬件系统结构分为环境感知层、动作执行层和规划决策层。软件系统包括环境探测模块、建图模块、自主定位模块、运动控制模块、通讯模块、上位机模块和电源模块，该机器人的研制为本文的建图与自主定位算法研究奠定基础。

         针对煤矿巷道结构重复、内壁特征点少易造成点云误匹配、建图场景退化的问题，提出一种基于SC-LIO-SAM(Lidar Inertial Odometry via Smoothing and Mapping with Scan Context)的煤矿井下移动机器人的建图算法。引入Scan Context进行回环检测，不再依赖传统欧式距离，融合历史帧筛选策略，建立全局数据关联，通过构建局部地图和全局位姿图优化实现建图的一致性。实验结果表明，采用SC-LIO-SAM算法建图的终点距离误差相比ALOAM、LeGO-LOAM、LIO-SAM分别降低了24.4%、27.5%、16.6%，提升建图精度，保证全局一致性。

         针对移动机器人在煤矿井下封闭式复杂环境中的自主定位问题，提出基于3D LiDAR-IMU紧耦合的TIL-SAM (Tightly-Coupled Lidar-Inertial Smoothing and Mapping)自主定位算法。建立机器人SLAM因子图模型，构建IMU预积分约束因子、3D LiDAR相对位姿约束因子以及回环检测约束因子，将三种约束因子植入因子图优化模型中实现全局位姿优化。利用3D LiDAR扫描匹配得到更为精准的位姿，修正IMU的位姿，进一步提高机器人位姿估计的精度，提高自主定位的鲁棒性。实验结果表明，TIL-SAM的均方根误差相比于ALOAM、LeGO-LOAM以及LIO-SAM分别降低了3.4%、18.6%以及5.7%，并且在最大值、最小值和与标准差上均有较大的提升，证明TIL-SAM具有更高的精度。

         以研制的煤矿井下移动机器人系统为平台，在西安科技大学煤矿主体实训基地的模拟煤矿巷道中进行现场实验，验证本文所提出的基于SC-LIO-SAM 的煤矿井下移动机器人的建图算法、基于3D LiDAR-IMU紧耦合的TIL-SAM自主定位算法的先进性和可靠性。实验结果表明，本文提出的理论和方法可为煤矿井下移动机器人建图与自主定位技术提供理论支撑，同时能够为其他类似封闭复杂环境下的机器人的地图构建与自主定位研究提供参考依据。

     The underground environment of coal mines features spatial non-structuring, relatively closed and narrow spaces, and uneven lighting. Such scenarios pose new challenges for the high-precision mapping and autonomous positioning technology of mobile robots in coal mines. Due to the inability to receive Global Positioning System(GPS) signals in underground coal mines, the traditional positioning methods fail, and the uneven lighting in the underground will affect the mapping effect, so it is difficult to directly apply the map construction and positioning technology commonly used in indoor and outdoor scenes.To this end, the Simultaneous Localization and Mapping (SLAM) technology is introduced to construct a 3D LiDAR and a nine-axis Inertial Measurement Unit (IMU) tightly coupled environment perception system. The main research contents are as follows:

        To address the enclosed and complex environment of underground coal mines, this study analyzes the operational conditions and functional requirements of mobile robots in such settings and develops a dedicated underground coal mine mobile robot. The hardware system structure of the robot is divided into an environment perception layer, an action execution layer and a planning and decision-making layer. The software system comprises multiple modules, including environment detection, mapping, autonomous localization, motion control, communication, host computer interaction, and power management. The development of this robot provides a foundational platform for the research on mapping and autonomous localization algorithms presented in this paper.

       The repetitive structures and sparse feature points of coal mine roadways often lead to point cloud mismatching and mapping degradation. To solve these problems, we developed SC-LIO-SAM (Lidar Inertial Odometry via Smoothing and Mapping with Scan Context), a lidar-inertial odometry algorithm for underground mobile robot mapping. The method introduces Scan Context for loop closure detection, eliminating reliance on traditional Euclidean distance metrics. By integrating a historical keyframe selection strategy, it establishes global data association and ensures mapping consistency through local submap construction and global pose graph optimization. Experimental results demonstrated that the proposed SC-LIO-SAM algorithm reduces the endpoint distance error by 24.4%, 27.5%, and 16.6% compared to ALOAM, LeGO-LOAM, and LIO-SAM, respectively. This improvement enhances mapping accuracy and guarantees global consistency in challenging underground coal mine environments.

       To address the autonomous localization challenge for mobile robots in the enclosed and complex underground coal mine environment, this paper proposes a tightly-coupled 3D LiDAR-IMU fusion algorithm named Tightly-Coupled Lidar-Inertial Smoothing and Mapping (TIL-SAM). The approach establishes a factor graph model for robot SLAM, incorporating three key constraint factors: IMU pre-integration constraints, 3D LiDAR relative pose constraints, and loop closure detection constraints. These constraints are integrated into a unified factor graph optimization framework to achieve global pose optimization. The algorithm leverages 3D LiDAR scan matching to obtain more accurate pose estimates, which are then used to correct IMU-derived poses, thereby significantly improving both the precision and robustness of robot localization. Experimental results demonstrated that TIL-SAM reduces the root mean square error by 3.4%, 18.6%, and 5.7% compared to ALOAM, LeGO-LOAM, and LIO-SAM respectively. Furthermore, substantial improvements are observed in maximum error, minimum error, and standard deviation metrics, confirming TIL-SAM's superior localization accuracy in challenging underground environments.

        This study conducted field experiments at the simulated coal mine tunnel facility in Xi'an University of Science and Technology's Coal Mine Training Base, utilizing the developed underground coal mine mobile robot system as the experimental platform. The experiments were designed to validate the advancement and reliability of both the proposed SC-LIO-SAM based mapping algorithm and the tightly-coupled 3D LiDAR-IMU TIL-SAM autonomous localization algorithm for underground coal mine mobile robots. The experimental results demonstrated that the theoretical framework and methodologies proposed in this study can provide substantial theoretical support for mapping and autonomous localization technologies of underground coal mine mobile robots. Furthermore, the findings offer valuable reference for research on mapping and autonomous localization of robots operating in other similarly enclosed and complex environments.

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