A Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS)/Light Detection and Ranging (LiDAR)-Simultaneous Localization and Mapping (SLAM) integrated navigation system based on graph optimization is proposed and implemented in this paper. The navigation results are obtained by the information fusion of the GNSS position, Inertial Measurement Unit (IMU) preintegration result and the relative pose from the 3D probability map matching with graph optimizing. The sliding window method was adopted to ensure that the computational load of the graph optimization does not increase with time. Land vehicle tests were conducted, and the results show that the proposed GNSS/INS/LiDAR-SLAM integrated navigation system can effectively improve the navigation positioning accuracy compared to GNSS/INS and other current GNSS/INS/LiDAR methods. During the simulation of one-minute periods of GNSS outages, compared to the GNSS/INS integrated navigation system, the root mean square (RMS) of the position errors in the North and East directions of the proposed navigation system are reduced by approximately 82.2% and 79.6%, respectively, and the position error in the vertical direction and attitude errors are equivalent. Compared to the benchmark method of GNSS/INS/LiDAR-Google Cartographer, the RMS of the position errors in the North, East and vertical directions decrease by approximately 66.2%, 63.1% and 75.1%, respectively, and the RMS of the roll, pitch and yaw errors are reduced by approximately 89.5%, 92.9% and 88.5%, respectively. Furthermore, the relative position error during the GNSS outage periods is reduced to 0.26% of the travel distance for the proposed method. Therefore, the GNSS/INS/LiDAR-SLAM integrated navigation system proposed in this paper can effectively fuse the information of GNSS, IMU and LiDAR and can significantly mitigate the navigation error, especially for cases of GNSS signal attenuation or interruption.

Forest mapping, one of the main components of performing a forest inventory, is an important driving force in the development of laser scanning. Mobile laser scanning (MLS), in which laser scanners are installed on moving platforms, has been studied as a convenient measurement method for forest mapping in the past several years. Positioning and attitude accuracies are important for forest mapping using MLS systems. Inertial Navigation Systems (INSs) and Global Navigation Satellite Systems (GNSSs) are typical and popular positioning and attitude sensors used in MLS systems. In forest environments, because of the loss of signal due to occlusion and severe multipath effects, the positioning accuracy of GNSS is severely degraded, and even that of GNSS/INS decreases considerably. Light Detection and Ranging (LiDAR)-based Simultaneous Localization and Mapping (SLAM) can achieve higher positioning accuracy in environments containing many features and is commonly implemented in GNSS-denied indoor environments. Forests are different from an indoor environment in that the GNSS signal is available to some extent in a forest. Although the positioning accuracy of GNSS/INS is reduced, estimates of heading angle and velocity can maintain high accurate even with fewer satellites. GNSS/INS and the LiDAR-based SLAM technique can be effectively integrated to form a sustainable, highly accurate positioning and mapping solution for use in forests without additional hardware costs. In this study, information such as heading angles and velocities extracted from a GNSS/INS is utilized to improve the positioning accuracy of the SLAM solution, and two information-aided SLAM methods are proposed. First, a heading angle-aided SLAM (H-aided SLAM) method is proposed that supplies the heading angle from GNSS/INS to SLAM. Field test results show that the horizontal positioning accuracy of an entire trajectory of 800 m is 0.13 m and is significantly improved (by 70%) compared to that of a traditional GNSS/INS; second, a more complex information added SLAM solution that utilizes both heading angle and velocity information simultaneously (HV-aided SLAM) is investigated. Experimental results show that the horizontal positioning accuracy can reach a level of six centimetres with the HV-aided SLAM, which is a significant improvement (by 86%). Thus, a more accurate forest map is obtained by the proposed integrated method.

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%.

