Zhu Darong, Wang Qi, Wang Fangbin, Gong Xue
School of Mechanical and Electrical Engineering, Anhui Jianzhu University, HeFei, 230601, China.
Key Laboratory of Construction Machinery Fault Diagnosis and Early Warning, Technology, Anhui Jianzhu University, HeFei, 230601, China.
Sci Rep. 2025 Apr 1;15(1):11175. doi: 10.1038/s41598-025-95730-3.
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.
针对室外环境中GPS定位信号易丢失以及传统SLAM算法在室外场景中地图构建不准确和位置漂移的问题，本文提出了一种三维激光雷达与惯性导航紧密耦合的SLAM算法。首先，利用惯性测量单元（IMU）前向传播预测当前位置，然后通过后向传播补偿激光雷达数据中的运动畸变，并基于GICP算法构建点云对齐残差，接着利用迭代误差状态卡尔曼滤波器（IESKF）算法完成点云残差与IMU前向传播得到的先验位置的融合以完成状态更新，进而构建前端融合里程计。其次，采用基于稀疏体素近邻结构iVox的方法来选择关键帧并构建局部地图，在帧-地图匹配过程中利用空间信息。这种方法减少了点云对齐所需的计算时间。最后，在实际场景和室外开源数据集KITTI上对所提算法进行了验证。将其与包括FAST-LIO2和LIO-SAM在内的主流算法进行了比较。结果表明，所提方法在室外环境中实现了更低累积误差、更高定位精度以及更好的可视化效果和更强的鲁棒性。
