Abstract
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.
Abstract (translated)
由于预算限制，室内导航通常采用2D LiDAR而不是3D LiDAR。然而，在同时定位与映射（SLAM）中使用2D LiDAR会经常遇到与运动退化相关的挑战，特别是在几何相似的环境中。为解决这个问题，本文提出了一种专为室内移动机器人设计的 robust、accurate、多传感器融合的2D LiDAR SLAM系统。首先，对原始LiDAR数据进行详细的处理，通过点线提取建立线线约束。利用室内环境的独特特点，建立线线约束以补充其他传感器数据，从而增强系统的整体稳健性和精度。同时，创建了一个紧密耦合的前端，将来自2D LiDAR、IMU和轮径测量的数据进行整合，从而实现实时状态估计。在此基础上，提出了一种基于全局特征点匹配的环闭检测算法。该算法在减轻前端累积误差方面表现出高度的有效性，并最终构建了一个全局一致的地图。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较低的轨迹误差，而且在退化问题中表现出更强的稳健性。
URL
https://arxiv.org/abs/2404.07644
