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<p>Aiming at the problems of easy loss of GPS positioning signals in outdoor environments and inaccurate map construction and position drift of traditional SLAM algorithms in outdoor scenes, this paper proposes a 3D LiDAR and inertial guidance tightly coupled SLAM algorithm. Firstly, inertial measurement unit (IMU) forward propagation is used to predict the current position, then backward propagation is used to compensate the motion distortion in the LiDAR data, and the point cloud alignment residuals are constructed based on the GICP algorithm, and then the iterative error state Kalman filter (IESKF) algorithm is utilized to complete the fusion of the point cloud residuals and the a priori position obtained from the forward propagation of the IMU to complete the state updating, and then the front-end fusion odometer is constructed. Next, a sparse voxel near-neighbor structure, iVox-based method, is employed to select key frames and construct local maps, leveraging spatial information during frame-map matching. This approach reduces the computational time required for point cloud alignment. Finally, the proposed algorithm is validated in real-world scenarios and on the outdoor open-source dataset KITTI. It is compared against mainstream algorithms, including FAST-LIO2 and LIO-SAM. The results demonstrate that the proposed approach achieves lower cumulative error, higher localization accuracy, and improved visualization with greater robustness in outdoor environments.</p>

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Research on 3D LiDAR outdoor SLAM algorithm based on LiDAR/IMU tight coupling

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月1日

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  • Darong Zhu,
  • Qi Wang,
  • Fangbin Wang,
  • Xue Gong

摘要

Aiming at the problems of easy loss of GPS positioning signals in outdoor environments and inaccurate map construction and position drift of traditional SLAM algorithms in outdoor scenes, this paper proposes a 3D LiDAR and inertial guidance tightly coupled SLAM algorithm. Firstly, inertial measurement unit (IMU) forward propagation is used to predict the current position, then backward propagation is used to compensate the motion distortion in the LiDAR data, and the point cloud alignment residuals are constructed based on the GICP algorithm, and then the iterative error state Kalman filter (IESKF) algorithm is utilized to complete the fusion of the point cloud residuals and the a priori position obtained from the forward propagation of the IMU to complete the state updating, and then the front-end fusion odometer is constructed. Next, a sparse voxel near-neighbor structure, iVox-based method, is employed to select key frames and construct local maps, leveraging spatial information during frame-map matching. This approach reduces the computational time required for point cloud alignment. Finally, the proposed algorithm is validated in real-world scenarios and on the outdoor open-source dataset KITTI. It is compared against mainstream algorithms, including FAST-LIO2 and LIO-SAM. The results demonstrate that the proposed approach achieves lower cumulative error, higher localization accuracy, and improved visualization with greater robustness in outdoor environments.