Journal of Surveying Engineering

Site displacements caused by ocean tide loading (OTL) can reach more than several centimeters vertically, particularly in coastal regions. An ocean tide model typically is used to correct OTL effects, but these model predictions cannot satisfy accuracy requirements because of irregularities. The Global Navigation Satellite System (GNSS) is influenced by OTL and provides a way to measure OTL displacements precisely. This study develops an improved method for estimating OTL-displacement parameters in which the effects of three-dimensional (3D) OTL displacements are estimated as unknown parameters at each epoch by precise point positioning (PPP); then, the amplitudes and phase lags of eight principal constituents are extracted from the time series of the OTL displacements using the harmonic analysis method. 3D OTL displacements of eight constituents at 12 sites in Hong Kong with 8 years of continuous GNSS observations are calculated. The convergence speed of these constituents indicates that the O1, Q1, N2, and M2 constituents are much faster than the K1, P1, K2, and S2 constituents. In addition, the PPP-derived OTL estimates are compared with the predictions from six advanced global ocean tide models, which were modified by the osu.chinasea.2010 local tide model. The results show that the constituents estimated by the improved method are most consistent with the model-predicted values. RMS misfits between the PPP model are small for the N2, M2, Q1, and P1 constituents (less than 1.5 mm in the east component, 2.0 mm in the north component, and 1.9 mm in the vertical component). The S2 and K1 constituents show relatively large misfits of up to 4.3 mm.

The concept of real-time kinematic precise point positioning (PPP-RTK) is to achieve integer ambiguity resolution (IAR) at a single global navigation satellite system (GNSS) user by providing network-derived satellite phase biases (SPBs) in addition to the standard PPP corrections. The integerness of the user ambiguities gets recovered and resolved, obtaining high-precision position solutions with the aid of the precise carrier-phase observables. Most of current PPP-RTK methods focus on processing dual-frequency or multifrequency GNSS network observations. The new developing Indian regional navigation satellite system (IRNSS), however, provides only a single-frequency signal in L-band, and shares the L5 frequency with the American global positioning system (GPS), the European Galileo, and the Japanese quasi-zenith satellite system (QZSS). This contribution proposes a new array-aided state-space RTK (SS-RTK) method following the concept of PPP-RTK, which is applicable to the single-frequency network data processing. A small array of multi-GNSS stations, separated by a few meters, takes the role of reference station to provide a batch of single-frequency RTK corrections. Similar to PPP-RTK, the single-frequency stand-alone user ambiguities are a double-differenced (DD) form after applying the SS-RTK corrections. Based on the proposed array-aided SS-RTK concept, the authors analyze the capability of the single-receiver positioning with IAR using L5/E5a frequency observations from IRNSS, as well as GPS, Galileo, and QZSS. Results from real-data experiments demonstrate that even though stand-alone RTK using current IRNSS satellites is not yet possible, they effectively contribute to the tightly integrated multisystem RTK. By increasing the number of antennas in the array used for SS-RTK corrections, the user would achieve more precise and reliable positions. After increasing the dimension of the array to four antennas, a 4–10% improvement in the IAR success rate is experienced. Moreover, the convergence time of the float solutions, reaching a subdecimeter precision level, reduces from 30 to 40 min (single-antenna array) to about 20 min (four-antenna array).

