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Using a GNSS RTK (Global Navigation Satellite System Real Time Kinematic) -equipped unmanned aerial vehicle (UAV) could greatly simplify the construction of highly accurate digital models through SfM (Structure from Motion) photogrammetry, possibly even avoiding the need for ground control points (GCPs). As previous studies on this topic were mostly performed using fixed-wing UAVs, this study aimed to investigate the results achievable by a quadrocopter (DJI Phantom 4 RTK). Three image acquisition flights were performed for two sites of a different character (urban and rural) along with three calculation variants for each flight: georeferencing using ground-surveyed GCPs only, onboard GNSS RTK only, and a combination thereof. The combined and GNSS RTK methods provided the best results (at the expected level of accuracy of 1–2 GSD (Ground Sample Distance)) for both the vertical and horizontal components. The horizontal positioning was also accurate when georeferencing directly based on the onboard GNSS RTK; the vertical component, however, can be (especially where the terrain is difficult for SfM evaluation) burdened with relatively high systematic errors. This problem was caused by the incorrect identification of the interior orientation parameters calculated, as is customary for non-metric cameras, together with bundle adjustment. This problem could be resolved by using a small number of GCPs (at least one) or quality camera pre-calibration.

Georeferencing using ground control points (GCPs) is the most common strategy in photogrammetry modeling using unmanned aerial vehicle (UAV)-acquired imagery. With the increased availability of UAVs with onboard global navigation satellite system–real-time kinematic (GNSS RTK), georeferencing without GCPs is becoming a promising alternative. However, systematic elevation error remains a problem with this technique. We aimed to analyze the reasons for this systematic error and propose strategies for its elimination. Multiple flights differing in the flight altitude and image acquisition axis were performed at two real-world sites. A flight height of 100 m with a vertical (nadiral) image acquisition axis was considered primary, supplemented with flight altitudes of 75 m and 125 m with a vertical image acquisition axis and two flights at 100 m with oblique image acquisition axes (30° and 15°). Each of these flights was performed twice to produce a full double grid. Models were reconstructed from individual flights and their combinations. The elevation error from individual flights or even combinations yielded systematic elevation errors of up to several decimeters. This error was linearly dependent on the deviation of the focal length from the reference value. A combination of two flights at the same altitude (with nadiral and oblique image acquisition) was capable of reducing the systematic elevation error to less than 0.03 m. This study is the first to demonstrate the linear dependence between the systematic elevation error of the models based only on the onboard GNSS RTK data and the deviation in the determined internal orientation parameters (focal length). In addition, we have shown that a combination of two flights with different image acquisition axes can eliminate this systematic error even in real-world conditions and that georeferencing without GCPs is, therefore, a feasible alternative to the use of GCPs.

The validity of the results obtained within different permanent GNSS reference station networks (GNSS Network) must be periodically controlled using criteria that are generally known from statistical analyzes or prescribed by International Standards. Procedures for evaluating the uncertainty of measurements are defined in accordance with the purpose of the GNSS Network. The authors of this paper want to point out the need to establish requirements for periodical and systematical control of GNSS coordinates within the same permanent GNSS Network and control of GNSS coordinates between different permanent GNSS Networks measured on the same/unique point on the ground. This paper presents control procedures for three permanent GNSS reference station Networks established and operating in the Republic of Serbia. Special attention is on the analysis of data consistency within one permanent GNSS Network and the mutual consistency of GNSS data between different networks. The paper aims to promote reliance on the different GNSS Networks and contains suggestions on how GNSS Networks may prove that they are performing competently and that they can provide valid results for field measurements. Particularly highlighted is the need to plan and implement measures related to increasing the effectiveness of the GNSS system, achieving improved results, and preventing negative effects while performing field measurements. The paper presents the results for comparison, selected according to the rules for creating a Digital Cadastral Map features, i.e., points, lines, and polygon. The results for comparing point features are the GNSS coordinates. The results for comparing line features are the lengths of the line, i.e., distances, and the results for comparing polygon features are the areas of the polygons.

