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Review

Application of SLAM-Based Mobile Laser Scanning in Forest Inventory: Methods, Progress, Challenges, and Perspectives

by
Yexu Wu
1,
Shilei Zhong
1,2,3,
Yuxin Ma
1,
Yao Zhang
1 and
Meijie Liu
1,*
1
College of Physics Science, Qingdao University, Qingdao 266071, China
2
Center for Marine Observation and Communication, Qingdao University, Qingdao 266071, China
3
National Demonstration Center for Experiment Applied Physics Education, Qingdao 266071, China
*
Author to whom correspondence should be addressed.
Forests 2025, 16(6), 920; https://doi.org/10.3390/f16060920 (registering DOI)
Submission received: 15 April 2025 / Revised: 25 May 2025 / Accepted: 29 May 2025 / Published: 30 May 2025
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)

Abstract

A thorough understanding of forest resources and development trends is based on quick and accurate forest inventories. Because of its flexibility and localized independence, mobile laser scanning (MLS) based on simultaneous localization and mapping (SLAM) is the best option for forest inventories. The gap in the review studies in this field is filled by this study, which offers the first comprehensive review of SLAM-based MLS in forest inventory. This synthesis includes methods, research progress, challenges, and future perspectives of SLAM-based MLS in forest inventory. The precision and efficiency of SLAM-based MLS in forest inventories have benefited from improvements in data collection techniques and the ongoing development of algorithms, especially the application of deep learning. Based on evaluating the research progress of SLAM-based MLS in forest inventory, this paper provides new insights into the development of automation in this field. The main challenges of the current research are complex forest environments, localized bias, and limitations of the algorithms. To achieve accurate, real-time, and applicable forest inventories, researchers should develop SLAM technology dedicated to forest environments in the future so as to perform path planning, localization, autonomous navigation, obstacle avoidance, and point cloud recognition. In addition, researchers should develop algorithms specialized for different forest environments and improve the information processing capability of the algorithms to generate forest maps capable of extracting tree attributes automatically and in real time.
Keywords: SLAM; mobile laser scanning; forest inventory; data collection method; algorithm SLAM; mobile laser scanning; forest inventory; data collection method; algorithm

Share and Cite

MDPI and ACS Style

Wu, Y.; Zhong, S.; Ma, Y.; Zhang, Y.; Liu, M. Application of SLAM-Based Mobile Laser Scanning in Forest Inventory: Methods, Progress, Challenges, and Perspectives. Forests 2025, 16, 920. https://doi.org/10.3390/f16060920

AMA Style

Wu Y, Zhong S, Ma Y, Zhang Y, Liu M. Application of SLAM-Based Mobile Laser Scanning in Forest Inventory: Methods, Progress, Challenges, and Perspectives. Forests. 2025; 16(6):920. https://doi.org/10.3390/f16060920

Chicago/Turabian Style

Wu, Yexu, Shilei Zhong, Yuxin Ma, Yao Zhang, and Meijie Liu. 2025. "Application of SLAM-Based Mobile Laser Scanning in Forest Inventory: Methods, Progress, Challenges, and Perspectives" Forests 16, no. 6: 920. https://doi.org/10.3390/f16060920

APA Style

Wu, Y., Zhong, S., Ma, Y., Zhang, Y., & Liu, M. (2025). Application of SLAM-Based Mobile Laser Scanning in Forest Inventory: Methods, Progress, Challenges, and Perspectives. Forests, 16(6), 920. https://doi.org/10.3390/f16060920

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