Real-time Kinematic Research Articles

Monitoring of networked RTK reference stations for coordinate reference system realization and maintenance – case study of the Czech Republic

Abstract Networked Real-Time Kinematic (NRTK) is the most widely used GNSS (Global Navigation Satellite Systems) positioning technique in surveying practice due to its speed, operability and sufficient accuracy. Positioning using NRTK methods in the European Terrestrial Reference System ETRS89 obligatory coordinate system is tied to the positioning of reference stations of the RTK network. Monitoring of GNSS reference stations provides both the determination of the position of individual stations in the obligatory coordinate system and the monitoring of daily position deviations of individual stations. The application to four networks of GNSS reference stations in the Czech Republic has demonstrated the accuracy of the positioning of stations from the daily solution at the level of 1.2 mm in the horizontal components and 3.8 mm in the vertical component. This accuracy is quite suitable for NRTK methods that determine position with centimetre accuracy. At the same time, it allows to detect the behaviour of time series of coordinates of individual stations – sudden jumps, linear and periodic trends. The accuracy achieved also allows tracking geokinematic movements.

Ensuring High Integrity of GNSS Precise Orbit and Clock Products Based on Performance-Assured Sliding-Window Innovation Test

Precise Point Positioning – Real-Time Kinematic (PPP-RTK) can offer fast and accurate positioning services by employing raw measurements from Global Navigation Satellite Systems (GNSS) along with precise satellite orbit, clock, bias, and atmospheric products. Incorrect orbit and clock products may threaten the generation of satellite bias and atmospheric products, leading to hazardously misleading information at the user end. Therefore, their integrity monitoring is of great importance for achieving safety-assured PPP-RTK. There had been a few studies focusing on fault detection of precise orbit and clock products. However, they paid little attention to theoretically quantify the integrity performance of the detectors, thereby being unable to provide reliable integrity support message and be applied in Protection Level (PL) calculation. In response, a dedicated sliding-window innovation-based integrity monitoring detector is designed for precise orbit and clock products, whose integrity performance can be guaranteed. To achieve this, innovation-based test statistics utilized in this paper is constructed step by step from raw measurement equations. After analyzing the drawback of performance degradation using snapshot-based detection method, the sliding-window innovation-based detector is designed to detect the faults in orbit and clock products, with a special focus on small ramp errors. Then, the theoretical performance of the designed detector considering the requirement of integrity is analytically derived. The theoretical optimal window length is also analyzed. Simulations are carried out for performance verification. Comparing to snapshot-based method, sliding window method has better performance on ramp fault detection, even with very short window length.

Assessing the impact of overhead agrivoltaic systems on GNSS signal performance for precision agriculture

Agrivoltaic systems, which integrate solar energy production with agricultural practices, present a sustainable solution to optimize land use. However, specific configurations, such as overhead solar panels, can obstruct Global Navigation Satellite System (GNSS) signals, which are vital for precision agriculture technologies. This study investigates the impact of an agrivoltaic system on GNSS signal performance compared to a conventional orchard in Kressbronn am Bodensee, Germany, using data from multiple GNSS constellations (GPS, GLONASS, Galileo, BeiDou). Key metrics such as carrier-to-noise density ratio (C/N₀), positional accuracy, time to first fix (TTFF), and dilution of precision (DOP) were analyzed. The results showed significant signal degradation under the overhead agrivoltaic system, particularly in C/N₀ and positional accuracy. While there were no significant differences in DOP metrics or TTFF, the average C/N₀ decreased from 30.62 dB-Hz in the conventional orchard to 26.92 dB-Hz in the agrivoltaic system. Moreover, the average of satellites with C/N₀ values below 24 dB-Hz was notably higher in the agrivoltaic area, indicating critical signal degradation. In addition, relying on a single constellation, like GPS, may not provide enough satellites for reliable GNSS performance.Despite the lower signal quality, the average number of satellites with C/N₀ above 24 dB-Hz remained over 22 in both areas. Combined with slight or no significant differences in other metrics compared to the conventional orchard, this suggests GNSS-based systems can support precision agriculture tasks within the agrivoltaic environment. The study highlights the importance of addressing signal interference to avoid hindering the integration of drones, robots, and other advanced technologies in agrivoltaics management, and suggests alternative solutions, such as Real-Time Kinematic (RTK) correction or Simultaneous Localization and Mapping (SLAM) techniques, to enhance performance. These findings are critical as agrivoltaic adoption grows, requiring a balance between solar energy production and the operational needs of precision agriculture.