Global Navigation Satellite System Real-time Kinematic (GNSS-RTK) is an indispensable source for the absolute positioning of autonomous systems. Unfortunately, the performance of the GNSS-RTK is significantly degraded in urban canyons, due to the notorious multipath and Non-Line-of-Sight (NLOS). On the contrary, LiDAR/inertial odometry (LIO) can provide locally accurate pose estimation in structured urban scenarios but is subjected to drift over time. Considering their complementarities, GNSS-RTK, adaptively integrated with LIO was proposed in this paper, aiming to realize continuous and accurate global positioning for autonomous systems in urban scenarios. As one of the main contributions, this paper proposes to identify the quality of the GNSS-RTK solution based on the point cloud map incrementally generated by LIO. A smaller mean elevation angle mask of the surrounding point cloud indicates a relatively open area thus the correspondent GNSS-RTK would be reliable. Global factor graph optimization is performed to fuse reliable GNSS-RTK and LIO. Evaluations are performed on datasets collected in typical urban canyons of Hong Kong. With the help of the proposed GNSS-RTK selection strategy, the performance of the GNSS-RTK/LIO integration was significantly improved with the absolute translation error reduced by more than 50%, compared with the conventional integration method where all the GNSS-RTK solutions are used.

Geospatial information acquired with Unmanned Aerial Vehicles (UAV) provides valuable decision-making support in many different domains, and technological advances coincide with a demand for ever more sophisticated data products. One consequence is a research and development focus on more accurately referenced images and derivatives, which has long been a weakness especially of low to medium cost UAV systems equipped with relatively inexpensive inertial measurement unit (IMU) and Global Navigation Satellite System (GNSS) receivers. This research evaluates the positional accuracy of the real-time kinematics (RTK) GNSS on the DJI Matrice 600 Pro, one of the first available and widely used UAVs with potentially surveying-grade performance. Although a very high positional accuracy of the drone itself of 2 to 3 cm is claimed by DJI, the actual accuracy of the drone RTK for positioning the images and for using it for mapping purposes without additional ground control is not known. To begin with, the actual GNSS RTK position of reference center (the physical point on the antenna) on the drone is not indicated, and uncertainty regarding this also exists among the professional user community. In this study the reference center was determined through a set of experiments using the dual frequency static Leica GNSS with RTK capability. The RTK positioning data from the drone were then used for direct georeferencing, and its results were evaluated. Test flights were carried out over a 70 x 70 m area with an altitude of 40 m above the ground, with a ground sampling distance of 1.3 cm. Evaluated against ground control points, the planimetric accuracy of direct georeferencing for the photogrammetric product ranged between 30 and 60 cm. Analysis of direct georeferencing results showed a time delay of up to 0.28 seconds between the drone GNSS RTK and camera image acquisition affecting direct georeferencing results.

At the current stage, the automation level of GNSS RTK equipment is low, and manual operation leads to decreased accuracy and efficiency in setting out. To address these issues, this paper has designed an algorithm for automatic setting out that resolves the common problem of reduced accuracy in conventional RTK. First, the calculation of the laser rotation center is conducted using relevant parameters to calibrate the instrument’s posture and angle. Then, by analyzing the posture information, the relative position and direction of the instrument to the point to be set out are determined, and the rotation angles in the horizontal and vertical directions are calculated. Following this, the data results are analyzed, and the obtained rotation angles are output to achieve automatic control of the instrument. Finally, a rotating laser composed of servo motors and laser modules is used to control the GNSS RTK equipment to locate the set-out point, thereby determining its position on the ground and displaying it in real-time. Compared to traditional GNSS RTK equipment, the proposed automatic setting out algorithm and the developed GNSS laser RTK equipment reduce the setting out error from 15 mm to 10.3 mm. This reduces the barrier to using GNSS RTK equipment, minimizes human influence, enhances the work efficiency of setting out measurements, and ensures high efficiency and stability under complex conditions.

Accurate navigation in outdoor environments requires integrating multiple sensor sources for reliable localization and trajectory tracking. This study proposes Pure Pursuit with Dynamic Steering Control (PP-DSC), which adaptively adjusts both lookahead distance and velocity based on steering angle. The algorithm was deployed on a four-wheeled steering-type autonomous mobile robot (AMR) using Robot Operating System 2 (ROS 2) Jazzy, with real-time sensor fusion from GNSS-RTK, IMU, and wheel encoders. Experiments were conducted on straight, circular, and figure-eight trajectories at 1.0–5.0 m/s in an open area (64 × 20 m). PP-DSC achieved mean lateral deviations of 0.05, 0.07, and 0.08 m respectively, representing 68–82% improvement over standard PP (means 0.19, 0.40, and 0.27 m). To evaluate cross-domain applicability, the algorithm was extended with a Fire and Explosion Index (F&EI)-based safety factor (Safety-integrated PP-DSC) and tested via simulation in an empty fruit bunch (EFB) biodiesel plant (92 × 65 m). Standard PP outperformed Safety-integrated PP-DSC by 15.6% in this industrial setting due to tight turning radii (5–9 m), though Safety-integrated PP-DSC retained advantages in moderate-curvature sections with 11–17% improvement. The F&EI-based safety integration added less than 1% tracking overhead while providing automatic velocity reduction in hazard zones for Process Safety Management (PSM) compliance. The findings confirm that PP-DSC significantly improves trajectory tracking in open-field environments, while industrial deployment requires geometry-specific algorithm selection.