Cooperative networks of low-cost unmanned aerial vehicles (UAVs) are attracting researchers because of their potential to enhance UAV performance. Cooperative networks can be used in many applications, including assisted guidance and navigation, surveillance, search and rescue, disaster management, defense, mapping, precision agriculture, and mineral exploration. Such cooperative networks of UAVs can act as ad hoc networks and share information among different network nodes. Such information sharing makes these nodes more robust and efficient for the intended purpose. The location of UAVs is traditionally determined using a global navigation satellite system (GNSS), which limits the use of UAVs in regions that lack GNSS. However, the location of UAVs can be determined even in environments without GNSS through a cooperative network if a few of the nodes have access to GNSS. This is achieved by sharing the information among the nodes of the network. Information sharing in a cooperative network further results in improvement in the proportional accuracy of the nodes in cases where GNSS is available to all nodes. This study investigated a mathematical model and operational framework for cooperative localization of UAVs using GNSS, microelectromechanical systems (MEMS), inertial navigation system (INS), and UWB (ultra-wide-band) sensors under different architectures. This paper briefly discusses the practical feasibility of different distributed architectures and provides a comparison of distributed and centralized architectures. The study analyzed the proposed network using numerical simulation and investigated changes in performance with respect to different parameters. The simulation results show that the centralized architecture generally provided higher localization accuracy compared with the distributed architecture. It was also observed that reliable and consistent localization can be achieved, irrespective of the size of the network, by using a cooperative approach even if only four nodes have GNSS access in the network if there is good connectivity among the nodes. Further, the simulation results demonstrate that a cooperative approach benefits all the nodes in terms of improved localization accuracy even if all the nodes have access to GNSS.

Terrestrial laser-scanning (TLS) techniques have been proven to be efficient tools for collecting three-dimensional high-density and high-accuracy point clouds for coastal research and resource management. However, TLS collects a massive number of surveying points. The processing and presenting of the large volumes of data sets is always a challenge for research when targeting a large area with high resolution. This article introduces a practical workflow using shell-scripting techniques to chain together tools from the Generic Mapping Tools (GMT), Geographic Resources Analysis Support System (GRASS), and other command-based open-source utilities for automating TLS data processing. TLS point clouds acquired in the beach and dune area near Freeport, Texas, in May 2015 were used for the case study. GMT is an open-source collection of programs designed for manipulating and displaying geographic data sets. Shell scripts for rotating the coordinate system, removing anomalous points, assessing data quality, generating high-accuracy bare-earth digital elevation models (DEMs), and quantifying changes of beach and sand dune features (shoreline, cross-shore section, dune ridge, toe, and volume) are presented in this article. This investigation indicated that GMT provides efficient and robust programs for regridding and filtering massive TLS point-cloud data sets, generating and displaying high-resolution DEMs, and, finally, producing publication-quality maps and graphs. The methods and scripts presented in this article will benefit a large research and application community of geomorphologists, geologists, geophysicists, engineers, and others who need to handle large volumes of topographic data sets and generate high-resolution DEMs.

Small-sized unmanned aircraft systems (UAS) are restricted to use only lightweight microelectromechanical systems (MEMS)-based inertial measurement units (IMUs) due to their limited payload capacity. Still, some UAS-based geospatial remote sensing applications, such as airborne spectroscopy or laser scanning, require high accuracy pose (position and orientation) determination of the onboard sensor payload. This study presents ground-based experiments investigating the pose accuracy of two MEMS-based IMUs: the single-antenna MTi-G-700 (Xsens, Enschede, Netherlands) and the dual-antenna/dual-frequency Spatial Dual IMU (Advanced Navigation, Sydney, Australia)/global navigation satellite system (GNSS). A tightly coupled and postprocessed pose solution from a fiberoptic gyroscope (FOG)-based NovAtel synchronized position attitude navigation (SPAN) IMU (NovAtel, Calgary, Canada) served as a reference to evaluate the performance of the two IMUs under investigation. Results revealed a better position solution for the Spatial Dual, and the MTi-G-700 achieved a better roll/pitch accuracy. Most importantly, the heading solution from the dual-antenna configuration of the Spatial Dual was found to be more stable than the heading obtained with the reference SPAN IMU.