Differential Positioning with Bluetooth Low Energy (BLE) Beacons for UAS Indoor Operations: Analysis and Results.

Localization of unmanned aircraft systems (UASs) in indoor scenarios and GNSS-denied environments is a difficult problem, particularly in dynamic scenarios where traditional on-board equipment (such as LiDAR, radar, sonar, camera) may fail. In the framework of autonomous UAS missions, precise feedback on real-time aircraft position is very important, and several technologies alternative to GNSS-based approaches for UAS positioning in indoor navigation have been recently explored. In this paper, we propose a low-cost IPS for UAVs, based on Bluetooth low energy (BLE) beacons, which exploits the RSSI (received signal strength indicator) for distance estimation and positioning. Distance information from measured RSSI values can be degraded by multipath, reflection, and fading that cause unpredictable variability of the RSSI and may lead to poor-quality measurements. To enhance the accuracy of the position estimation, this work applies a differential distance correction (DDC) technique, similar to differential GNSS (DGNSS) and real-time kinematic (RTK) positioning. The method uses differential information from a reference station positioned at known coordinates to correct the position of the rover station. A mathematical model was established to analyze the relation between the RSSI and the distance from Bluetooth devices (Eddystone BLE beacons) placed in the indoor operation field. The master reference station was a Raspberry Pi 4 model B, and the rover (unknown target) was an Arduino Nano 33 BLE microcontroller, which was mounted on-board a UAV. Position estimation was achieved by trilateration, and the extended Kalman filter (EKF) was applied, considering the nonlinear propriety of beacon signals to correct data from noise, drift, and bias errors. Experimental results and system performance analysis show the feasibility of this methodology, as well as the reduction of position uncertainty obtained by the DCC technique.

Testing of Shoreline Erosion Monitoring Methodologies for Heritage at Risk Sites: Pockoy Island, South Carolina, USA

Abstract We review shoreline monitoring methodologies used by members of the South Carolina Department of Natural Resources (SCDNR) Archaeology, Geology, and Wildlife Biology teams from February 2021 to December 2022 on Pockoy Island in Charleston County, South Carolina, USA. Our project objectives were to better understand the driving forces behind the landward movement of the shoreline (transgression), to apply new understanding to the rate of shoreline erosion of the island that directly impacts the Pockoy Island Shell Ring Complex (38CH2533), and to establish best practice for future community science monitoring efforts. Each member of our team used a different shoreline monitoring methodology (a nested methodology approach). Multiple unoccupied aerial vehicle (UAV)-derived orthoimagery datasets, on-the-ground transect measurements, and Arrow Gold real-time kinematic (RTK) unit measurements have been collected monthly following significant storms or king (perigean) tide events. Moving forward, the erosion transect approach tested within this project will serve as the foundation for community science monitoring at heritage at-risk sites in South Carolina. In this article, we introduce initial efforts in establishing a community science monitoring program in South Carolina that will influence future research, land management, and policy, and we propose how our research might be adapted for other sites at risk.