Coastal sand dunes, shaped by aeolian and marine processes, are critical to natural ecosystems and human societies, making their morphological monitoring essential for effective conservation. However, large-scale, high-precision monitoring of topographic change remains a persistent challenge, a challenge that advanced sensing technologies can address. In this study, we propose an integrated, sensor-based approach using a UAV-mounted light detection and ranging (LiDAR) system, combined with a GNSS-RTK positioning unit and a novel ground control point (GCP) design to acquire high-resolution topographic data. Field surveys were conducted at four time points between October 2022 and February 2023 in the Tottori Sand Dunes, Japan. The digital elevation models (DEMs) derived from LiDAR point clouds achieved centimeter-level accuracy, enabling reliable detection of subtle topographic changes. Analysis of DEM differencing revealed that wind-driven sand deposition and erosion resulted in elevation changes of up to 0.4 m. These results validate the efficacy of the UAV-LiDAR sensor system for high-resolution, multitemporal monitoring of coastal sand dunes, highlighting its potential to advance the development of environmental sensing frameworks and support data-driven conservation strategies.

A wide range of applications from multiple sectors already use ultra-wideband (UWB) technology to locate and track assets precisely. This is not the case, however, for first responder localization during emergency response (ER) operations, which are highly conditioned by procedural and environmental constraints. After analyzing these limitations and reviewing the current state-of-the-art solutions, this work presents a UWB-based indoor positioning system (IPS) that relies on the global navigation satellite system real-time kinematic (GNSS-RTK) technology to quickly, accurately, and safely deploy its required infrastructure on site. A set of tests conducted on a two-story building prove the suitability of such a system, providing an average accuracy of less than 1 meter for static targets and the ability to faithfully reproduce the path followed by a mobile target inside the building. The obtained results strengthen the presented approach and pave the way for more sophisticated UWB-based IPSs that would include unmanned aerial vehicles (UAVs) and/or mobile robots to speed up network deployment even more while offering additional ER services.

This study focused on the monitoring of a bridge using the global navigation satellite system real-time kinematic (GNSS-RTK) sensor. An improved hybrid denoising method was developed to enhance the GNSS-RTK’s accuracy. The improved hybrid denoising method consists of the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), the detrended fluctuation analysis (DFA), and an improved wavelet threshold denoising method. The stability experiment demonstrated the superiority of the improved wavelet threshold denoising method in reducing the noise of the GNSS-RTK. A noisy simulation signal was created to assess the performance of the proposed method. Compared to the ICEEMDAN method and the CEEMDAN-WT method, the proposed method achieves lower RMSE and higher SNR. The signal obtained by the proposed method is similar to the original signal. Then, GNSS-RTK was used to monitor a bridge in maintenance and rehabilitation construction. The bridge monitoring experiment lasted for four hours. (Considering the space limitation of the article, only representative 600 s data is displayed in the paper.) The bridge is located in Tianjin, China. The original displacement ranges are −14.9~19.3 in the north–south direction; −26.9~24.7 in the east–west direction; and −46.7~52.3 in the vertical direction. The displacement ranges processed by the proposed method are −12.3~17.2 in the north–south direction; −24.6~24.1 in the east–west direction; and −46.7~51.1 in the vertical direction. The proposed method processed fewer displacements than the initial monitoring displacements. It indicates the proposed method reduces noise significantly when monitoring the bridge based on the GNSS-RTK sensor. The average sixth-order frequency from PSD is 1.0043 Hz. The difference between the PSD and FEA is only 0.99%. The sixth-order frequency from the PSD is similar to that from the FEA. The lower modes’ natural frequencies from the PSD are smaller than those from the FEA. It illustrates the fact that, during the repair process, the missing load-bearing rods made the bridge less stiff and strong. The smaller natural frequencies of the bridge, the complex construction environment, the diversity of workers’ operations, and some unforeseen circumstances occurring in the construction all bring risks to the safety of the bridge. We should pay more attention to the dynamic monitoring of the bridge during construction in order to understand the structural status in time to prevent accidents.