A Point Cloud Slice-based UAV SLAM Method 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 Method 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 Method 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 Method 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 621022, China

Funds: The 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: 2026-05-10

Available Online: 2026-05-31

Abstract

Abstract

Objective With the continuous development of port intelligence, the demand for digital management in container port areas has increased. In large container yards, Three-Dimensional (3D) reconstruction of the yard environment can be achieved using Unmanned Aerial Vehicle (UAV)-based Simultaneous Localization And Mapping (SLAM). However, container port areas contain many repetitive semantic structures. Traditional semantic matching methods therefore show low efficiency and limited accuracy. In addition, lanes between container yards form large feature-sparse regions during UAV-based 3D reconstruction, which can cause odometry degradation. Repetitive scene features also interfere with loop closure detection. To address these problems, this paper proposes a rapid feature extraction method based on point cloud slicing and further optimizes it according to the structural characteristics of container yards. A UAV point cloud slice-based SLAM method, termed Slice-SLAM, is proposed for high-precision 3D reconstruction of large container port areas. Methods To improve point cloud semantic extraction, a rapid point cloud slicing method is proposed. The principal direction is extracted rapidly, and the point cloud is divided into multiple layers to obtain multi-layer semantic point clouds efficiently. The slicing strategy is further optimized for container yard scenarios. Principal plane extraction is simplified using the gravity direction, and the elevation range of each container layer is obtained adaptively from point cloud density gradient changes. Multi-layer slice point clouds are then constructed. A progressive adaptive Light Detection And Ranging (LiDAR) odometry method based on slice point clouds is developed. Elevation slices are used to identify degenerate scenarios adaptively, and a layer-wise incremental slice matching and fusion strategy is used. This improves the accuracy, efficiency, and stability of LiDAR odometry. In addition, a factor graph optimization method that integrates slice point cloud information is designed. Fusion voting is performed on the matching results of multi-layer slice point clouds to remove erroneous matches and reduce the effect of repetitive structures on loop closure detection. Slice factors are then used to construct factor graph edges, which improves global optimization and supports efficient and stable 3D reconstruction. Results and Discussions The feasibility and effectiveness of the proposed method are verified in CARLA simulation scenarios and real-world tests at a large container port in Wuhan. First, comparisons with three semantic extraction algorithms, namely RANSAC, Region Growth, and 3DG_SEG, demonstrate the efficiency and accuracy of the proposed semantic extraction method. Second, estimated trajectories are compared with those obtained by two open-source LiDAR algorithms, FAST-LIO2 and Faster-LIO, confirming the advantages of the proposed odometry method. Finally, speed and confidence score are compared with those of six algorithms: ICP, NDT, GICP, Fast-GICP, Scan Context+ICP, and Quatro. The loop closure detection module of LIO-SAM is also integrated into FAST-LIO2, and the Scan Context module is integrated into Faster-LIO. The resulting estimated trajectories are compared with those of the proposed method, verifying the effectiveness of the proposed loop closure detection algorithm. The proposed method achieves high 3D reconstruction accuracy and is suitable for practical port operations. Conclusions The proposed method uses an efficient point cloud slicing technique and a multi-layer slice matching mechanism. Points within the same elevation range are defined as a slice point cloud, and the segmentation process is defined as point cloud slicing. This design enables efficient and robust 3D reconstruction in large-scale scenes with repetitive features. First, the LiDAR point cloud is aligned with the positive Z-axis using the gravity direction derived from the Inertial Measurement Unit (IMU). A sliding window records density gradient changes to determine the elevation range of each layer adaptively. This simplifies point cloud slicing and reduces the effects of non-standard containers and ground height variations on semantic extraction. Multi-layer slice information is then integrated into the odometry module to detect degenerate scenarios. Under normal conditions, progressive slice matching is used to initialize pose estimation. In degenerate scenarios, iterative Kalman filtering with increased IMU weighting is used. Finally, the fusion voting mechanism removes outliers from multi-layer slice matching results. The optimal match is used to initialize loop closure for global registration of container-region point clouds, enabling dual-stage loop closure detection and slice factor construction. By integrating slice point cloud information into factor graph optimization, the proposed method unifies point clouds in a common coordinate system and achieves efficient and robust 3D reconstruction.
- Unmanned Aerial Vehicle (UAV),
- Large container port areas,
- Multi-layer slice matching,
- Odometry,
- Loop closure detection

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References(28)

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