面向大型集装箱港区三维重建的无人机点云切片同步定位与建图

胡钊政; 左志航; 许聪; 陶倩文; 刘超; 孟杰

doi:10.11999/JEIT251112

面向大型集装箱港区三维重建的无人机点云切片同步定位与建图

doi: 10.11999/JEIT251112 cstr: 32379.14.JEIT251112

胡钊政¹,
左志航^{1, 2},
许聪¹,
陶倩文³,
刘超⁴,
孟杰^1, ,

1.
武汉理工大学智能交通系统研究中心武汉 430070
2.
武汉理工大学信息工程学院武汉 430063
3.
南京林业大学汽车与交通工程学院南京 210037
4.
中国工程物理研究院机械制造工艺研究所绵阳 621022

基金项目: 国家自然科学基金(52472453)，武汉市科技局科技成果转化项目(2024030803010173)

详细信息

作者简介:
胡钊政：男，教授，研究方向为3D计算机视觉、智能网联汽车、机器人定位与导航、智能车路协同

左志航：男，硕士生，研究方向为激光SLAM定位、移动机器人等

许聪：男，博士生，研究方向为机器人定位与感知、BEV多模态大模型等

陶倩文：女，副教授，研究方向为3D计算机视觉、机器人定位与导航等

刘超：男，助理研究员，研究方向为特种机器人，机器人导航定位等

孟杰：男，副研究员，研究方向为移动机器人、自动驾驶、自主导航定位等

通讯作者:
孟杰　mengjie09@whut.edu.cn

中图分类号: TN249; TP242
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- HTML全文浏览量: 28
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- 被引次数: 0
出版历程
- 收稿日期: 2025-10-22
- 修回日期: 2026-05-10
- 录用日期: 2026-05-12
- 网络出版日期: 2026-05-31

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

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)

摘要

摘要: 在大型集装箱港区堆场环境中，大量重复性语义特征以及部分退化场景导致无人机在大面积场景下难以实现高效可靠的三维重建。为此，该文提出一种基于无人机点云切片同步定位与建图(SLAM)的大型集装箱港区三维重建方法，基于地面约束与点云密度梯度变化自适应提取多层切片点云，高效精准获取堆场语义信息，并基于同层切片点云匹配有效改善了里程计以及回环检测的精度。首先提出一种面向快速特征提取的点云切片方法，通过快速提取主方向并将点云划分为多层切片，高效获取多层语义点云。其次基于集装箱堆场场景特点进一步优化切片提取方法，基于重力方向简化场景主平面提取过程，并通过点云梯度变化自适应获取各层集装箱所在高程区间，构建多层切片点云。然后构建基于切片点云的递进式自适应激光里程计，利用高程切片自适应判别退化场景，同时在层间利用增量式迭代策略实现切片融合匹配，从而提升激光里程计精度、效率与稳定性。此外，设计融合激光点云切片信息的因子图优化方法，通过对多层切片点云匹配结果进行融合投票，筛除错误结果并减少大量重复结构对回环检测的影响，并利用切片因子来构建因子图边，从而提升全局优化水平，实现高效稳定的三维重建。最后，通过Carla仿真场景以及武汉某大型集装箱港区的实际场景测试，证实了该方法的可行性和有效性。
- 无人机 /
- 大型集装箱港区 /
- 多层切片匹配 /
- 里程计 /
- 回环检测
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

HTML全文

图 1 Slice-SLAM系统框图

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图 2 切片原理示意图

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图 3 集装箱堆场切片点云提取

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图 4 点云密度梯度结构示例

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图 5 里程计示意图

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图 6 回环检测示意图

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图 7 因子图示意图

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图 8 集装箱港区仿真场景

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图 9 真实实验环境与平台

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图 10 仿真场景下各算法轨迹误差对比

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图 11 语义提取结果

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图 12 真实场景下各算法轨迹误差

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图 13 局部放大对比图

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表 1 实验参数表

参数	$ {\lambda } $	$ \alpha $	$ \beta $	$ {\delta }_{t} $	$ {\delta }_{\theta } $	飞行高度	$ \tau $
数值	0.85	0.6	0.4	0.02	0.017	100 m	0.04

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表 3 轨迹精度对比(m)

场景	指标	方法
场景	指标	FAST-LIO2	Faster-LIO	Slice-SLAM (无回环)	FAST-LIO-SAM	Faster-LIO-SC	Slice-SLAM (有回环)
仿真场景	MAXE	29.043	15.884	13.025	6.487	3.502	3.173
	MAE	2.625	1.999	1.672	1.846	1.472	1.401
	RSME	3.842	1.445	1.131	0.730	0.656	0.632
	HME	1.181	0.646	0.530	0.264	0.143	0.129
真实场景	MAXE	35.694	26.014	17.234	28.008	23.501	10.812
	MAE	13.598	8.808	6.268	10.261	8.137	5.215
	RSME	7.185	6.581	3.753	5.010	5.855	2.443
	HME	0.972	0.708	0.469	0.763	0.639	0.294

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表 2 语义提取运行效率(FPS)

方法	单帧帧率	3帧帧率	5帧帧率
RANSAC	14.388	11.990	10.010
Region Growth	5.376	1.001	0.529
3D_SEG	4.274	1.458	0.740
本文	211	112.104	62.893

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表 4 语义提取运行效率(FPS)

方法	单帧帧率	5帧帧率	10帧帧率
RANSAC	9.215	8.456	7.450
Region Growth	1.993	0.722	0.345
3D_SEG	3.425	1.276	0.309
本文	206.267	95.950	32.660

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表 5 回环检测结果对比

指标		方法
指标		ICP	NDT	GICP	Fast_GICP	Quatro	Scan_Context + ICP	本文
0～2 m	运行时间(s)	0.817271	0.735771	0.364556	0.336739	0.166818	0.422534	0.245286
0～2 m	置信分数	0.113421	0.513051	0.107523	0.115033	2.470698	0.111862	0.108105
4～6 m	运行时间(s)	0.760850	0.961706	0.379740	0.374925	0.169581	0.453751	0.250143
4～6 m	置信分数	0.129462	0.452106	0.117008	0.112414	2.587779	0.116497	0.110242
8～10 m	运行时间(s)	0.783931	0.863472	0.448796	0.333281	0.174932	0.465473	0.264625
8～10 m	置信分数	0.128523	0.581624	0.114952	0.114824	2.909427	0.114505	0.113278

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