FOREST FEATURE LIDAR SLAM (F 2 -LSLAM) AND INTEGRATED SCAN SIMULTANEOUS TRAJECTORY ENHANCEMENT AND MAPPING (IS 2 -TEAM) FOR ACCURATE FOREST INVENTORY USING BACKPACK SYSTEMS

• Published in: International archives of the photogrammetry, remote sensing and spatial information sciences. Vol. XLVIII-1/W2-2023; pp. 1823 - 1830
• Main Authors: Zhao, C., Zhou, T., Fei, S., Habib, A.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Gottingen Copernicus GmbH 14.12.2023; Copernicus Publications
• Subjects: Backpacks; Biometrics; Biometry; Canopies; Canopy; Data collection; Forests; Global navigation satellite system; Height; Lidar; Mapping; Navigation; Navigational satellites; Performance evaluation; Plant cover; Simultaneous localization and mapping; Trajectories
• ISSN: 2194-9034; 1682-1750; 2194-9034
• DOI: 10.5194/isprs-archives-XLVIII-1-W2-2023-1823-2023

Under-canopy mapping is desired to derive critical forest biometrics, such as diameter at breast height (DBH), merchantable height, and debris volume. The main challenge of such under-canopy mapping is the intermittent access to the global navigation satellite system (GNSS) signal, which is crucial to deriving accurately georeferenced mapping products. In this study, we propose two frameworks – Forest Feature LiDAR Simultaneous Localization and Mapping (F2-LSLAM) and Integrated Scan Simultaneous Trajectory Enhancement and mapping (IS2-TEAM) – for 3D LiDAR unit mounted on backpack systems to achieve accurate forest inventory. In the F2-LSLAM strategy, ground/tree trunk features are extracted from individual LiDAR scans. On the other hand, when trajectory information provided by navigation sensors is available, these semantic features are derived from LiDAR points within several scans (i.e., integrated scan) for the IS2-TEAM strategy. Then, local/global least squares adjustment (LSA) using derived features is performed to register LiDAR scans to a common reference frame for both strategies. To evaluate the performance of the proposed strategies, three in-house developed backpack systems with varying specifications were used to collect data in complicated forest environments. Through the comparison with point clouds acquired by a commercial backpack LiDAR system, the proposed frameworks are capable of generating point clouds with satisfactory intra-dataset alignment quality (in the range of 2–4 cm) for all backpack systems in natural forest areas with relatively flat terrain. However, for more challenging areas with dense undergrowth vegetation and/or large height differences, F2-LSLAM framework cannot extract sufficient features, while IS2-TEAM still exhibits good performance.