Application of Kinematic GPR-TPS Model with High 3D Georeference Accuracy for Underground Utility Infrastructure Mapping: A Case Study from Urban Sites in Celje, Slovenia

This paper describes in detail the development of a ground-penetrating radar (GPR) model for the acquisition, processing and visualisation of underground utility infrastructure (UUI) in a controlled environment. The initiative was to simulate a subsurface urban environment through the construction of regional road, local road and pedestrian pavement in real urban field/testing pools (RUTPs). The RUTPs represented a controlled environment in which the most commonly used utilities were installed. The accuracy of the proposed kinematic GPR-TPS (terrestrial positioning system) model was analysed using all the available data about the materials, whilst taking into account the thickness of the pavement as well as the materials, dimensions and 3D position of the UUI as given reference values. To determine the reference 3D position of the UUI, a terrestrial geodetic surveying method based on the established positional and height geodetic network was used. In the first phase of the model, the geodetic network was used as a starting point for determining the 3D position of the GPR antenna with the efficient kinematic GPR surveying setup using a GPR and self-tracking (robotic) TPS. In the second phase, GPR-TPS system latency was quantified by matching radargram pairs with a set of fidelity measures based on a correlation coefficient and mean squared error. This was followed by the most important phase, where, by combining sets of “standard” processing routines of GPR signals with the support of advanced algorithms for signal processing, UUI were interpreted and visualised semi-automatically. As demonstrated by the results, the proposed GPR model with a kinematic GPR-TPS surveying setup for data acquisition is capable of achieving an accuracy of less than ten centimetres.

Autonomous navigation for large Unmanned Aerial Vehicles(UAVs) is straight-forward to implement, just employ expensive and sophisticated sensors and monitoring devices. On the contrary, usual small quadrotor UAV still have the challenge on obstacle avoidance since this kind of UAV can only carry very light weight sensors such as cameras. Given the above reason, making autonomous navigation over obstacles on small UAV is much more challenging. In this paper, we focus on proposing a novel and memory efficient deep network architecture named UAVNet for small UAV to achieve obstacle detection in the urban environment. Compared with state-of-the-art DNN architecture, UAVNet has only 2.23M parameters(which is half compared with MobileNet) and 141 MFLOPs complexity. Though the parameters are fewer than usual, the accuracy is acceptable, about 80% validated on ImageNet-2102 dataset. To further justify the utility of UAVNet, we also implement the architecture on Nvidia TX2 in real environment using NCTU campus dataset. The experiment shows the proposed UAVNet can detect obstacles to 15 fps, which is a real-time application.

The proven value of DOD Unmanned Aerial Vehicles (UAVs) will ultimately transition to National and Homeland Security missions that require real-time aerial surveillance, situation awareness, force protection, and sensor placement. Public services first responders who routinely risk personal safety to assess and report a situation for emergency actions will likely be the first to benefit from these new unmanned technologies. ‘Packable’ or ‘Portable’ small class UAVs will be particularly useful to the first responder. They require the least amount of training, no fixed infrastructure, and are capable of being launched and recovered from the point of emergency. All UAVs require wireless communication technologies for real- time applications. Typically on a small UAV, a low bandwidth telemetry link is required for command and control (C2), and systems health monitoring. If the UAV is equipped with a real-time Electro-Optical or Infrared (EO/Ir) video camera payload, a dedicated high bandwidth analog/digital link is usually required for reliable high-resolution imagery. In most cases, both the wireless telemetry and real-time video links will be integrated into the UAV with unity gain omni-directional antennas. With limited on-board power and payload capacity, a small UAV will be limited with the amount of radio-frequency (RF) energy it transmitsmore » to the users. Therefore, ‘packable’ and ‘portable’ UAVs will have limited useful operational ranges for first responders. This paper will discuss the limitations of small UAV wireless communications. The discussion will present an approach of utilizing a dynamic ground based real-time tracking high gain directional antenna to provide extend range stand-off operation, potential RF channel reuse, and assured telemetry and data communications from low-powered UAV deployed wireless assets.« less

