2DLIW-SLAM：具有实时闭环的2D LiDAR-惯性-轮式里程计
2DLIW-SLAM:2D LiDAR-Inertial-Wheel Odometry with Real-Time Loop Closure
AI总结：
本文提出一种融合2D LiDAR、IMU和轮式里程计的紧耦合室内SLAM系统，利用线-线约束提升鲁棒性，并采用基于全局特征点匹配的闭环检测算法减少累积误差，相比Cartographer具有更低轨迹误差和更强抗退化能力。
摘要：
由于预算限制，室内导航通常采用2D LiDAR而非3D LiDAR。然而，在SLAM（同时定位与建图）中使用2D LiDAR经常面临运动退化的挑战，尤其是在几何相似的环境中。为了解决这个问题，本文提出了一种专为室内移动机器人设计的鲁棒、准确且多传感器融合的2D LiDAR SLAM系统。首先，原始LiDAR数据通过点和线提取进行精细处理。利用室内环境的显著特征，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，创建了一个紧耦合前端，整合来自2D LiDAR、IMU和轮式里程计的数据，从而实现实时状态估计。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。该算法在缓解前端累积误差方面非常有效，最终构建出全局一致的地图。实验结果表明，我们的系统完全满足实时要求。与Cartographer相比，我们的系统不仅表现出更低的轨迹误差，而且展现出更强的鲁棒性，特别是在退化问题上。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
