2DLIW-SLAM:2D LiDAR-Inertial-Wheel Odometry with Real-Time Loop Closure

TL;DR

This work targets robust indoor localization with 2D LiDAR by addressing motion degeneracy through a tightly coupled front-end that fuses 2D LiDAR, IMU, and wheel odometry, enhanced by line/point feature extraction and ground constraints. It introduces a global feature-point descriptor-based loop-closure mechanism and a pose-graph optimization backbone, combined with a 2D probability grid map to produce a globally consistent map in real time. Key contributions include a novel point-line extraction and line-to-line alignment framework, IMU preintegration tailored to this setup, and a ground-constrained optimization that reduces to $3$DoF while maintaining full 6DoF estimates in theory. Experimental results on OpenLORIS-Scenes show improved trajectory accuracy and robustness over baselines such as Cartographer, with real-time performance even in degeneracy-prone environments, highlighting its practicality for low-cost indoor robotics.

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.

Figures (8)

• Figure 1: The system structure of 2DLIW-SLAM. The Initialization and Front-end Odometry will be elaborated in detail in Section IV. The Global Optimization and Mapping will be discussed comprehensively in Section V.
• Figure 2: Illustration of line feature extraction. All points are processed using $A^TP=0$ to obtain the linear equation, and the line segment range is defined by $(P_{s},P_{e})$.
• Figure 3: Illustration of line feature matching optimization between the current frame and the reference frame. The distances between $(P^{'}_{s},P^{'}_{e})$ and line in the reference frame are utilized as observational constraints to optimize the pose of the current frame.
• Figure 4: Illustration of loop closure matching. All points are corner feature points, and similarity matching is performed by comparing the distance and angle features between these corner points.
• Figure 5: The performance of 2DLIW in home scene. 2DLIW can address the drift error in wheel odometry and achieve effective localization and mapping.