Due to economic and environmental considerations, there exists a need for effective, efficient, and nondestructive methods for locating buried agricultural drainage pipes. The traditional ways of locating buried drainage pipe involve the use steel rod tile probes or trenching machinery, which can be tedious, time consuming, and/or cause pipe damage. Under certain circumstances, ground penetrating radar (GPR) has proven to be a viable, nondestructive method of finding drainage pipes. The effectiveness of GPR drainage pipe detection is influenced by a number of factors including; soil type, shallow hydrologic conditions, antenna frequency, orientation of the drain line relative to the GPR measurement transect, drainage pipe depth, GPR equipment settings, and computer processing steps. By integrating GPR with Real-Time Kinematic (RTK) Global Navigation Satellite System (GNSS) technology, up to 20 ha of field area can be mapped for subsurface drainage in a single day. For field areas greater than 20 ha, a more efficient drainage mapping method is required, and imagery obtained by Unmanned Aerial Vehicles (UAVs) offer a potential solution. Fixed-wing UAVs can easily cover a 80 ha field area in a single one-hour flight. A fixed-wing UAV mounted with a visible color (VIS-C), multispectral (MS), or thermal infrared (TIR) camera was tested at agricultural field sites across the Midwest U.S. Overall results showed that UAV VISC, MS, TIR imagery detected at least some of the drainage pipe present at 48%, 59%, and 69% of the sites, respectively. To date, there have been five key findings. (1) Although TIR generally worked best, there were sites where either VIS-C or MS proved more effective than TIR for mapping subsurface drainage systems. (2) Timing of UAV surveys relative to recent rainfall can sometimes, but not always, have an important impact on drainage pipe detection. (3) Linear features representing drain lines and farm field operations can be confused with one another and are often both depicted on UAV imagery, but knowledge of subsurface drainage system installation and farm field operations can be employed to distinguish between the two. (4) The drain line response depicted by UAV TIR imagery can change during the day, and relative humidity, which also changes during the day, can impact TIR image quality. (5) Although UAV imagery obtained outside the growing season is generally better for drainage mapping, good results are sometimes achieved with crops in place. Although UAV surveys are more efficient than GPR for drainage mapping, GPR can still be useful for ground truth of drain line locations determined by UAV imagery. With GPR, drainage pipe depth information can be obtained, which is not possible with UAV imagery. Consequently, there are cases where the complementary employment of both GPR and UAV methods are needed for agricultural drainage mapping applications.

Abstract. Recently, 3D measurements using small unmanned aerial vehicles (UAVs) have increased in Japan, because small type UAVs is easily available at low cost and the analysis software can be created the easily 3D models. However, small type UAVs have a problem: they have very short flight times and a small payload. In particular, as the payload of a small type UAV increases, its flight time decreases. Therefore, it is advantageous to use lightweight sensors in small type UAVs. A mobile camera is lightweight and has many sensors such as an accelerometer, a magnetic field, and a gyroscope. Moreover, these sensors can be used simultaneously. Therefore, the authors think that the problems of small UAVs can be solved using the mobile camera. The authors executed camera calibration using a test target for evaluating sensor values measured using a mobile camera. Consequently, the authors confirmed the same accuracy with normal camera calibration.

Aerial photography obtained by unmanned aerial vehicles (UAVs) is a rising market for their civil application. Small UAVs are believed to close gaps in niche markets, such as acquiring airborne image data for remote sensing purposes. Small UAVs can fly at low altitudes, in dangerous environments, and over long periods of time. However, their small lightweight construction leads to new problems, such as higher agility and more susceptibility to turbulence, which has a big impact on the quality of the data and their suitability for aerial photography. This paper investigates the use of fish-eye lenses to overcome field-of-view (FOV) issues for highly agile UAV platforms susceptible to turbulence. The fish-eye lens has the benefit of a large observation area (large FOV) and does not add additional weight to the aircraft, such as traditional mechanical stabilizing systems. We present the implementation of a fish-eye lens for aerial photography and mapping purposes, with potential use in remote sensing applications. We describe a detailed investigation from the fish-eye lens distortion to the registering of the images. Results of the process are presented using low-quality sensors typically found on small UAVs. The system was flown on a midsize platform (a more stable Cessna aircraft) and also on ARCAA's small (<10 kg) UAV platform. The effectiveness of the approach is compared for the two sized platforms.

