DEVELOPING TOPOGRAPHIC MAPS USING A SUB–METER ACCURACY GLOBAL POSITIONING RECEIVER

Real-time kinematic (RTK) global positioning system (GPS) receivers are being marketed for applications where accurate estimates of elevation are required. Because this technology is fairly expensive, the quality of these data should be considered with respect to that of USGS digital elevation DEMs (digital elevation models) that are freely available for most of the United States at the 30-m grid scale and for some areas on 10-m grids. The objective of this study was to compare terrain attributes calculated from USGS DEMs with those calculated from DEMs developed with precision RTK GPS measurements. At two locations in central Kentucky, intensive elevation measurements were collected with RTK GPS. The USGS and RTK data for these areas were interpolated to create 4-m DEMs for comparing terrain attributes. Correlations between the attributes developed from different DEMs were substantially larger for slope and aspect than for curvature, topographic wetness index, and stream power index values. Although there were large quantitative differences in terrain attribute values, the general spatial patterns were similar for the attributes calculated from the RTK and USGS DEMs. However, landscape features defined using DEMs developed with RTK data were more similar to those observed with 10- than 30-m USGS DEMs. For some course-scale applications, 30-m USGS DEMs may be adequate for identifying important landscape characteristics. However, the resolution of many of these features will be substantially better with 10-m USGS DEMs. However, quantitative estimates of terrain attributes at a specific location may be grossly incorrect. For situations where accuracy is essential, precision elevation surveys will be required.

Network-based Real Time Kinematic (NRTK) GPS positioning is considered to be a superior solution compared to the conventional single reference station based Real Time Kinematic (RTK) GPS positioning technique whose accuracy is highly affected by the distance dependent errors such as satellite orbital and atmospheric biases. NRTK GPS positioning uses raw measurements gathered from a network of Continuously Operating Reference Stations (CORS) in order to generate more reliable error models that can mitigate the distance dependent errors within the area covered by the CORS. This technique has been developed and tested considerably during recent years and the overall performance in terms of achievable accuracies, reliability and mobility is as good as or even better than can be achieved using the conventional RTK GPS positioning technique. Currently, there are several commercial NRTK services around the world. In the United Kingdom (UK), for instance, Leica Geosystems in partnership with Ordnance Survey has been offering a NRTK GPS service since 2006. This service is called SmartNet and it can provide continuous centimetric level of accuracy to its subscribers. However, NRTK GPS positioning is particularly constrained by wireless data link coverage, correction transmission delay and completeness, GPS signal availability, etc., which could downgrade the positioning quality of the NRTK results. The paper presents some preliminary testing results of an investigation of the SmartNet service from the end users' point of view. A snapshot of the service's performance was carried out as part of a recent PhD studentship jointly awarded by the UK's Engineering and Physical Sciences Research Council (EPSRC) and Leica Geosystems (UK) to conduct comprehensive research into NRTK GPS quality control measures at the Institute of Engineering Surveying and Space Geodesy (IESSG), the University of Nottingham. In order to evaluate the service's quality several static and kinematic tests were performed using the same type of equipment and in the same way that the SmartNet subscribers would have used it. Centimetric accuracy was generally attained during both static and kinematic tests. This high accuracy was only affected by some level of unavailability mainly caused by GPS signal blockage. Additionally, the influence of the number of satellites in view, dilution of precision (DOP) and age of corrections (AoC) over the accuracy and stability of the NRTK GPS solution was also investigated during this research and presented in the paper.

Stockpiles are accumulations of materials such as coal or ore stored at specific locations. Accurate stockpile volume measurement is crucial in the mining and logistics industries for inventory management and cost efficiency. Conventional methods like Real-Time Kinematic (RTK) GPS rely on surface coordinate measurements but require numerous points, especially for irregular-shaped objects. Newer technologies like Terrestrial Laser Scanners (TLS) offer high-precision alternatives by capturing thousands of points per second, expediting and enhancing the resolution of volume measurements. This study compares TLS and RTK GPS methods in measuring the volume of andesite rock stockpiles at PT Bara Tabang. This research uses a quantitative approach, involving six Ground Control Points (GCPs) measured using the Sokkia GRX2 RTK GPS on October 24, 2024. TLS generated over 8.6 million point cloud data filtered down to 35,197 points, while RTK GPS yielded 2,276 coordinates. Accuracy testing showed very small RMSE values (RMSEr: 0.008 m; RMSEz: 0.007 m), and both LE90 and CE90 demonstrated 90% confidence within a 0.012 m range. Volume calculation using Surpac software with the cut and fill method showed TLS produced a volume of 18,766 bcm (51,982 tons/m³), while RTK GPS resulted in 18,694 bcm (51,782 tons/m³), with a difference of 72 bcm or 0.211%.These results indicate that both methods offer acceptable accuracy; however, TLS provides greater data density, efficiency, and precision, particularly for complex or large-scale stockpile objects. Therefore, TLS is recommended for high-accuracy volume measurement in mining operations that require efficiency and detailed analysis.

