The detailed structure information under the forest canopy is important for forestry surveying. As a high-precision environmental sensing and measurement method, terrestrial laser scanning (TLS) is widely used in high-precision forestry surveying. In TLS-based forestry surveys, stem-mapping, which is focused on detecting and extracting trunks, is one of the core data processing tasks and the basis for the subsequent calculation of tree attributes; one of the most basic attributes is the diameter at breast height (DBH). This article explores and improves the methods for stem mapping and DBH estimation from TLS data. Firstly, an improved 3D stem mapping algorithm considering the growth direction in random sample consistency (RANSAC) cylinder fitting is proposed to extract and fit the individual tree point cloud section. It constructs the hierarchical optimum cylinder of the trunk and introduces the growth direction into the establishment of the backbone buffer in the next layer. Experimental results show that it can effectively remove most of the branches and reduce the interference of the branches to the discrimination of trunks and improve the integrity of stem extraction by about 36%. Secondly, a robust least squares ellipse fitting method based on the elliptic hypothesis is proposed for DBH estimation. Experimental results show that the DBH estimation accuracy of the proposed estimation method is improved compared with other methods. The mean root mean squared error (RMSE) of the proposed estimation method is 1.14 cm, compared with other methods with a mean RMSE of 1.70, 2.03, and 2.14 cm. The mean relative accuracy of the proposed estimation method is 95.2%, compared with other methods with a mean relative accuracy of 92.9%, 91.9%, and 90.9%.

Intelligent vehicles and connected vehicles have garnered more and more attention recently, and both require accurate positions of the vehicles in their operation, which relies on navigation sensors such as Global Navigation Satellite System (GNSS), Inertial Navigation System (INS), Light Detection And Ranging (LiDAR) and so on. GNSS is the key sensor to obtain high accuracy positions in the navigation system, because GNSS Real Time Kinematic (RTK) with correct ambiguity resolution (AR) can provide centimeter-level absolute position. But AR may fail in the urban occlusion environment because of the limited satellite visibility for single vehicles. The navigation data from multiconnected vehicles can improve the satellite geometry significantly, which is able to help improve the AR, especially in occlusion environment. In this work, the GNSS, INS, and LiDAR data from multiconnected vehicles are jointly processed together to improve the GNSS RTK AR, and to obtain high accuracy positioning results, using a scan-to-map matching algorithm based on an occupancy likelihood map (OLM) for the relative position between the connected vehicles, a Damped Least-squares AMBiguity Decorrelation Adjustment (LAMBDA) method with least-squares for a relative AR between the connected vehicles, and a joint RTK algorithm for solving the absolute positioning for the vehicles by involving the relative position and relative ambiguity constraints. The experimental results show that the proposed approach can improve the AR for the connected vehicles with higher ratio values, success rates, and fixed rates, and achieve high-precision cooperative absolute positions compared with traditional GNSS RTK methods, especially in occlusion environments such as below a viaduct.