Recently, small unmanned aerial vehicles (UAVs) have been widely investigated for a variety of applications, including remote sensing and aerial surveying. For such applications, current small UAV platforms use a camera to generate a 3D map from aerial images obtained by the UAV. To generate a 3D map, accurate position and attitude data of the UAV is necessary. However, the typical positioning accuracy of a single frequency global navigation satellite system (GNSS) receiver is 1–3 m, and the attitude accuracy obtained from a low-cost micro electro mechanical system (MEMS) sensor is limited to approximately 1–3°. This accuracy is not sufficiently high for accurate 3D mapping. The goal of this study is establish an accurate position and attitude determination technique by using low-cost GNSS receivers for small UAVs. The key idea behind the proposed method is using multiple low-cost and single-frequency GNSS antennas/receivers to accurately estimate the position and attitude of a UAV. Using the “redundancy” of multiple GNSS receivers, we improve the performance of Real-time kinematic (RTK)-GNSS by using the single-frequency GNSS receivers. This method consists of two approaches: hybrid GNSS fix solutions and consistency check of the GNSS signal strength. In multipath environments, the carrier-phase multipath affects the ambiguity resolution of RTK-GNSS. Different GNSS signal propagation paths are caused at each GNSS antenna. As a result, different multipath errors are caused in each GNSS receiver. It can be used to detect the multipath signals. With this method, we can enhance the availability of carrier-phase ambiguity solutions by using a single-frequency GNSS receiver. Furthermore, we propose a direct attitude estimation technique for a small UAV by using the multiple GNSS receivers. To estimate the absolute attitude of the UAV, we used relative GNSS antenna positions determined by GNSS carrier-phase measurements. It is difficult to resolve the ambiguity for a low-cost single-frequency receiver. In this paper, baseline length constraints can be applied between the GNSS antennas to estimate a reliable ambiguity. To evaluate the proposed method, we conducted a static test in a narrow-sky environment. First, we determined the attitude by using the proposed technique. Using the proposed method, we could almost perfectly solve the GNSS carrier-phase ambiguity by using low-cost single-frequency GNSS receivers in multipath environments. Next, we evaluated the position determination by using the proposed technique. The fix rates are improved in every GNSS antenna by using the proposed multipath elimination technique. Finally, the fix rate reaches 99.9 %, and it can be concluded that the proposed technique offers increased positioning accuracy in urban environments.

In this paper, we study the problem of network reconfiguration when unmanned aerial vehicle (UAV) swarms suffer damage. Multiple UAVs are divided into several groups to perform various tasks. Each master UAV is connected to the ground control station and provides network services for small UAVs that perform various tasks, ensuring that the information of small UAVs can be transmitted back in a timely manner. When master UAVs are destroyed due to factors such as jamming or attacks, the associated small UAVs must select new master UAVs for network service and cooperate with other small UAVs to execute tasks. Based on the heterogeneity and relevance of tasks, we model and analyze the task relationship among different UAVs. Since both master UAVs and small UAVs have respective optimization objectives in the network reconfiguration process, we construct a many-to-one bilateral matching market to model the interaction between master UAVs and small UAVs. To realize an efficient solution for UAV network reconfiguration in complex environments, we propose a distributed matching algorithm and prove that the algorithm can converge to two-sided stable matching. Simulation results indicate that the proposed algorithm can significantly improve the task completion degree of the network compared with three other algorithms.

We present an integration of elevation correction algorithm with Singular Value Decomposition (SVD) based surface reflected wave removal algorithm for ground penetrating radar (GPR) systems positioned above ground with elevation variation. The GPR system and the experimental environment are simulated using the gprMax open-source software. The simulated scenario can be used for GPR mounted on Unmanned Aerial Vehicle (UAV), e.g. drone. The results show excellent reduction of both the effect of terrain with elevation variation and the effect of the surface reflected wave, increasing the probability of object detection, especially when the targets are low-reflective objects. The improved detection capability is tested using Faster-RCNN network, which shows the effectiveness of the method.

Microscale urban meteorological models have been widely used in interpreting atmospheric flow and thermal discomfort in urban environments, but most previous studies examined the urban flow and thermal environments for an idealized urban morphology with imposing neutral or homogeneous thermal forcing. This study has developed a new building-scale meteorological prediction system that extends the ability to predict microscale meteorological fields in real urban environments. A computational fluid dynamics (CFD) model has been developed based on the non-hydrostatic incompressible Reynolds-averaged Navier-Stokes (RANS) equations with a standard k-ε turbulence model, and the microscale urban surface energy (MUSE) model was coupled with the CFD model to provide realistic surface thermal boundary conditions in real urban environments. It is driven by the large scale wind and temperature fields predicted by the Korean operational weather prediction model. The validation results of the new building-scale meteorological prediction system were presented against wind tunnel data and field measurements, showing its ability to predict in-canyon flows and thermal environments in association with spatiotemporal variations of surface temperatures in real urban environments. The effects of realistic surface heating on pedestrian level wind and thermal environments have been investigated through sensitivity simulations of different surface heating conditions in the highly built-up urban area. The results implied that the inclusion of surface thermal forcing is important in interpreting urban flow and thermal environment of the urban area, highlighting a realistic urban surface heating that should be considered in predicting building-scale meteorology over real urban environments.

