Due to budgetary constraints, indoor navigation typically employs 2D LiDAR rather than 3D LiDAR. However, the utilization of 2D LiDAR in Simultaneous Localization And Mapping (SLAM) frequently encounters challenges related to motion degeneracy, particularly in geometrically similar environments. To address this problem, this paper proposes a robust, accurate, and multi-sensor-fused 2D LiDAR SLAM system specifically designed for indoor mobile robots. To commence, the original LiDAR data undergoes meticulous processing through point and line extraction. Leveraging the distinctive characteristics of indoor environments, line-line constraints are established to complement other sensor data effectively, thereby augmenting the overall robustness and precision of the system. Concurrently, a tightly-coupled front-end is created, integrating data from the 2D LiDAR, IMU, and wheel odometry, thus enabling real-time state estimation. Building upon this solid foundation, a novel global feature point matching-based loop closure detection algorithm is proposed. This algorithm proves highly effective in mitigating front-end accumulated errors and ultimately constructs a globally consistent map. The experimental results indicate that our system fully meets real-time requirements. When compared to Cartographer, our system not only exhibits lower trajectory errors but also demonstrates stronger robustness, particularly in degeneracy problem.

翻译：由于预算限制，室内导航通常采用二维激光雷达而非三维激光雷达。然而，在同时定位与地图构建（SLAM）中，二维激光雷达的应用常面临运动退化问题，尤其在几何相似环境中尤为突出。为解决该问题，本文提出了一种专为室内移动机器人设计的鲁棒、精确的多传感器融合二维激光雷达SLAM系统。首先，原始激光雷达数据通过点与线提取进行精细处理。利用室内环境的独特特征，建立线-线约束以有效补充其他传感器数据，从而增强系统的整体鲁棒性和精度。同时，构建了紧耦合前端，融合二维激光雷达、惯性测量单元（IMU）和轮式里程计数据，实现实时状态估计。在此坚实基础上，提出了一种基于全局特征点匹配的闭环检测算法，该算法能有效抑制前端累积误差，最终构建全局一致的地图。实验结果表明，系统完全满足实时性要求。与Cartographer相比，本系统不仅轨迹误差更低，而且在退化问题中展现出更强的鲁棒性。