English

应用科学学报 >

2026 , Vol. 44 >Issue 4: 515 - 536

DOI: https://doi.org/10.3969/j.issn.0255-8297.2026.04.001

智能视觉感知

激光雷达同步定位与建图研究综述

  • 段旭哲 ,
  • 钟若飞 ,
  • 李健 ,
  • 付晶 ,
  • 赵鹏程 ,
  • 李加元 ,
  • 艾明耀 ,
  • 胡庆武
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  • 1. 武汉大学 遥感信息工程学院, 湖北 武汉 430079;
    2. 首都师范大学 资源环境与旅游学院, 北京 100048;
    3. 郑州大学 地球科学与技术学院, 河南 郑州 450001;
    4. 中国电力科学研究院有限公司, 湖北 武汉 430072

收稿日期: 2026-02-23

  网络出版日期: 2026-08-01

基金资助

国家自然科学基金(No.42371439)

收起

Review of LiDAR Simultaneous Localization and Mapping

  • DUAN Xuzhe ,
  • ZHONG Ruofei ,
  • LI Jian ,
  • FU Jing ,
  • ZHAO Pengcheng ,
  • LI Jiayuan ,
  • AI Mingyao ,
  • HU Qingwu
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  • 1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, Hubei, China;
    2. College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China;
    3. School of Geo-Science and Technology, Zhengzhou University, Zhengzhou 450001, Henan, China;
    4. China Electric Power Research Institute Co., Ltd., Wuhan 430072, Hubei, China

Received date: 2026-02-23

  Online published: 2026-08-01

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摘要

本文系统梳理了近十年来激光雷达同步定位与建图（simultaneous localization and mapping,SLAM）技术的发展脉络。首先对SLAM问题给出通用的数学定义，以此建立统一的分析框架。随后按照平台数量将相关研究归纳为单平台激光雷达SLAM与多平台协同激光雷达SLAM两大类别。在单平台部分，重点总结了以激光雷达为核心的多传感器融合方法；在多平台部分，概述了当前较为成熟的协同SLAM系统及其关键特征。同时讨论了激光雷达SLAM在复杂场景、退化环境、多模态数据源及多平台协同下所面临的挑战与发展机遇。最后结合当前研究热点，对激光雷达SLAM未来的技术演进趋势进行了展望，分析了深度学习在激光雷达SLAM中的主要应用形式，并概述了神经辐射场和三维高斯泼溅等新型地图表达在建图质量与表示能力方面的潜在价值。

关键词： 同步定位与建图; 激光雷达; 多源数据融合; 多平台协同; 视觉定位导航

本文引用格式

段旭哲 , 钟若飞 , 李健 , 付晶 , 赵鹏程 , 李加元 , 艾明耀 , 胡庆武 . 激光雷达同步定位与建图研究综述[J]. 应用科学学报, 2026 , 44(4) : 515 -536 . DOI: 10.3969/j.issn.0255-8297.2026.04.001

Abstract

The development of light detection and ranging(LiDAR) simultaneous localization and mapping(SLAM) technology over the past decade was systematically reviewed.First, a general mathematical definition of the SLAM problem was provided to establish a unified analytical framework. Subsequently, the related research was categorized into two main categories according to the number of platforms: single-platform LiDAR SLAM and multi-platform collaborative LiDAR SLAM. In the single-platform section, the multi-sensor fusion methods centered on LiDAR were emphatically summarized; in the multi-platform section, the currently mature collaborative SLAM systems and their key characteristics were outlined. Meanwhile, the challenges and development opportunities faced by LiDAR SLAM in complex scenes, degraded environments, multi-modal data sources, and multiplatform collaboration were discussed. Finally, combined with current research hotspots,the future technical evolution trends of LiDAR SLAM were prospected. The main application forms of deep learning in LiDAR SLAM were analyzed, and the potential value of emerging map representations(such as neural radiance fields and three-dimensional Gaussian splatting) in mapping quality and representation capability was outlined.

Key words： simultaneous localization and mapping(SLAM); light detection and ranging(LiDAR); multisource data fusion; multi-platform collaboration; visual localization and navigation

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