This paper presents a comprehensive solution for distance estimation of the following vehicle solely based on visual data from a low-resolution monocular camera. To this end, a pair of vehicles were instrumented with real-time kinematic (RTK) GPS, and the lead vehicle was equipped with custom devices that recorded video of the following vehicle. Forty trials were recorded with a sedan as the following vehicle, and then the procedure was repeated with a pickup truck in the following position. Vehicle detection was then conducted by employing a deep-learning-based framework on the video footage. Finally, the outputs of the detection were used for following distance estimation. In this study, three main methods for distance estimation were considered and compared: linear regression model, pinhole model, and artificial neural network (ANN). RTK GPS was used as the ground truth for distance estimation. The output of this study can contribute to the methodological base for further understanding of driver following behavior with a long-term goal of reducing rear-end collisions.

RTK GPS mapping of transplanted row crops

Various corrector surface models are proposed to mitigate systematic height errors to fit the orthometric heights determined by combining the Tainan City real-time kinematic (RTK) global positioning system (GPS) network and the local geoid model data to the published orthometric heights. Several data sets for Tainan City were tested and analyzed. Two geometric geoid models and one gravimetric-geometric geoid model were generated using the GPS and leveling data. Consequently, three types of orthometric heights were determined (Models I, II, and III). The selection of the optimal corrector surface model for different models was based on a series of statistical tests. The test results show that (1) the selection of the optimal corrector surface model is highly related to the geoid model generating method, hence the optimal corrector surface models are a fifth-degree polynomial for Models I and II and a seven-parameter similarity transformation for Model III; and (2) the determined orthometric height ...

The potential use of a real-time kinematics (RTK) GPS receiver for seed mapping with a high level of accuracywas investigated. High-accuracy seed mapping can potentially be used in weed control and plant-specific crop management.A four-row Salvo 650 vacuum planter was retrofitted with an RTK GPS receiver. Four seed detector sensors (one per plantingunit) were mounted directly above the planter shoes. These sensors detected the seeds as they fell through seed tubes. Twolow-cost single-board computers were used to acquire data in real time and display it in the tractor cab. The first computerwas interfaced to the RTK GPS unit to determine seed location and forward speed. The second computer was interfaced toa display unit mounted in the cab. The first computer obtained the GPS time and UTM coordinates every second and storedthem with a reference time (time tag). This computer also monitored the seed detector sensors, time-tagged the seeds fromeach unit, and stored the information in memory. The second computer monitored the first computer and reported the plantersperformance through the display unit mounted in the tractor cab. Field tests were conducted to check the performance of thisplanter over two growing seasons. The first year test results indicated a need to control the sensitivity of the seed detectorsensors used for seed detection. The second year tests showed that the improved system performed very well. The differencesbetween the actual plant and the seed map generated by the RTK GPS based planter were in the range of 30 to 38 mm.

This paper presents an adaptive learning method for data fusion in autonomous\ndriving vehicles. The localization is based on the integration of Inertial\nMeasurement Unit (IMU) with two Real-Time Kinematic (RTK) Global Positioning\nSystem (GPS) units in an adaptive Kalman filter (KF). The observability\nanalysis reveals that $i$) integration of a single GPS with IMU does not\nconstitute an observable system; $ii$) integration of two GPS units with IMU\nresults in a locally observable system provided that the line connecting two\nGPS antennas is not parallel with the vector of the measured acceleration,\ni.e., the sum of inertial and gravitational accelerations. The latter case\nmakes it possible to compensate for the error in the estimated orientation due\nto gyro drift and its bias without needing additional instruments for absolute\norientation measurements, e.g., a magnetic compass. Moreover, in order to cope\nwith the fact that GPS systems sometimes lose their signal and receive\ninaccurate position data, the self-tuning filter estimates the covariance\nmatrix associated with the GPS measurement noise. This allows the KF to\nincorporate GPS measurements in the data fusion process heavily only when the\ninformation received by GPS becomes reliably available.\n

