{"title":"3D LiDAR SLAM: A survey","authors":"Yongjun Zhang, Pengcheng Shi, Jiayuan Li","doi":"10.1111/phor.12497","DOIUrl":null,"url":null,"abstract":"Simultaneous localization and mapping (SLAM) is a very challenging yet fundamental problem in the field of robotics and photogrammetry, and it is also a prerequisite for intelligent perception of unmanned systems. In recent years, 3D LiDAR SLAM technology has made remarkable progress. However, to the best of our knowledge, almost all existing surveys focus on visual SLAM methods. To bridge the gap, this paper provides a comprehensive review that summarizes the scientific connotation, key difficulties, research status, and future trends of 3D LiDAR SLAM, aiming to give readers a better understanding of LiDAR SLAM technology, thereby inspiring future research. Specifically, it summarizes the contents and characteristics of the main steps of LiDAR SLAM, introduces the key difficulties it faces, and gives the relationship with existing reviews; it provides an overview of current research hotspots, including LiDAR‐only methods and multi‐sensor fusion methods, and gives milestone algorithms and open‐source tools in each category; it summarizes common datasets, evaluation metrics and representative commercial SLAM solutions, and provides the evaluation results of mainstream methods on public datasets; it looks forward to the development trend of LiDAR SLAM, and considers the preliminary ideas of multi‐modal SLAM, event SLAM, and quantum SLAM.","PeriodicalId":22881,"journal":{"name":"The Photogrammetric Record","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Photogrammetric Record","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1111/phor.12497","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Simultaneous localization and mapping (SLAM) is a very challenging yet fundamental problem in the field of robotics and photogrammetry, and it is also a prerequisite for intelligent perception of unmanned systems. In recent years, 3D LiDAR SLAM technology has made remarkable progress. However, to the best of our knowledge, almost all existing surveys focus on visual SLAM methods. To bridge the gap, this paper provides a comprehensive review that summarizes the scientific connotation, key difficulties, research status, and future trends of 3D LiDAR SLAM, aiming to give readers a better understanding of LiDAR SLAM technology, thereby inspiring future research. Specifically, it summarizes the contents and characteristics of the main steps of LiDAR SLAM, introduces the key difficulties it faces, and gives the relationship with existing reviews; it provides an overview of current research hotspots, including LiDAR‐only methods and multi‐sensor fusion methods, and gives milestone algorithms and open‐source tools in each category; it summarizes common datasets, evaluation metrics and representative commercial SLAM solutions, and provides the evaluation results of mainstream methods on public datasets; it looks forward to the development trend of LiDAR SLAM, and considers the preliminary ideas of multi‐modal SLAM, event SLAM, and quantum SLAM.
同步定位与绘图(SLAM)是机器人学和摄影测量学领域一个极具挑战性的基本问题,也是无人系统实现智能感知的前提条件。近年来,三维激光雷达 SLAM 技术取得了显著进展。然而,据我们所知,几乎所有现有的研究都集中在视觉 SLAM 方法上。为了弥补这一空白,本文对三维激光雷达 SLAM 的科学内涵、关键难点、研究现状和未来趋势进行了全面综述,旨在让读者更好地了解激光雷达 SLAM 技术,从而对未来的研究有所启发。具体而言,本书总结了LiDAR SLAM主要步骤的内容和特点,介绍了其面临的关键难点,并给出了与现有综述的关系;概述了当前的研究热点,包括纯LiDAR方法和多传感器融合方法,并给出了每类方法中具有里程碑意义的算法和开源工具;总结了常见的数据集、评估指标和代表性的商业 SLAM 解决方案,并提供了主流方法在公共数据集上的评估结果;展望了激光雷达 SLAM 的发展趋势,并考虑了多模态 SLAM、事件 SLAM 和量子 SLAM 的初步设想。