The rapid movement of low Earth orbit (LEO) satellite can improve geometric diversity, which contributes to the rapid convergence of Global Navigation Satellite System (GNSS) precise point positioning (PPP). However, the LEO onboard receiver clock cannot be used directly by PPP users as the LEO satellite clock because the LEO onboard receiver clock and LEO satellite clock absorb different code delays when receiving and transmitting signals. In this study, a real-time estimation approach for the LEO satellite clock based on ground tracking stations was proposed for the first time. The feasibility for the rapid convergence of the LEO satellite clock was analyzed using the satellite time dilution of precision (TDOP) that one satellite is relative to multiple ground tracking stations. The LEO constellation of 168 satellites and observations for 15 ground tracking stations were simulated to verify the proposed method. The experiment results showed that the average convergence time was 31.21 min for the Global Positioning System (GPS) satellite clock, whereas the value for the LEO satellite clock was only 2.86 min. The average root mean square (RMS) and standard deviation (STD) values after convergence were 0.71 and 0.39 ns for the LEO satellite clock, whereas the values were 0.31 and 0.13 ns for the GPS satellite clock. The average weekly satellite TDOP for the LEO satellite was much smaller than that for the GPS satellite. The average satellite TDOPs for all LEO and GPS satellites were 19.13 and 1294.70, respectively. However, the average delta TDOPs caused by satellite motion for all LEO and GPS satellites were both 0.10. Therefore, the rapid convergence of the LEO satellite clock resulted from the better geometric distribution of the LEO satellite relative to ground stations. Despite errors and the convergence time of the LEO satellite clock, the convergence time and positioning accuracy for LEO-augmented GPS and BeiDou Navigation Satellite System (BDS) PPP with the real-time estimated LEO satellite clock can still reach 10.63 min, 1.94 cm, 1.44 cm, and 4.18 cm in the east, north, and up components, respectively. The improvements caused by LEO satellite for GPS/BDS PPP were 59%, 30%, 31%, and 33%, respectively.

Global navigation satellite system (GNSS) is one of the most effective means for landslide monitoring. At present, most studies on GNSS-based landslide monitoring focus on the long-term landslide analysis, while short-term landslide displacement is not clear. The purpose of this article is to explore a short-term displacement detection method based on GNSS kinematic positioning for landslide monitoring. The significance and feasibility of short-term landslide monitoring are presented, and a short-term displacement detection method based on GNSS kinematic positioning time series segmentation is proposed. The coordinate time series is reconstructed by the Daubechies wavelet to extract the abrupt components. The detection window is formed by the current epoch coordinates and the previous epochs’ coordinates and segmented according to the segmentation index. The segmentation point obtained by segmenting the detection window is regarded as a possible change point (PCP), and a test is conducted to determine whether the segmentation point is a change point (CP). Simulation and field experiments were carried out to verify the proposed method. The results show the feasibility and effectiveness of the method for short-term landslide change detection. The influence of the detection window size and segmentation index on the proposed method is discussed, and suggestions for the selection of detection window size as well as segmentation index are given.

Simultaneous localization and mapping (SLAM) has been investigated in the field of robotics for two decades, as it is considered to be an effective method for solving the positioning and mapping problem in a single framework. In the SLAM community, the Extended Kalman Filter (EKF) based SLAM and particle filter SLAM are the most mature technologies. After years of development, graph-based SLAM is becoming the most promising technology and a lot of progress has been made recently with respect to accuracy and efficiency. No matter which SLAM method is used, loop closure is a vital part for overcoming the accumulated errors. However, in 2D Light Detection and Ranging (LiDAR) SLAM, on one hand, it is relatively difficult to extract distinctive features in LiDAR scans for loop closure detection, as 2D LiDAR scans encode much less information than images; on the other hand, there is also some special mapping scenery, where no loop closure exists. Thereby, in this paper, instead of loop closure detection, we first propose the method to introduce extra control network constraint (CNC) to the back-end optimization of graph-based SLAM, by aligning the LiDAR scan center with the control vertex of the presurveyed control network to optimize all the poses of scans and submaps. Field tests were carried out in a typical urban Global Navigation Satellite System (GNSS) weak outdoor area. The results prove that the position Root Mean Square (RMS) error of the selected key points is 0.3614 m, evaluated with a reference map produced by Terrestrial Laser Scanner (TLS). Mapping accuracy is significantly improved, compared to the mapping RMS of 1.6462 m without control network constraint. Adding distance constraints of the control network to the back-end optimization is an effective and practical method to solve the drift accumulation of LiDAR front-end scan matching.