Is it possible to generate accurate 3D point clouds with UAS-LIDAR and UAS-RGB photogrammetry without GCPs? A case study on a beach and rocky cliff

ContextRecently, Unoccupied Aerial Systems (UAS) with photographic or Light Detection and Ranging (LIDAR) sensors have incorporated on-board survey-grade Global Navigation Satellite Systems that allow the direct georeferencing of the resulting datasets without Ground Control Points either in Real-Time (RTK) or Post-Processing Kinematic (PPK) modes. These approaches can be useful in hard-to-reach or hazardous areas. However, the resulting 3D models have not been widely tested, as previous studies tend to evaluate only a few points and conclude that systematic errors can be found.ObjectivesWe test the absolute positional accuracy of point clouds produced using UAS with direct-georeferencing systems.MethodsWe test the accuracy and characteristics of point clouds produced using a UAS-LIDAR (with PPK) and a UAS-RGB (Structure-from-Motion or SfM photogrammetry with RTK and PPK) in a challenging environment: a coastline with a composite beach and cliff. The resulting models of each processing were tested using as a benchmark a point cloud surveyed simultaneously by a Terrestrial Laser Scanner.ResultsThe UAS-LIDAR produced the most accurate point cloud, with homogeneous cover and no noise. The systematic bias previously observed in the UAS-RGB RTK approaches are minimized using oblique images. The accuracy observed across the different surveyed landforms varied significantly.ConclusionsThe UAS-LIDAR and UAS-RGB with PPK produced unbiased point clouds, being the latter the most cost-effective method. For the other direct georeferencing systems/approaches, the acquisition of GCP or the co-registration of the resulting point cloud is still necessary.

Landslide deformation velocity real-time monitoring based on time-differenced carrier phase and its fault detection method

GNSS technologies such as Real-Time Kinematic (RTK) and Precise Point Positioning (PPP) have become indispensable for landslide monitoring. However, RTK relies on stable reference stations and robust communication network, rendering it unusable without reference station data or communication technology support. Real-time PPP technology provides decimeter level monitoring accuracy and suffers from long convergence times, failing to meet the real-time and reliable monitoring requirements during landslide destabilization phase. This paper presents the first study to investigate the application of time-differenced carrier phase (TDCP) technology for real-time monitoring of landslide deformation velocity. An adaptive fault detection method is proposed, which combines prior closure error outlier detection with the chi-squared residual-based fault detection and exclusion. Unlike traditional methods that require iteratively recalculating solutions with each satellite exclusion, our approach significantly reduces computational time. The effectiveness of the proposed method has been validated through two sets of real measurement data. The results demonstrate that TDCP technology enables real-time monitoring of deformation velocity at the millimeter level, and the proposed adaptive fault detection method effectively identifies measurement anomalies in complex environments. In static complex environments, the standard deviations of the GPS/BDS/GLONASS/Galileo combined solution in the E, N, and U directions are 1.7 mm/s, 1.7 mm/s, and 3.9 mm/s, respectively. During rapid sliding in open environments, the root mean square of the differences between TDCP and RTK monitoring results are 2.2 mm/s, 3.3 mm/s, and 4.6 mm/s, respectively.

Real-Time IMU-Based Kinematics in the Presence of Wireless Data Drop.

Wireless inertial motion capture holds promise for real-time human-machine interfaces and home-based rehabilitation applications. However, wireless data drop can cause significant estimation errors deteriorating performance or even making the system unusable. It is currently unclear how to estimate non-periodic kinematics with wearable inertial measurement units (IMUs) in the presence of wireless data drop (packet loss). We thus propose a novel inference encoder-decoder network model for real-time kinematics during dynamic movement. Twenty-four healthy subjects performed yoga, golf, swimming, dance, and badminton movement activities while wearing IMUs and 10-90% of each IMU's data were randomly removed to determine the effects of data drop on estimation accuracy with and without the proposed model. Results demonstrated a reduction in RMSE of 45.2% to 51.5% in the upper limb kinematic estimation of the proposed model compared to the No Prediction strategy, and a reduction of 19.1% to 31.3% of the proposed model compared with an baseline LSTM model. In addition, the proposed model has significantly less error (p<0.05) than the No Prediction strategy and the baseline LSTM model for 10%, 20%, 30%, 40%, 50%, 60%, 70%, and 80% data drop. These results could enable wearable, wireless IMU dynamic motion analysis and assessment with reduced kinematic estimation error in the presence of varying amounts of wireless data drop and thus could further facilitate human-machine interaction and home-based medical assessment and treatment.

