在自主靠离泊环境中，水面无人艇使用传统激光同步定位与建图算法面临许多挑战，如水面高动态变化、水波干扰和局部极值问题等。针对这些问题，本文提出一种基于改进LIO-SAM（Lidar Inertial Odometry via Smoothing and Mapping）的USV（Unmanned Surface Vehicle）激光SLAM（Simultaneous Localization and Mapping）算法。首先，通过优化惯性测量单元预积分过程，在保持算法水面动态环境下精度的同时，降低了计算负担；其次，引入了一种基于复合滤波的水波干扰抑制方法，降低了水波对激光SLAM的影响；最后，采用了一种改进的图优化方法，有效解决了局部极值问题。实验结果表明，所提出的算法在USV的自主靠离泊环境中具有较高的定位精度和稳定性。
In the autonomous berthing and unberthing environment, the unmanned surface vehicle (USV) using the traditional simultaneous localization and mapping (SLAM) algorithm faces many challenges, such as high dynamic changes of water surface, water wave interference and local extremum problems. To solve these problems, this paper proposes a USV lidar SLAM algorithm based on improved LIO-SAM（Lidar Inertial Odometry via Smoothing and Mapping）. Firstly, by optimizing the pre integration process of inertial measurement unit (IMU), the computational burden is reduced while maintaining the accuracy of the algorithm in the dynamic environment of water surface; Secondly, a water wave interference suppression method based on composite filtering is introduced to reduce the influence of water wave on lidar SLAM; Finally, an improved graph optimization method is used to effectively solve the local extremum problem. Experimental results show that the proposed algorithm has high positioning accuracy and stability in the autonomous berthing and unberthing environment of USV.
2025,47(3): 117-122 收稿日期：2024-5-9
DOI：10.3404/j.issn.1672-7649.2025.03.019
分类号：U665.22
基金项目：国防科工局预研项目（JCKY2021206B015）
作者简介：刘华康(2000-),男,硕士研究生,研究方向为水面无人艇的环境感知技术
参考文献：
[1] ZHANG J, SINGH S. LOAM: Lidar odometry and mapping in real-time[J]. Robotics: Science and systems, 2014, 2(9): 1?9.
[2] YE H, CHEN Y, LIU M. Tightly coupled 3d lidar inertial odometry and mapping[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019.
[3] QIN C, YE H, PRANATA C E, et al. R-lins: A robocentric lidar-inertial state estimator for robust and efficient navigation[J] Robotics, 2019.
[4] SHAN T, ENGLOT B, MEYERS D, et al. Lio-sam: Tightly-coupled lidar inertial odometry via smoothing and mapping[C]//2020IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, 2020: 5135?5142.
[5] 闭雄栅. 基于激光雷达的无人船SLAM避障技术[D]. 大连: 大连海事大学, 2019.
[6] ZHOU B, HE Y, QIAN K, et al. S4-SLAM: A real-time 3D LIDAR SLAM system for ground/watersurface multi-scene outdoor applications[J]. Autonomous Robots, 2021, 45: 77?98.
[7] 李可染, 李立刚, 贺则昊, 等. 基于岸堤特征提取的近岸无人船激光SLAM方法[J/OL]. 激光与光电子学进展, 2022, 1?20.
[8] 平洋, 刘文斌, 缪正元等. 智能无人艇研究现状及关键问题发展趋势[J]. 船舶工程, 2023, 45(2): 61?69.
[9] ZHANG J, SINGH S. Low-drift and real-time lidar odometry and mapping[J]. Autonomous Robots, 2017, 41: 401?416.
[10] 韦和钧, 许恩永, 韩冰, 等. 基于自适应特征及闭环优化的激光即时定位与建图算法[J]. 激光与光电子学进展, 2023, 60(4): 201?211.
