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Innovations and Refinements in LiDAR Odometry and Mapping: A Comprehensive Review

文献类型： 外文期刊

作者： Liu, Guangjie 1 ; Huang, Kai 2 ; Lv, Xiaolan 2 ; Sun, Yuanhao 2 ; Li, Hailong 1 ; Lei, Xiaohui 2 ; Yuan, Quanchun 2 ; Shu, Lei 4 ;

作者机构： 1.Nanjing Univ Informat Sci & Technol, Jiangsu Acad Agr Sci, Inst Agr Facil & Equipment, Coll Automat, Nanjing 210014, Peoples R China

2.Minist Agr & Rural Affairs, Key Lab Modern Hort Equipment, Nanjing 210014, Peoples R China

3.Jiangsu Acad Agr Sci, Inst Agr Facil & Equipment, Nanjing 210014, Peoples R China

4.Nanjing Agr Univ, Coll Artificial Intelligence, Nanjing 210031, Peoples R China

5.Univ Lincoln, Sch Engn, Lincoln LN6 7TS, England

关键词： Simultaneous localization and mapping; Laser radar; Three-dimensional displays; Optimization; Accuracy; Sensors; Real-time systems; Robots; Feature extraction; Point cloud compression; Autonomous navigation; LiDAR; LiDAR odometry and mapping (LOAM); multi-sensor fusion; simultaneous localization and mapping (SLAM)

期刊名称：IEEE-CAA JOURNAL OF AUTOMATICA SINICA （ 影响因子：19.2； 五年影响因子：13.1 ）

ISSN： 2329-9266

年卷期： 2025 年 12 卷 6 期

页码：

收录情况： SCI

摘要： Since its introduction in 2014, the LiDAR odometry and mapping (LOAM) algorithm has become a cornerstone in the fields of autonomous driving and intelligent robotics. LOAM provides robust support for autonomous navigation in complex dynamic environments through precise localization and environmental mapping. This paper offers a comprehensive review of the innovations and optimizations made to the LOAM algorithm, covering advancements in multi-sensor fusion technology, frontend processing optimization, backend optimization, and loop closure detection. These improvements have significantly enhanced LOAM's performance in various scenarios, including urban, agricultural, and underground environments. However, challenges remain in areas such as data synchronization, real-time processing, computational complexity, and environmental adaptability. Looking ahead, future developments are expected to focus on creating more efficient multi-sensor fusion algorithms, expanding application domains, and building more robust systems, thereby driving continued progress in autonomous driving, intelligent robotics, and autonomous unmanned systems.

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