The Accuracy of RTK enabled Mobile Phones in RTK denied Areas

Abstract. Surveyors often encounter challenges when tasked with measuring points in regions where buildings disrupt satellite visibility. The precise surveying of points in these areas typically relies on employing total stations. This method effectively extends high precision from unaffected zones (where satellite visibility is assured) to those areas where disruption occurs due to buildings. Total stations achieve this by executing distance and angle measurements from a reference point coordinate to the location where satellite visibility is compromised. The process is notably time-consuming, thereby incurring significant costs. Moreover, the intricate nature of this surveying process necessitates specialized skills and expertise, contributing further to the overall expense. In this study, we investigate an approach based on RTK (Real-Time Kinematic) enabled mobile phones and photogrammetry to achieve the same. In principle, this approach has a similar philosophy. However, instead of working with a few points with good satellite visibility that are obtained by a GNSS (Global Navigation Satellite System) rover, the procedure works with hundreds of observations with varying accuracy. Those accuracies depend on the satellite visibility. Each observation captures an image from the mobile phone together with the GNSS signal and its accuracy. Angle and distance measurements that traditionally are observed by a total station are retrieved by photogrammetry on the total image collection.

Estimation of Surface Water Level in Coal Mining Subsidence Area with GNSS RTK and GNSS-IR

Ground subsidence caused by underground coalmining result in the formation of ponding water on the ground surface. Monitoring the surface water level is crucial for studying the hydrologic cycle in mining areas. In this paper, we propose a combined technique using Global Navigation Satellite System Real-Time Kinematic (GNSS RTK) and GNSS Interferometric Reflectometry (GNSS-IR) to estimate the surface water level in areas of ground subsidence caused by underground coal mining. GNSS RTK is used to measure the geodetic height of the GNSS antenna, which is then converted into the normal height using the local height anomaly model. GNSS-IR is employed to estimate the height from the water surface to the GNSS antenna (or, the reflector height). To enhance the accuracy of the reflector height estimation, a weighted average model has been developed. This model is based on the coefficient of determination of the signal fitted by the Lomb-Scargle spectrogram and can be utilized to combine the reflector height estimations derived from multiple GNSS system and band reflection signals. By subtracting the GNSS-IR reflector height from the GNSS RTK-based normal height, the proposed method-based surface water level estimation can be obtained. In an experimental campaign, a low-cost GNSS receiver was utilized for the collection of dual-frequency observations over a period of 60 days. The collected GNSS observations were used to test the method presented in this paper. The experimental campaign demonstrates a good agreement between the surface water level estimations derived from the method presented in this paper and the reference observations.

Sky-GVIO: Enhanced GNSS/INS/Vision Navigation with FCN-Based Sky Segmentation in Urban Canyon

Accurate, continuous, and reliable positioning is critical to achieving autonomous driving. However, in complex urban canyon environments, the vulnerability of stand-alone sensors and non-line-of-sight (NLOS) caused by high buildings, trees, and elevated structures seriously affect positioning results. To address these challenges, a sky-view image segmentation algorithm based on a fully convolutional network (FCN) is proposed for NLOS detection in global navigation satellite systems (GNSSs). Building upon this, a novel NLOS detection and mitigation algorithm (named S−NDM) uses a tightly coupled GNSS, inertial measurement units (IMUs), and a visual feature system called Sky−GVIO with the aim of achieving continuous and accurate positioning in urban canyon environments. Furthermore, the system combines single-point positioning (SPP) with real-time kinematic (RTK) methodologies to bolster its operational versatility and resilience. In urban canyon environments, the positioning performance of the S−NDM algorithm proposed in this paper is evaluated under different tightly coupled SPP−related and RTK−related models. The results exhibit that the Sky−GVIO system achieves meter-level accuracy under the SPP mode and sub-decimeter precision with RTK positioning, surpassing the performance of GNSS/INS/Vision frameworks devoid of S−NDM. Additionally, the sky-view image dataset, inclusive of training and evaluation subsets, has been made publicly accessible for scholarly exploration.

