GLIO: Tightly-Coupled GNSS/LiDAR/IMU Integration for Continuous and Drift-Free State Estimation of Intelligent Vehicles in Urban Areas

Liu, Xikun; Wen, Weisong; Hsu, Li-Ta

doi:10.1109/tiv.2023.3323648

Cited by 6 publications

(2 citation statements)

References 40 publications

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“…Noteworthy examples include R2LIVE [17] and R3LIVE [18] proposed by Zhong et al, and LVIO-SAM [19] proposed by Zhang et al [20] have improved traditional point cloud registration and loop-detection methods, proposing a new scheme for multi-sensor SLAM systems. GLIO [21] tightly couples GNSS, LiDAR, and IMU integration for continuous and drift-free state estimation of intelligent vehicles in urban areas. Song et al [22] utilized a dual neural network and a square-root cubature Kalman filter to mitigate sensor noise and varied maneuvers.…”

Section: Related Workmentioning

confidence: 99%

2DLIW-SLAM:2D LiDAR-inertial-wheel odometry with real-time loop closure

Zhang,

Peng,

Zeng

et al. 2024

Meas. Sci. Technol.

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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. We opensource our methods here: https://github.com/LittleDang/2DLIW-SLAM.Keywords: SLAM,2D LiDAR, IMU, wheel odometry, tightly coupled odometry, loopdetection, optimization.

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Section: Related Workmentioning

confidence: 99%

2DLIW-SLAM:2D LiDAR-inertial-wheel odometry with real-time loop closure

Zhang,

Peng,

Zeng

et al. 2024

Meas. Sci. Technol.

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“…Liu et al [21] proposed a method for tight integration of LiDAR-inertial navigation system odometry based on the concept of complementary filtering. GLIO [22] introduced an integrated estimator for GNSS/LiDAR/IMU that employs factor graph optimization (FGO) for tightly fusing GNSS pseudoranges, Doppler, LiDAR, and IMU measurements. HDL-Graph-SLAM [23] amalgamates various inputs, including IMU, LiDAR sensors, and global positioning system, achieving minimal resource consumption and high accuracy.…”

Section: Related Workmentioning

confidence: 99%

MF-LIO: integrating multi-feature LiDAR inertial odometry with FPFH loop closure in SLAM

Song,

Shi,

et al. 2024

Meas. Sci. Technol.

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Simultaneous Localization and Mapping (SLAM) is a pivotal technology in autonomous vehicle navigation and a significant research area in robotics. Addressing the inaccuracies in point cloud registration of traditional LiDAR SLAM, which lead to localization and mapping errors, we propose a novel LiDAR Inertial Odometry approach integrating IMU and a multi-feature joint registration strategy. Initially, we introduce an innovative ground segmentation method and feature categorization strategy, enhancing ground detection mechanisms and optimizing the feature extraction process. Subsequently, our multi-feature joint registration method computes the pose transformations between current frames and the local map. Finally, we employ a global registration method based on Fast Point Feature Histograms (FPFH) feature descriptors for coarse alignment, providing initial estimates for the Generalized Iterative Closest Point (GICP) algorithm, thus efficiently and accurately mitigating cumulative errors. Extensive evaluations on the KITTI dataset and real-world campus environment demonstrate that our approach significantly surpasses existing advanced LiDAR SLAM solutions, achieving over a 24% improvement in pose estimation accuracy.

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