During the last few years, the acquisition of geometric information about objects by using a moving sensor platform has gained increasing popularity in the surveying community. A large number of companies offer vehicle-based mobile mapping systems, which usually contain a fast profile laser scanner, a high-precision inertial measurement unit, and geodetic global navigation satellite system (GNSS) receivers to directly georeference the laser scans and build a consistent point cloud of the object. In contrast, there is a growing number of companies offering small and lightweight unmanned aerial vehicles (UAVs), which automatically fly over the area of interest and create dense point clouds of the environment by using camera images and photogrammetric-processing software. In this case, the georeferencing is usually realized by ground control points. This contribution summarizes activities performed by the authors to bring both fields closer together by developing a small (11 × 10 × 5 cm) and lightweight (240-g) direct-georeferencing unit, which is able to provide accurate position (<5 cm) and orientation ($<1°$) information in real time. The authors describe the development of the sensor unit and the sensor-fusion algorithms, address the topics of calibration and accuracy evaluation, and provide an overview of different applications in which the unit has been already used. These include the high-resolution acquisition of crop-surface models by using UAV-based imagery, the direct georeferencing of an autonomously flying robot with a laser scanner and multiple cameras, and the generation of laser point clouds using a human-carried mobile mapping system.

Ultra-wideband (UWB) radio networks are low-power, simple, and easily deployable local systems that may be used for navigation and positioning purposes. The properties of the UWB signal are attractive because they can go through obstacles, providing potential positioning in environments where other systems are not suitable. As the goal for this study, UWB-network positioning was used to support accurate geolocalization of slow-moving vehicles in environments that challenge global positioning systems (GPSs), such as forested areas. This work analyzes the results of several experiments with land vehicle positioning within a UWB network in various outdoor scenarios. Only the key aspects of the investigations are discussed in more detail, including the influence of obstacles on signal propagation and, ultimately, on range measurements and the motion compensation resulting from the low UWB-data acquisition rates. Results show that the tested equipment within an area of approximately 30 × 30 m can achieve positioning accuracy to 10 to 30 cm, depending on the environmental parameters and UWB-network configuration.

The use of multisensor system (MSS) plays a key role in engineering geodesy. Because of the complexity of tasks such as industrial applications with high accuracy requirements for heterogeneous and efficient three-dimensional (3D) data acquisition, kinematic MSS are often used. Traditionally, these MSS are composed of referencing sensors and object-capturing sensors. The crucial point in a MSS setup is the determination of the mutual position and orientation [six degrees of freedom (6 DOF)] of each sensor. Within this contribution, a possibility for the determination of the 6 DOF of light detection and ranging (LiDAR) sensors in MSS is introduced. The presented approach is generally applicable and allows the 6 DOF determination of profile laser scanners. The 6 DOF and their uncertainty measures are estimated within an adjustment model by utilizing known reference geometries (RFGs). The approach is especially of interest when sensor origins are not physically available and measurable. It is generally applicable in a static or kinematic measurement environment. As an example, the approach is applied in an industrial environment with accuracy requirements of a few millimeters. The used MSS consists of a terrestrial laser scanner (object capturing) and a laser tracker (referencing). The linking component is a tracker-machine control sensor, which is attached to the laser scanner. More precisely, 6 DOF between the tracker-machine control sensor and the origin of the laser scanner have to be determined. Depending on the required accuracy of the 3D object acquisition, the determination of the 6 DOF must fulfill high accuracy requirements. Finally, the results and the accuracy of the 6 DOF determination are shown and validated with a static calibration procedure.

The network of Continuously Operating Reference Stations (CORS) in Israel realized Israel’s current geodetic datum and is used to monitor it. It was done by adopting a set of fixed International Terrestrial Reference Frame (ITRF) coordinates for the Israeli CORS stations. This datum serves as the infrastructure for all mapping, surveying, and especially the Cadastre system. The relative motion (3–6 mm/year) along the Dead Sea Fault causes the degradation of the Israeli datum over a relative short time. To manage this problem, the authors examined the use of a kinematic datum. This datum includes a 14-parameter transformation, between ITRF05 to the Israeli plane coordinate system, which is static, in conjunction with a velocity field, all evaluated from global positioning system (GPS) time-series solutions of the CORS in Israel from the past 10 years. The results of this examination are promising and show that with a kinematic datum, the accuracy can be maintained over a long period of time.

