A Point Cloud Slice-based UAV SLAM for 3D Reconstruction of Large Container Port Areas

HU Zhaozheng; ZUO Zhihang; XU Cong; TAO Qianwen; LIU Chao; MENG Jie

doi:10.11999/JEIT251112

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HU Zhaozheng, ZUO Zhihang, XU Cong, TAO Qianwen, LIU Chao, MENG Jie. A Point Cloud Slice-based UAV SLAM for 3D Reconstruction of Large Container Port Areas[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251112

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HU Zhaozheng, ZUO Zhihang, XU Cong, TAO Qianwen, LIU Chao, MENG Jie. A Point Cloud Slice-based UAV SLAM for 3D Reconstruction of Large Container Port Areas[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251112

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A Point Cloud Slice-based UAV SLAM for 3D Reconstruction of Large Container Port Areas

doi: 10.11999/JEIT251112 cstr: 32379.14.JEIT251112

HU Zhaozheng¹,
ZUO Zhihang^{1, 2},
XU Cong¹,
TAO Qianwen³,
LIU Chao⁴,
MENG Jie^{1
,
,}

1.
Intelligent Transport System Research Center, Wuhan University of Technology, Wuhan 430070, China
2.
School of Information Engineering, Wuhan University of Technology, Wuhan 430063, China
3.
College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China
4.
Institute of Mechanical Manufacturing Technology, China Academy of Engineering Physics, Mianyang 621900, China

Funds: National Natural Science Foundation of China (52472453), Wuhan Science and Technology Achievement Transformation Project (2024030803010173)

Received Date: 2025-10-22
Accepted Date: 2026-05-12
Rev Recd Date: 2026-04-29

Available Online: 2026-05-31

Abstract

Abstract

Objective With the continuous advancement of port intelligence, the demand for digital management in container port areas is increasingly growing. In large container yard scenarios, 3D reconstruction of the yard environment can be achieved by utilizing drone Simultaneous Localization and Mapping (SLAM) technology. However, container port areas contain an abundance of repetitive semantic structural information, where traditional semantic matching methods suffer from low efficiency and poor accuracy. Furthermore, during the 3D reconstruction process conducted by drones over container port areas, the lanes between yards present large feature-sparse regions, which can easily lead to odometry degradation. Additionally, the extensive presence of repetitive scene features also interferes with loop closure detection. To address these issues, this paper proposes a slicing method for rapid feature extraction, which is further optimized based on the characteristics of the container yard scenario. A UAV point cloud slicing SLAM method tailored for large-scale container port 3D reconstruction is introduced, enabling high-precision 3D reconstruction. Methods To address point cloud semantic extraction, this paper proposes a point cloud slicing method for rapid feature extraction, which quickly extracts the principal direction and divides the point cloud into multiple layers to efficiently obtain multi-layer semantic point clouds. The slicing method is further optimized based on the characteristics of the container yard scenario: the principal plane extraction is simplified using the direction of gravity, and the elevation range of each container layer is adaptively obtained through point cloud gradient changes to construct multi-layer sliced point clouds. Subsequently, a progressive adaptive LiDAR odometry based on sliced point clouds is constructed, which adaptively identifies degraded scenarios using elevation slices and employs an incremental iterative strategy for inter-layer slice fusion matching, thereby improving the accuracy, efficiency, and stability of the LiDAR odometry. In addition, a factor graph optimization method that fuses information from sliced point clouds is designed. By performing fusion voting on the matching results of multi-layer sliced point clouds, erroneous results are filtered out and the impact of repetitive structures on loop closure detection is reduced; slice factors are then used to construct factor graph edges, enhancing global optimization and achieving efficient and stable 3D reconstruction. Results and Discussions The feasibility and effectiveness of the proposed method are verified through testing in Carla simulations and real-world scenarios at a large container port in Wuhan. Results are as follows: First, through comparative analysis with three algorithms—RANSAC, Region Growth, and 3DG_SEG—the efficiency and accuracy of the proposed semantic extraction algorithm are demonstrated. Furthermore, by comparing mapping trajectories with two renowned open-source LiDAR algorithms, FAST-LIO2 and Faster-LIO, the superiority of the proposed odometry method is proven. Finally, comparisons of speed and confidence level are conducted with six algorithms: ICP, NDT, GICP, Fast_GICP, Scan Context+ICP, and Quatro. Simultaneously, the loop closure detection module from LIO-SAM is integrated into FAST-LIO2, and the Scan Context module into Faster-LIO. The mapping trajectories are then compared with that of the proposed algorithm, validating the effectiveness of the proposed loop closure detection algorithm. The proposed method achieves high 3D reconstruction accuracy; therefore, it is suitable for practical application in operational processes. Conclusions The proposed method uses an efficient point cloud slicing technique and a multi-layer slice matching mechanism. Points within the same elevation range form a slice point cloud (Slice), and the segmentation process is called slice generation. This enables efficient and robust 3D reconstruction in large-scale scenes with repetitive features.First, the LiDAR point cloud is aligned to the Z-axis using IMU-derived gravity direction. A sliding window records density gradient changes to adaptively determine each layer’s elevation range. This simplifies slicing and reduces the impact of non-standard containers or ground height variations on semantic extraction.Multi-layer slice data are then integrated into the odometry module to detect degenerate scenarios. Under normal conditions, progressive slice matching initializes pose estimation; otherwise, IMU-based iterative Kalman filtering is used.Finally, fusion voting removes outliers from multi-layer slice matching results. The best match initializes loop closure for global container point cloud registration, enabling dual-stage loop closure detection and slice factor construction. Integrating slice point cloud information into factor graph optimization unifies coordinates and achieves efficient, robust 3D reconstruction.
- UAV,
- Large Container Port Areas,
- Multi-layer Slice Matching,
- Odometry,
- Loop Closure Detection

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References(28)

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