In this paper, we present a complete set of solutions to detect the location of the autonomous horticulture rover used for precision agriculture purposes during the early growth stage of the crops. The horticulture rover autonomously traverses the furrow by aligning itself with the centerline using the dynamic inversion navigation algorithm. The navigation algorithm takes the cross-track error and heading error of the rover deviating from the centerline as the location input, and outputs the desired left and right track speeds to the control system to correct the rover course. The rover has been using the real time kinematic (RTK) -based global position system (GPS) as the sensor to detect the location of the rover, but satellite signals can suffer from sporadic loss, and the geographical characteristics of the environment need to be accurately mapped beforehand. These limits of RTK GPS drastically increase the work load of the operators and decrease the location detection accuracy of the rover. The proposed easy-to-implement machine-vision based location detection solution set accepts the raw pictures taken by the camera mounted on the rover as input and calculate the cross-track and heading errors of the rover as outputs. It neither requires the usage of GPS, nor acquisition of environmental geographical information beforehand. Our test results indicate that the proposed machine-vision based solution set is able to outperform the RTK GPS by providing the cross-track location and heading information of the rover with an error less than 20% of that achieved by a RTK GPS, when the rover is within the furrow during the early growth stage.

Describes the Position and Orientation System for Marine Vessels (POS/MV), being used in multibeam and geophysical surveys around the world, and its performance using integrated inertial/DGPS and real-time kinematic (RTK) GPS data. POS/MV is a 3-axis position and orientation measurement system that implements a tightly coupled Kalman filter based integration of an inertial navigator and various forms of GPS. POS/MV's integrated method of measuring position and orientation has advantages over other approaches, particularly when GPS, DGPS or RTK GPS outages occur. The inertial navigator component acts as a flywheel through an outage, maintaining data continuity and accuracy. This paper describes the technology behind the POS/MV and presents results which demonstrate the POS/MV's position and orientation accuracy and ability to provide uninterrupted position and orientation data through DGPS, RTK GPS and GPS outages.

Tropospheric delay is a major error source that affects the performance of the Global Navigation Satellite Systems (GNSS) Real Time Kinematic (RTK) positioning especially for the medium/long-range baseline. Although high precision tropospheric delay can be estimated by GNSS carrier phase measurement, together with position and ambiguity, a relatively long period of convergence time is necessary. In this study, we develop a new GPS/BDS RTK algorithm constrained with a tropospheric delay parameters from Numerical weather prediction (NWP) model for medium/long-range baselines. The accuracy of the tropospheric delays derived from NWP is assessed through comparisons with the results of GAMIT (GNSS at MIT). The positioning performance with standard GPS RTK, standard GPS/BDS RTK, the developed NWP-constrained GPS RTK and NWP-constrained GPS/BDS RTK over medium/long-range baselines are compared in terms of the initialization time and the positioning accuracy. Experiment results show that the mean differences between the NWP and GAMIT zenith tropospheric delay (ZTD) are between −5.50 mm and 5.60 mm, and the RMS values of the NWP ZTD residuals are from 24.02 mm to 32.62 mm. A reduction in the initialization time of over 41% and 58% for medium- and long-range baselines can be achieved with the NWP-constrained RTK (both GPS alone and GPS/BDS RTK solutions) compared to the standard RTK solution, respectively. An improvement of over 30% can be found with the GPS/BDS RTK compared with that of the GPS alone RTK for both standard and the NWP-constrained modes. The positioning precision of NWP-constrained GPS/BDS RTK is better than 3 cm in the horizontal direction and better than 5 cm in the vertical direction, which satisfies the requirement of the precise positioning service.

