Review of LiDAR-based SLAM methods

doi:10.3969/j.issn.1674-8484.2026.03.001

Abstract

Abstract:

As the core technology of robot navigation and environment perception, LiDAR-based simultaneous localization and mapping (SLAM) technology has been widely used in the fields of autonomous driving, drones, robots and so on. This paper summarizes the basic framework and key technologies of LiDAR-based SLAM, and focuses on the point cloud processing, front-end data registration, back-end optimization and closed-loop detection steps of LiDAR. It further reviews classical LiDAR-based SLAM methods and their corresponding improvements, discusses their technical innovations, and reveals the developmental trajectory and research trends of LiDAR-based SLAM, namely the transition from single-sensor-driven systems to multi-sensor fusion, from traditional geometric constraints to enhanced semantic understanding, and from local pose estimation to global consistency optimization. Meanwhile, an indoor 3D mapping experiment was conducted using a Livox HAP LiDAR to validate the effective support of high-precision maps constructed by SLAM for environmental perception tasks. Experimental results show that point cloud maps obtained through LiDAR-based SLAM can not only reconstruct the spatial structure of a scene with relatively high accuracy, but also provide a reliable data foundation for downstream tasks such as semantic segmentation, thereby demonstrating the significant application value of LiDAR-based SLAM in perception systems. Finally, the paper discusses the development potential of LiDAR-based SLAM in directions such as deep learning, neural radiance fields, and multi-sensor fusion, and points out that challenges remain in dynamic environment adaptation, the trade-off between real-time performance and accuracy, and large-scale engineering deployment.

Key words: LiDAR point cloud, LiDAR-based simultaneous localization and mapping (SLAM), frontend scan matching, backend optimization, loop closure detection, map construction

CLC Number:

TP242.6

LIU Kaiqi, KANG Fuxiang, LI Wei, GAO Bolin. Review of LiDAR-based SLAM methods[J]. Journal of Automotive Safety and Energy, 2026, 17(3): 279-295.

Figures/Tables 17

References 92

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前端扫描匹配方法	优点	缺点
ICP算法及其扩展算法	在较好的初始值下，收敛快	对噪声和初始条件敏感
相关性扫描匹配方法	全局暴力搜索，对噪声不敏感	资源消耗大，速度慢
正态分布变换方法	对噪声不敏感，能处理大型点云数据	Gauss分布假设不成立时效果差
基于特征的匹配方法	具有旋转和缩放不变性	依赖特征提取方法的选择

前端扫描匹配方法	优点	缺点
ICP算法及其扩展算法	在较好的初始值下，收敛快	对噪声和初始条件敏感
相关性扫描匹配方法	全局暴力搜索，对噪声不敏感	资源消耗大，速度慢
正态分布变换方法	对噪声不敏感，能处理大型点云数据	Gauss分布假设不成立时效果差
基于特征的匹配方法	具有旋转和缩放不变性	依赖特征提取方法的选择

方法类型	方法与特征	优缺点	适用场景
栅格地图	将空间离散成网格，使用Bayes滤波等方法更新栅格的占据概率	表示直观，建图稳定；二维表示，难适应三维环境	早期SLAM系统，简单环境建图
稠密点云地图	拼接连续激光点云，获得高精细三维模型	精度高，细节丰富；计算和存储开销大	自动驾驶、三维建模
稀疏点云地图	提取关键特征(角点、边缘等)建图	计算效率高，占用资源低；细节表达能力弱	移动机器人、资源受限场景
八叉树地图	使用递归空间划分，构建占据概率的三维地图	存储高效，支持实时更新；结构复杂，构建较难	三维建图、长期运行系统
增量式建图	在运动过程中持续更新地图，实现动态扩展	支持实时性，适应大场景；易积累误差，需配合闭环优化	室外导航、动态环境
层次化/多分辨率地图	地图分为粗细2层，全局结构+局部高分辨率细节	效率与精度平衡；设计复杂，参数多	大规模环境建图
语义地图	融合语义分割与几何信息，提升地图表达能力	具备环境语义理解能力；训练代价高	服务机器人、人机交互场景

方法类型	方法与特征	优缺点	适用场景
栅格地图	将空间离散成网格，使用Bayes滤波等方法更新栅格的占据概率	表示直观，建图稳定；二维表示，难适应三维环境	早期SLAM系统，简单环境建图
稠密点云地图	拼接连续激光点云，获得高精细三维模型	精度高，细节丰富；计算和存储开销大	自动驾驶、三维建模
稀疏点云地图	提取关键特征(角点、边缘等)建图	计算效率高，占用资源低；细节表达能力弱	移动机器人、资源受限场景
八叉树地图	使用递归空间划分，构建占据概率的三维地图	存储高效，支持实时更新；结构复杂，构建较难	三维建图、长期运行系统
增量式建图	在运动过程中持续更新地图，实现动态扩展	支持实时性，适应大场景；易积累误差，需配合闭环优化	室外导航、动态环境
层次化/多分辨率地图	地图分为粗细2层，全局结构+局部高分辨率细节	效率与精度平衡；设计复杂，参数多	大规模环境建图
语义地图	融合语义分割与几何信息，提升地图表达能力	具备环境语义理解能力；训练代价高	服务机器人、人机交互场景

经典方法	扩展方法/出处文献	特点
Cartographer ^[48]		大规模建图场景下的基于LiDAR的实时闭环检测方法
	蔡芸，等^[52]	语义信息改进重定位
	沈欣，等^[53]	速度积分位姿融合
LOAM^[49]		将激光雷达的位姿估计与环境建图解耦为两个相对独立的计算单元
	VLOAM ^[54]	融合视觉里程计和激光雷达里程计
	LIMO ^[55]	激光雷达的深度信息与单目视觉的特征追踪能力结合
	LeGO-LOAM ^[56]	引入轻量化、增加闭环检测
	ZHANG Ji，et al ^[57]	集成激光雷达、视觉和惯性测量单元
	LLOAM ^[58]	优化闭环检测、后端采用GTSAM 4.0因子图优化
	LIOM ^[59]	针对高速环境优化
	LOAM_livox ^[60]	针对小视场激光雷达的LOAM算法
	F-LOAM ^[61]	针对计算有限的机器人平台进行轻量化
Hector-SLAM ^[50]		低计算资源下的高效定位与建图
	ZHANG Yong，et al ^[63]	结合L-M优化和贝塞尔平滑动态加权A算法
	汪建华，等^[64]	采用插值法和扩展Kalman法提高精度
	苏易衡，等^[65]	降低对激光雷达精度和刷新频率的依赖
LIO-SAM ^[51]		紧耦合的激光雷达惯性里程计系统
	WANG Ji，et al ^[66]	引入基于强度扫描上下文改进
	MENG Xinyu，et al ^[67]	解决高度动态和无结构环境中的应用问题
	Faster-LIO ^[68]	引入增量体素的激光惯性里程计方法
	FAST-LIO2 ^[69]	引入高效的紧耦合迭代卡尔曼滤波器
其他方法	IMLS-SLAM ^[70]	仅依赖3D LiDAR数据进行定位和建图
	Barrau A，et al ^[71]	不变EKF在SLAM的应用
	FastSLAM ^[72]	粒子滤波和Rao-Blackwellized滤波的应用
	Gmapping ^[73]	基于FastSLAM 的高效2D SLAM 算法
	Grisetti G，et al ^[74]	优化rao-blackwelized粒子滤波器