Due to size limitations and the working environment near the ground, small low-altitude unmanned aerial vehicles (UAVs) cannot carry large-area solar cells and energy storage devices. Solar energy alone has only a limited effect on the improvement of their endurance. To solve this problem, this study proposes the combination of solar energy technology and dynamic soaring technology to improve the endurance of small UAVs. Then, the optimal energy acquisition strategy based on the combination of solar energy and dynamic soaring technology is analyzed in terms of energy. The motion equations and energy acquisition and consumption models of a small solar UAV based on horizontal wind shear are established. Moreover, an optimal trajectory planning problem with the maximum charging power of rechargeable batteries as the optimization objective is proposed. The optimal trajectory is solved based on the hp adaptive pseudo-spectral method, and the optimal trajectory combining the two technologies is simulated and compared with the constant-altitude constant-velocity (CACV) trajectory of the UAV using only solar technology. The simulation results show that under the flight background of tmission = 6:00 (summer sunrise) and β = 0.125 s−1 (wind gradient of the wind shear), the optimized O-type trajectory decreased the energy consumption power by 39.86% and increased the solar energy acquisition power by 86.34% compared to the CACV trajectory. Consequently, the small UAV stores more energy in the rechargeable batteries to improve the endurance performance. Compared to the environment where the sun is located on the leeward side of the wind shear, the environment where the sun is located on the windward side of the wind shear can enable the UAV to acquire more energy through the optimized O-type trajectory.

In multiple input multiple output (MIMO) systems the antenna array configuration in the base station (BS) and mobile station (MS) has a large influence on the available channel capacity. In this paper, we first introduce a new frequency selective (FS) MIMO framework for macro-cells in a realistic urban environment. Next, MIMO configuration characteristics are investigated in order to maximize capacity, mainly the number of antennas and inter-antenna spacing. Channel and capacity simulation results are presented for the city of Lisbon, Portugal, using different antenna configurations. Two power allocations schemes are considered, uniform distribution and FS spatial water-filling. The results suggest optimized MIMO configurations, considering the antenna array size limitations, at the MS side.

In teleoperation situations where virtual environments are employed and fine operation is needed, it is crucial to dynamically keep the virtual environment consistent with the real remote environment. This is especially important when the remote site is at a great distance, such as in a space station, and therefore large time delays exist during the process of teleoperation. In this paper, we propose an automatic calibration method which dynamically determines the difference in 3D position and orientation between virtual and real environments by using a new color image matching technique which is based on gradients of both gray levels and color information. During the process of model building, significant color features in the real environment, either natural or specially prepared, are picked up and mapped onto the corresponding environment model positions. During the process of teleoperation, color images are taken by a camera mounted on a manipulator. These images are analyzed and features are extracted and matched with those in the model in real time. The 3D poses and positions of the camera in the real environment are calculated and then compared with those in the virtual environment in order to determine differences between them. Feature correspondences are determined based on color attributes and geometric relations. A simplified closed-form solution for 3D location of a 4-DOF mobile camera is given. Experimental results show the effectiveness of this dynamic calibration approach.

In this paper, we combine the multiple-input-multiple-output (MIMO) array antenna technology with a multipolarization component in a ground penetrating radar (GPR) system to improve target detection accuracy. The MIMO technology introduced in previous literature is widely applied in radar and other wireless communication fields. Here, we apply the MIMO technology with a “plane-wave like” (PWL) source that uses array antennas with small spacing to emit a pulse source at the same time in GPR detection. First, we analyze the physical mechanism of the MIMO GPR system with a “PWL” source to improve the target detection resolution. Then, we carry out a numerical simulation with a finite-difference time-domain method in 1-D and 2-D array antennas to compare the imaging results of the MIMO and traditional GPR systems. Finally, the synthetic data MIMO GPR experiment with a step-frequency GPR system is implemented. Compared with the traditional GPR system, our results demonstrate that the MIMO GPR system with a multipolarization detection mode can overcome the influence of target radar cross sections and antenna radiation directions, and improve target detection accuracy effectively. Meanwhile, the synthetic MIMO GPR system also provides a good idea to improve the system performance and reduce system design requirements and the manufacture cost.

The hardware instability of a ground penetrating radar (GPR) system has a severe impact on the quantitative analysis of GPR data, which is aimed for material characterization and subsurface monitoring. In this letter, an instability index is proposed to quantify the stability performance of a GPR system and the influences of the GPR system type, warm-up time, environmental noise, and the antenna vibration on it are evaluated through a series of laboratory experiments on a sandbox model. It is found that the GPR signal recorded by a stepped-frequency GPR system based on a vector network analyzer is much more stable than that by a commercial impulse GPR system at a cost of more sweep time. A warm-up time of several minutes is enough for an impulse GPR system. Environmental noise has a negligible influence on the stability performance of a GPR system. Mechanical vibrations of GPR antennas have a severe impact on the stability performance of the GPR system, and the instability index and timing jitter can be increased by more than one order of magnitude in a vibrating condition over those in a static condition. The instability index of the direct signal has a negligible difference with that of the reflection signal from a metal plate; thus, a simple measurement of direct signal on the ground surface is suggested for the evaluation of the instability of a GPR system in field in the future.