SLAM-based Forest Plot Mapping by Integrating IMU and Self-calibrated Dual 3D Laser Scanners

• Published in: IEEE transactions on geoscience and remote sensing 2023-08, p.1-1
• Main Authors: Pan, Dong, Shao, Jie, Zhang, Shuhang, Zhang, Shuai, Chang, Bingtao, Xiong, Hao, Zhang, Wuming
• Format: Article
• Language: English
• Subjects: backpack laser scanning; dual laser scanners; Feature extraction; Forestry; forests; Laser radar; Measurement by laser beam; Point cloud compression; Sensors; Simultaneous localization and mapping; SLAM
• ISSN: 0196-2892
• DOI: 10.1109/TGRS.2023.3307817

Efficiently and accurately measuring forest structure is of great significance for high-quality assessment of forest resources. Backpack laser scanning (BLS) has become a common device to acquire forest structural information due to its low cost and high time efficiency. However, complex forest environments bring challenges to BLS-based forest mapping, which faces problems with incomplete data and poor mapping accuracy. In this article, we design a disassembly-free dual-scanner BLS system for complete and accurate forest mapping. We first execute a high precision automatic self-calibration of dual laser scanners by means of angle compensation and the fixed rotation angle. Then, a simultaneous localization and mapping (SLAM) framework by combining the natural feature of trees and Inertial Measurement Unit (IMU) measurements is proposed, in which IMU provides priori motion estimation and motion compensation for dual scanners, and the natural feature of the forest is used to correct motion. The proposed method is validated in three small-scale forest plots with size of 0.1 ha. Experimental results show well performance in terms of mapping accuracy, where the mean errors and the root square mean errors are less than 3.0 cm in both horizontal and vertical directions. Our study demonstrates the effectiveness of the proposed strategy and has the potential to perform accurate and complete mapping in understory.