Track irregularity is one of the most important factors in the regular maintenance and overhaul work needed to determine the state of a railway for safe operation. In this paper, a mobile approach to the measurement of subway track irregularity is proposed. A high-precision three-dimensional (3D) curve of railway track was surveyed by a track trolley equipped with a laser-aided inertial navigation system (INS)/odometer navigation system. Models for measuring the alignment, vertical, cant, and twist irregularities were established. These four types of irregularities were estimated using the 3D track curve. To solve the problem of inherent drift of the INS/odometer system, a laser scanner on the trolley was used to obtain observations of control points along the rail track for position updates. Measurements of real track irregularities were conducted to validate the proposed method. The experimental results indicate that this approach has good repeatability. The accuracy of track-irregularity measurements meets the technical requirements of the Shenzhen Metro in China.

In recent years, three-dimensional (3D) models have been used in a large variety of applications, and the steadily growing capacity in both quality and quantity is increasing demand. To apply new applications to already existing buildings, reconstructed 3D models need to provide the spatial information and semantic and thematic characteristics of target buildings, which are obtained from uninterpreted geometry data sources (i.e., surveying data). This paper proposes a system to automatically reconstruct semantically enhanced 3D building models from terrestrial laser-scanning (TLS) data by combining the strengths of grammars and the maximum a posteriori (MAP) principle. The context-free grammars and rules are predefined and adopted to generate the candidate building models with alternative modeling possibilities. To address the problem of modeling ambiguities, the MAP principle is used to evaluate and control the reconstruction process for both aspects (i.e., goodness of fit and complexity of selected models). Two building models were reconstructed automatically to demonstrate the validation of the authors’ proposed method.

As an external defect, cracks cannot be neglected, and crack detection is crucial for the safety monitoring of nuclear containments. Because of the low continuity and tiny size of cracks, traditional crack-detection methods sometimes fail to detect cracks in complex backgrounds. This paper proposes a crack-detection method featuring a template that can adaptively determine the crack width. The search starts from the local minimum point of the gray level, and the crack width is determined by comparing the gray difference between the seed region and its neighborhoods on a pixel basis. Then, the block-iterative algorithm is used to intersect with the results of the template method followed by the morphological method for further noise removal. Compared with the traditional methods, the developed method can detect tiny cracks with a higher recognition ratio. The applicability of this method was verified by experiments from a nuclear containment in southern China.

A legacy inertial profiler treats accelerometer sensor data as ultraclean zero-noise data. This is not true in reality. The inertial displacement of an inertial profiler is calculated by double integration of raw accelerometer sensor data. The integral operation amplifies the noise to form direct-current drifting and a random-walk effect in inertial displacement. Introducing a high-precision real-time kinematic (RTK) global navigation satellite system (GNSS) receiver is helpful in suppressing the direct-current drifting and random-walk effect in inertial displacement. The fusion of GNSS and accelerometer sensor data relies on a Kalman filtering–based algorithm. However, a low-cost inertial profiler normally does not have a high-precision GNSS receiver. Therefore, this paper proposes a new Kalman filtering–based framework in which the laser rangefinder sensor will replace the GNSS as an observer to control the direct-current drifting and random-walk effect. In the new framework, the accelerometer sensor is assumed to be contaminated by noise, the pavement is assumed to be perfectly smooth, and the road profile essentially becomes observation noise. The new framework also incorporates a legacy algorithm based on high-pass filtering (HPF) and a newly proposed Kalman filtering–based algorithm into a unified framework. The legacy HPF-based algorithm is an open-loop mode, and the Kalman filtering–based algorithm is a closed-loop mode. The HPF-based algorithms involve a specifically designed high-pass filter that can cancel the poles of the integral operator on the unit circle, making the new inertial profiler system unconditionally stable. In the new framework, all variables in state vectors are post-HPF, so the chances of variables reaching their upper bounds or saturating next-stage wavelength filters are low. By leveraging the laser measurement as an observer, the closed-loop Kalman filtering–based algorithm can contribute 20 dB greater attenuation to the low-frequency random walk than the open-loop solutions. The new inertial profiler can switch from legacy to Kalman mode when the strong random walk appears in real-time. Combined with a high-precision optical triangulation sensor, the precision of the newly proposed inertial profiler can reach the subcentimeter or even submillimeter level under a large speed range.