Mitigating ionospheric scintillation effects on RTK-based long-span bridge deformation monitoring

Ionospheric scintillation presents significant challenges to the application of Global Navigation Satellite System (GNSS) for positioning and bridge deformation monitoring in low-latitude region. This study aims to develop a GNSS positioning model to improve long-span bridge deformation monitoring performance under ionospheric scintillation. We propose a new Rate of Total Electron Content (ROT) calculation method using undifferenced estimated ionosphere delays for identifying ionospheric scintillation. Then, we construct an undifferenced and uncombined (UDUC) ionosphere-refined Real-time Kinematic (RTK) model by replacing ionospheric spatial constraints with temporal constraints to mitigate the impact of ionospheric scintillation. RTK experiments are conducted on the South China Sea Bridge (SCSB), spanning 55 km. The results indicate that the new ROT calculation method can effectively identify observations affected by ionospheric scintillation, and the ionosphere-refined RTK model shows superior performance compared to ionosphere-float and ionosphere-weighted RTK model in terms of convergence time, Time to First Fix (TTFF), and positioning accuracy.

EMSA-IK: a real-time evolutionary multi-objective semi-analytical inverse kinematics algorithm for redundant manipulators

Purpose This paper aims to propose a real-time evolutionary multi-objective semi-analytical inverse kinematics (IK) algorithm (EMSA-IK) for solving the multi-objective IK of redundant manipulators. Design/methodology/approach Within EMSA-IK, the parameterization method is applied to reduce the number of optimization variables of the evolutionary algorithm and calculate semi-analytical solutions that meet high target pose accuracy. The original evolutionary algorithm is improved with the proposed adaptive search sub-space strategy so that the improved evolutionary algorithm can be used to efficiently perform global search within the parametric joint space to obtain the global optimal parametric joint angles that satisfy multi-objective constraints. Findings Ablation experiments show the effectiveness of the improved strategy used for evolutionary algorithms. Comparative experiments on different manipulators demonstrate the advantages of EMSA-IK in terms of generalizability and balancing multiple objectives, for example, motion continuity, joint limits and obstacle avoidance. Real-world experiments further validate the effectiveness of the proposed algorithm for real-time application. Originality/value The semi-analytical IK solution that simultaneously satisfies high target pose accuracy and multi-objective constraints can be obtained in real time. Compared to existing semi-analytical IK algorithms, the proposed algorithm achieves obstacle avoidance for the first time. The proposed algorithm demonstrates superior generalizability, applicable to not only redundant manipulators with revolute joints but also those with prismatic joints.

Remote marine precise point positioning with baseline length and troposphere-constrained models of the receivers for the oceanographic research vessel

ABSTRACT Research vessels are typically equipped with multiple receivers for positioning and attitude measurement; however, there is no data interaction or model fusion to implement the Precise Point Positioning (PPP) for these receivers. Therefore, a Constrained PPP (CPPP) is established by using the baseline length and tropospheric constraints of two receivers to improve the vessel’s remote marine positioning performance. Based on two integrated Real-Time Kinematic (RTK) receivers, marine experiments are conducted at a distance of 300–600 km from land. Compared to the ground environment, the Root Mean Square error (RMS) of the multipath and the STandard Deviation (STD) of the carrier-to-noise ratio in the marine environment are increased by 2.87 and 2.6 times, respectively. The length constraints reduce the RMS of positioning in the East, North, and Up (ENU) directions by about 0.053 (67%), 0.020 (34%), and 0.054 m (34%), respectively, and also rapidly recover positioning after interruptions, achieving positioning with errors in the ENU-directions of 10, 15, and 20 cm, respectively, within 3 minutes. When the baseline length and tropospheric constraints are combined, with or without the addition of the Zenith Wet Delay bias (dZWD), the average STD can be reduced by about 0.060 m (43%) and the accuracy of the up-positioning can be significantly improved. The correlation of dZWD to the up-positioning deviation of du=−2.161*dZWD is obtained by simulation. Since the acquisition of dZWD depends on the PPP accuracy, it is recommended that dZWD be set to compensate for unmodeled receiver errors when the STD of the up-positioning is superior to 0.06 m.

