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Volume 12 Issue 6
Jun.  2025

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2025  >  12(6): 1072-1094
G. Liu, K. Huang, X. Lv, Y. Sun, H. Li, X. Lei, Q. Yuan, and L. Shu, “Innovations and refinements in LiDAR odometry and mapping: A comprehensive review,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 6, pp. 1072–1094, Jun. 2025. doi: 10.1109/JAS.2025.125198
Citation: G. Liu, K. Huang, X. Lv, Y. Sun, H. Li, X. Lei, Q. Yuan, and L. Shu, “Innovations and refinements in LiDAR odometry and mapping: A comprehensive review,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 6, pp. 1072–1094, Jun. 2025. doi: 10.1109/JAS.2025.125198
shu

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

doi: 10.1109/JAS.2025.125198
Funds:  This work was supported by Jiangsu Agriculture Science and Technology Innovation Fund (CX(23)2003), China Agriculture Research System of MOF and MARA (CARS-28-21), the National Natural Science Foundation of China (32201680), the National Science and Technology Development Program of China (NK2022160104), and the National Key Research and Development Program of China (2022YFD2001400)
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    Abstract
  • 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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