Due to the fragility of single-sensor positioning technology in complex scenarios, especially in complex urban areas, multi-sensor positioning technology is becoming increasingly popular. To further improve the robustness of the positioning system by fully utilizing the information from various sensors, this article proposes a differential-GNSS-visual-inertial navigation system (DGVINS) that tightly fuses differential global navigation satellite system (GNSS), vision and inertial information to provide accurate, robust and seamless position information for intelligent navigation applications. DGVINS effectively utilizes all sensor measurements within the factor graph optimization framework. When using the carrier phase of GNSS, single-epoch ambiguity optimization is employed to prevent cycle slip detection and adapted to complex environments. We conducted experiments on public datasets with various features and compared the performance of simple differential-GNSS (DGNSS), DGNSS + Inertial, and the state-of-the-art GNSS-visual-inertial navigation systems. We also compared the performance of different combinations of GNSS differential factors in various environments. Due to the superiority of differential GNSS and its appropriate integration with visual and inertial measurements, the experimental results demonstrate that DGVINS exhibits significant improvements in accuracy, stability, and continuity in both GNSS-challenged and vision-challenged environments.

Aboveground biomass (AGB) of shrubs and low-statured trees constitutes a substantial portion of the total carbon pool in temperate forest ecosystems, contributing much to local biodiversity, altering tree-regeneration growth rates, and determining above- and belowground food webs. Accurate quantification of AGB at the shrub layer is crucial for ecological modeling and still remains a challenge. Several methods for estimating understory biomass, including inventory and remote sensing-based methods, need to be evaluated against measured datasets. In this study, we acquired 158 individual terrestrial laser scans (TLS) across 45 sites in the Yanshan Mountains and generated metrics including leaf area and stem volume from TLS data using voxel- and non-voxel-based approaches in both leaf-on and leaf-off scenarios. Allometric equations were applied using field-measured parameters as an inventory approach. The results indicated that allometric equations using crown area and height yielded results with higher accuracy than other inventory approach parameters (R² and RMSE ranging from 0.47 to 0.91 and 12.38 to 38.11 g, respectively). The voxel-based approach using TLS data provided results with R² and RMSE ranging from 0.86 to 0.96 and 6.43 to 21.03 g. Additionally, the non-voxel-based approach provided similar or slightly better results compared to the voxel-based approach (R² and RMSE ranging from 0.93 to 0.96 and 4.23 to 11.27 g, respectively) while avoiding the complexity of selecting the optimal voxel size that arises during voxelization.