This study investigates the optimisation of Unmanned Aerial Vehicle (UAV) photogrammetry and Global Positioning System Real-Time Kinematic (GPS-RTK) technologies for volume calculation in complex terrains, using the hillside of Universiti Teknologi MARA (UiTM) Perak as a case study. Although UAV photogrammetry is increasingly applied in topographic modelling, few studies have empirically compared its volumetric accuracy at different flight altitudes against GPS-RTK benchmarks in undulating landscapes. In this study, two UAV flight altitudes (80m and 100m) were evaluated to determine their impact on measurement precision, with results benchmarked against ground-truthed GPS RTK data. Processing through Pix4Dmapper and ArcGIS Pro revealed that UAV-based surveys achieved 91.87% accuracy at 80m compared to 87.57% at 100m, demonstrating the critical role of flight altitude in terrain modelling. The higher accuracy at lower altitudes is attributed to enhanced image resolution and improved capture of terrain details, despite marginally increased operational time. These findings position UAV technology as a viable, cost-efficient complement to traditional GPS RTK surveys, particularly for large-scale or inaccessible terrain mapping. For projects requiring sub-decimetre accuracy in undulating landscapes, an 80m flight altitude is recommended as the optimal balance between data quality and survey efficiency. This research provides empirically validated guidelines for integrating UAV photogrammetry into geospatial workflows across construction, environmental management, and land development sectors.

An adaptive turning algorithm for a four-wheel robot tractor in the headland is presented in this paper. The navigation sensors consisted of an inertial measurement unit and a real-time kinematic global positioning system (GPS). An objective function based on weights was used to create the navigation path, connecting by continuous primitives. The asymmetric steering mechanism was then taken into consideration with a vehicle model. To follow the path accurately, the slide movement of the robot and the steering rate were taken into account by estimating the turning radius in real time. In addition, the vehicle model was tuned based on the results of each turn. Therefore, the turning control algorithm was optimized on the basis of the specific conditions in the field. Field experiments showed that the robot tractor approached the next path with an average lateral deviation of 3.9 cm at a speed of 1.2 m/s during a turn. Compared to a conventional turning scheme, the time consumption and turning trajectory were decreased by 17% and 21%, respectively. Keywords: autonomous tractor, path planning, dynamic circle-back turning, switch-back turning, robot tractor, reinforcement learning DOI: 10.25165/j.ijabe.20181106.3605 Citation: Wang H, Noguchi N. Adaptive turning control for an agricultural robot tractor. Int J Agric & Biol Eng, 2018; 11(6): 113–119.

This paper demonstrates the performance of a fixed-wing unmanned aerial vehicle in mapping semi-undulated area. Current practices incur huge cost and time consuming. The aim of this study is to evaluate photogrammetric technique for mapping slope. Fixed-wing unmanned aerial vehicle (UAV) was employed as a tool for image acquisition at the height of 300m. This unit is a complete package that includes low cost digital camera, autonomous chip, global positioning system (GPS), gyro and electronic speed controller. This study was conducted at Skudai, Johor, which is located approximately at latitude 1°33'24.49N and longitude 103°39'4.30E. All the acquired images were processed using photogrammetric technique which comprise of aerial triangulation and bundle adjustment. There are two main photogrammetric products produced from this study known as digital orthophoto and digital elevation model (DEM). Both products were analyzed to determine accuracy of the results. Ground control points and check points were distributed randomly at the study area and all the points were established using GPS Real Time Kinematic (RTK) technique. Based on the results, it was found that root mean square error for slope analysis is +1.169m and length analysis is +0.259m.

This study assesses the thematic information content using subwatershed boundaries mapped using advanced spaceborne thermal emission and reflection (ASTER) global digital elevation model (GDEM), shuttle radar topography mission (SRTM) DEM and a topographic map (Topo-DEM), and verifies the absolute elevation accuracy by the use of a real-time kinematic differential global positioning system (RTK-DGPS) survey. Subwatershed boundaries extracted from the three different DEMs exhibit a high degree of congruency especially in upstream areas. In these areas, SRTM exhibits lower root mean square errors (RMSEs) than the ASTER GDEM. The discrepancies were larger at lower altitudes. The vertical accuracy assessment using the RTK-DGPS data showed strong correlation with the three DEMs at some stations but the accuracy varied from one area to another. The ASTER GDEM and SRTM had lower accuracy than the Topo-DEM due to the influence of artefacts as shown by their total average RMSE (4.42, 3.30 and 3.13 m, respectively). The average RMSE values also indicate that SRTM is comparatively more accurate than ASTER GDEM.