Weighted total least squares (WTLS) has been widely used as a standard method to optimally adjust an errors-in-variables (EIV) model containing random errors both in the observation vector and in the coefficient matrix. An earlier work provided a simple and flexible formulation for WTLS based on the standard least-squares (SLS) theory. The formulation allows one to directly apply the available SLS theory to the EIV models. Among such applications, this contribution formulates the WTLS problem subject to weighted or hard linear(ized) equality constraints on unknown parameters. The constraints are to be properly incorporated into the system of equations in an EIV model of which a general structure for the (singular) covariance matrix $Q_A$ of the coefficient matrix is used. The formulation can easily take into consideration any number of weighted linear and nonlinear constraints. Hard constraints turn out to be a special case of the general formulation of the weighted constraints. Because the formulation is based on the SLS theory, the method automatically approximates the covariance matrix of the estimates from which the precision of the constrained estimates can be obtained. Three numerical examples with different scenarios are used to demonstrate the efficacy of the proposed algorithm for geodetic applications.

In rough environments, such as off-road or post-crisis environments, drivers often need assistance in piloting their vehicles, especially to anticipate obstacles on the driving path. This research aimed to develop such a system, focusing on a cheap and simple method for three-dimensional (3D) reconstruction. This step is important in the detection and classification of negative (under-the-ground) and positive (above-the-ground) obstacles. This information can then be exploited to give feedback to the driver, hence achieving the goal of driver assistance. This article focuses on the 3D reconstruction algorithm, its implementation, and its experimental testing. The choice of sensors is first explained. Because the approach is designed for driver assistance, not for mapping purposes, this leads to real-time and operational constraints (robust and cheap sensors) but eliminates certain other constraints (e.g., dealing with large point clouds). Thus, the selected sensors are a fusion of tilting two-dimensional (2D) LIDAR and stereo cameras. The three-step reconstruction algorithm is then explained. First, the system gets odometry from a stereo pair. Second, 3D points are computed in the ego reference frame with 2D LIDAR scans and servomotor rotation angles. Third, the 3D points are placed in the world reference frame by regularly positioning the previous points on a linear path given by the odometry measurements. The result is a 3D point cloud of the environment in front of the vehicle. Finally, the experimental validation of the approach is explained. A small mobile robot was first tested before applying the approach to a vehicle. Ground-truth acquisitions were conducted to test the veracity of the approach in an outdoor environment. Results show that a coherent map is obtained, but this fusion is not yet suitable for off-road driving at high speeds. However, with some improvements in visual odometry, good 3D reconstruction can be obtained for low- and high-speed driving.

The paper presents a multiview matching algorithm for processing sequence images acquired by a mobile mapping system (MMS). The workflow of the multiview matching algorithm is designed, and the algorithm is based on motion analysis of sequence images in computer vision. To achieve a high multiview matching accuracy, camera lens distortion in sequence images is first corrected, and images can then be resampled. Image points on sequence images are extracted using the Harris operator. The homologous image points are then matched based on correlation coefficients and used to make a robust estimation for a fundamental matrix F between the two adjacent images using the random sample consensus (RANSAC) algorithm. The fundamental matrix F is calculated under the condition of epipolar line constraints. Finally, the trifocal tensor T of the three-view images is calculated to achieve highly accurate triplet image points. These triplet image points are then provided as the initial value for bundle adjustment. The algorithm was tested using a set of sequence images. The results demonstrate that the designed workflow is available and the algorithm is promising in terms of both accuracy and feasibility.

