  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.
