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

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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.
Funding Information
  • Key Laboratory of Space Ocean Remote Sensing and Application, MNR (2023CFO015, 41976173, 61971455, 41976168, ZR2019MD016, ZR2020MD095, ZR201910290171)
  • National Natural Science Foundation of China (2023CFO015, 41976173, 61971455, 41976168, ZR2019MD016, ZR2020MD095, ZR201910290171)
  • Shandong Provincial Natural Science Foundation, China (2023CFO015, 41976173, 61971455, 41976168, ZR2019MD016, ZR2020MD095, ZR201910290171)

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