Thermal Infrared Orthophoto Geometry Correction Using RGB Orthophoto for Unmanned Aerial Vehicle

The geometric correction of thermal infrared (TIR) orthophotos generated by unmanned aerial vehicles (UAVs) presents significant challenges due to low resolution and the difficulty of identifying ground control points (GCPs). This study addresses the limitations of real-time kinematic (RTK) UAV data acquisition, such as network instability and the inability to detect GCPs in TIR images, by proposing a method that utilizes RGB orthophotos as a reference for geometric correction. The accelerated-KAZE (AKAZE) method was applied to extract feature points between RGB and TIR orthophotos, integrating binary descriptors and absolute coordinate-based matching techniques. Geometric correction results demonstrated a significant improvement in regions with stable and changing environmental conditions. Invariant regions exhibited an accuracy of 0.7~2 px (0.01~0.04), while areas with temporal and spatial changes saw corrections within 5~7 px (0.10~0.14 m). This method reduces reliance on GCP measurements and provides an effective supplementary technique for cases where GCP detection is limited or unavailable. Additionally, this approach enhances time and economic efficiency, offering a reliable alternative for precise orthophoto generation across various sensor data.

Are Modern Market-Available Multi-Rotor Drones Ready to Automatically Inspect Industrial Facilities?

Industrial inspection is a well-known application area for unmanned aerial vehicles (UAVs), but are modern market-available drones fully suitable for inspections of larger-scale industrial facilities? This review summarizes the pros and cons of aerial large-scale facility inspection, distinguishing it from other inspection scenarios implemented with drones. Moreover, based on paper analysis and additionally performed experimental studies, it reveals specific issues related to modern commercial drone software and demonstrates that market-available UAVs (including DJI and Autel Robotics) more or less suffer from the same problems. The discovered issues include a Global Navigation Satellite System (GNSS) Real Time Kinematic (RTK) shift, an identification of multiple images captured from the same point, limitations of custom mission generation with external tools and mission length, an incorrect flight time prediction, an unpredictable time of reaching a waypoint with a small radius, deviation from the pre-planned route line between two waypoints, a high pitch angle during acceleration/deceleration, an automatic landing cancellation in a strong wind, and flight monitoring issues related to ground station software. Finally, on the basis of the paper review, we propose solutions to these issues, which helped us overcome them during the first autonomous inspection of a 2400 megawatts thermal power plant.

Performance Evaluation of Real-Time Kinematic Global Navigation Satellite System with Survey-Grade Receivers and Short Observation Times in Forested Areas

The Real-Time Kinematic (RTK) method is currently the most widely used method for positioning using Global Navigation Satellite Systems (GNSSs) due to its accuracy, efficiency and ease of use. In forestry, position is a critical factor for numerous applications, with GNSS currently being the preferred solution for obtaining such data. However, the decreased performance of GNSS observations in challenging environments, such as under the forest canopy, must be considered. This paper analyzes the performance of a survey-grade GNSS receiver under coniferous/deciduous tree cover. Unlike most previous research concerning this topic, the focus here is on employing a methodology that is as close as possible to real working conditions in the field of forestry. To achieve this, short observation times of 30 s were used, with corrections received directly in the field from a Continuously Operating Reference Station (CORS) of the national RTK network in Romania. In total, 84 test points were determined, randomly distributed under the canopy, with reference data collected by topographical surveys using total station equipment. In terms of the overall horizontal accuracy, an RMSE of 2.03 m and MAE of 1.63 m are found. Meanwhile, the overall vertical accuracy is lower, as expected, with an RMSE of 4.85 m and MAE of 4.01 m. The variation in GNSS performance under the different forest compositions was found to be statistically significant, while GNSS-specific factors such as DOP values only influenced the precision and not the accuracy of observations. We established that this methodology offers sufficient accuracy, which is application-dependent, even if the majority of GNSS solutions were code-based, rather than carrier-phase-based, due to strong interference from the vegetation.