Investments in the development and improvement of the Romanian road network were made recently to ensure enhanced comfort, safety, and efficiency in transport conditions while observing sustainable development policies. The most important objective for Romanian road authorities has been improving the safety of the road system to achieve a secure and efficient movement of people and goods within the public network while effectively managing risks associated with road transport operations. Infrastructure-related risks include personnel injuries and material damages, which can result from physical and natural hazards, accidents, and engineering failures. In this context, this paper brings forward the applicability of state-of-the-art geodetic technologies in adjacent domains, such as civil engineering and road infrastructure works, while explaining that the best solution can be found only by thorough interdisciplinary collaboration between specialists. Thus, the study focuses on complex monitoring methods and the models used for a road structure in Romania. The main objective of the paper is to describe the methodologies (geodetic and laser scanning) used for assessing the deformations of a real road structure. By using these technologies, the authors found real deformations and their underlying causes and thereby managed to adopt the optimal technical solution for rehabilitating the road structure. Finally, the discussed case study provided highly accurate results because the methods and technologies used allow for millimeter-precise measurements of displacements and linear and angular deformations suffered by the construction.

Global positioning system (GPS) techniques have been applied to study land subsidence in the greater Houston area for over two decades (1993–2016). The free Online Positioning User Service (OPUS) utility developed by the National Geodetic Survey (NGS) has been frequently applied by the local surveying and engineering community for faulting and subsidence monitoring. Some OPUS users often wonder about the accuracy of OPUS solutions compared with the results from more sophisticated software packages preferred by the geodetic research institutions. This investigation compared OPUS solutions with precise point positioning (PPP) solutions. The authors’ results indicate that the earth-centered-earth-fixed (ECEF) XYZ coordinates (IGS08) provided by the OPUS and PPP methods differ by only millimeters in the greater Houston area. In this study, the PPP solutions with respect to IGS08 were transformed to a stable local reference frame, the Stable Houston Reference Frame of 2014 (SHRF14). The superficial displacements derived from the PPP solutions retain an accuracy, estimated by the RMS error (RMSE), of 2–3 mm in the horizontal directions and 4–6 mm in the vertical direction. The accuracy of the OPUS solutions is 1–2 mm worse than the accuracy of the PPP solutions. Overall, the PPP solutions provide a more coherent and slightly higher accuracy compared with the OPUS solutions. However, this minor difference in positional accuracy between the OPUS and PPP solutions has little effect on the evaluation of long-term (e.g., >3 years) site velocities. There are significant seasonal signals superimposed into the GPS-derived land subsidence time series in the greater Houston area. As a result, the estimated linear trend is often sensitive to the length of the time window used in the regression analysis. It is recommended that at least a 3-year time span should be used for deriving the average subsidence rate from the PPP solutions of continuous GPS sites. A longer time period may be required when invoking OPUS solutions and/or the campaign GPS survey. It is advisable to use both the OPUS and PPP methods as a quality assurance check for critical positional and velocity measurements.

The roof of Shenzhen Bao’an Stadium is a spoke-type prestressed cable space structure that uses the challenging construction methods of fixed-size and fixed-length design and tensioning technology. It is of critical importance to monitor and analyze the actual space positions of key cable nodes and their displacement during various tensioning periods to ensure that cable force and cable structure spatial configurations are in line with those in the design requirements. As a result, simulated calculations are conducted regarding the positions of the nodes of outer rings and radial cables at the upper haulage cables of 8.00, 1.00, 0.62, 0.33, and 0.00 m and lower haulage cables of 5.00, 1.00, 0.60, 0.20, and 0.00 m. The positions of inner ring nodes were also monitored and analyzed using modern surveying technology at the construction site. In this way, the actual displacements of monitoring points on the roof during tensioning were obtained. The measured and calculated values were then compared and analyzed to guide tension construction. The results indicate that the theoretical calculations and field measurements are in good agreement.

New insights about the concept of local accuracies are elaborated in this article. Recently found evidence supports the mathematical rigor of equations previously published in this journal as a unique alternative to rigorously estimate local accuracies. A mathematical algorithm to compute the averaged local accuracies at a point using the full network statistics of a preselected cluster of surrounding points is introduced. The relationship between eigenvalues and eigenvectors of error ellipsoids among different local frames is also addressed.