Intra- and interlimb effects of gait retraining in individuals with knee hyperextension

BackgroundGait retraining, which typically focuses on the most severely affected limb or joint, has shown promising results in treating faulty running and walking patterns. The closed-chain nature of gait during the stance phase may influence kinematic changes in the adjacent joints of the trained leg. In addition, the coupled nature of the lower extremity motion of gait suggests that changes in one leg may transfer to the other. This study aimed to assess the intra- and inter-limb transfer of kinematic changes following gait retraining to reduce knee extension in individuals with hyperextension walking patterns. MethodsSeventeen women with knee hyperextension gait patterns participated in six treadmill retraining sessions. All participants received verbal and real-time visual kinematic feedback in the form of knowledge of results. This intervention study took place at the Gait Analysis Laboratory at the University of Iowa. Mean peak sagittal-plane lower extremity joint kinematics during overground walking at pretraining, post-training, and 1- and 8-month follow-ups were calculated for analysis and comparisons. FindingsThe post-training changes in ankle range of motion returned to baseline values by the 8-month follow-up. There was a significant transfer effect of kinematic changes to the untrained knee following gait retraining. InterpretationTraining one knee did not result in long-term compensatory kinematic changes in the other joints. In addition, the improvements in knee extension range of motion were transferred to the untrained knee and retained at the 8-month follow-up. This study supports the use of gait retraining as an effective clinical intervention.

Accuracy assessment of UAV-based documentation of archaeological site: Kültepe-Kaneš

High quality 2D/3D metric documentation of archaeological sites is used in many different applications such as archaeological-heritage management, digital archaeological excavation planning, orthophotographic documentation, and ancient Digital Surfaces Models (DSM) research. Structure-from-Motion based photogrammetry (SfM) is an indirect geodesic measurement technique based on multiple view image processing using high-level automation techniques. Using SfM to generate the high accuracy 2D/3D data needed for digital documentation of archaeological cultural heritage is relatively practical, economical, and fast. Factors such as the number of ground control points and their distribution to the study area, the robustness of the camera system model parameters, and the analytical model of the photogrammetric triangulation method used affect the accuracy of the data produced using SfM-photogrammetry. Kültepe-Kaneš, the center of the ancient Kingdom of Kaneš, which is located in central-Anatolia, was an Assyrian Trade Colony active between ca. 2000 BC and 1700 BC. The site, which was the center of the Old Assyrian trade colonies in Anatolia and one of the most important sites in ancient history, has been under excavation for the last 75 years. In this paper, high accuracy digital documentation of the Kültepe-Kaneš archaeological excavation area was made using different SfM methods. Statistical analysis revealed no significant difference in accuracy between Real-Time Kinematic (RTK) and Post-Process Kinematic (PPK) methods for the study area. This suggests that either PPK, RTK, or aerial triangulation with Ground Control Points (GCPs) can be employed to generate orthophotos and DSMs for archaeological sites with relatively flat surface areas, as long as a sufficient number of uniformly distributed GCPs are used.

Development of a Robotic Platform with Autonomous Navigation System for Agriculture

The development of autonomous agricultural robots using a global navigation satellite system aided by real-time kinematics and an inertial measurement unit for position and orientation determination must address the accuracy, reliability, and cost of these components. This study aims to develop and evaluate a robotic platform with autonomous navigation using low-cost components. A navigation algorithm was developed based on the kinematics of a differential vehicle, combined with a proportional and integral steering controller that followed a point-to-point route until the desired route was completed. Two route mapping methods were tested. The performance of the platform control algorithm was evaluated by following a predefined route and calculating metrics such as the maximum cross-track error, mean absolute error, standard deviation of the error, and root mean squared error. The strategy of planning routes with closer waypoints reduces cross-track errors. The results showed that when adopting waypoints every 3 m, better performance was obtained compared to waypoints only at the vertices, with maximum cross-track error being 44.4% lower, MAE 64.1% lower, SD 39.4% lower, and RMSE 52.5% lower. This study demonstrates the feasibility of developing autonomous agricultural robots with low-cost components and highlights the importance of careful route planning to optimize navigation accuracy.