Kinematic Global Navigation Satellite System Research Articles

Experimental Study of Seamless Switch Between GNSS- and LiDAR-Based Self-Localization

A self-localization method that can seamlessly switch positions and attitudes estimated using normal distributions transform (NDT) scan matching and a real-time kinematic global navigation satellite system (GNSS) is successfully developed. One of the issues encountered in this method is the sharing of global coordinates among the different estimation methods. Therefore, the three-dimensional environmental maps utilized in the NDT scan matching are created based on the planar Cartesian coordinate system used in the GNSS to obtain accurate information regarding the location, shape, and size of the actual terrain and geographic features. Consequently, seamlessly switching between different methods enables mobile robots to stably obtain accurate estimated positions and attitudes. An autonomous driving experiment is conducted using this self-localization method in the Tsukuba Challenge 2022, and the mobile robot completed a designated course involving more than 2 km in an urban area.

Design and Experiment of a Breakpoint Continuous Spraying System for Automatic-Guidance Boom Sprayers

Repeated and missed spraying are common problems during the working of boom sprayers, especially in the breakpoint continuous process. Therefore, the present study investigated a breakpoint continuous spraying system for automatic-guidance boom sprayers based on a hysteresis compensation algorithm for spraying. An operational breakpoint identification algorithm, which combines a real-time kinematic global navigation satellite system (RTK-GNSS) and wheel odometer, was proposed; a pre-adjusted proportional-integral-derivative (PID) control algorithm for the opening degree of the proportional control valve was designed in thus study. Tests were conducted to establish equations correlating the opening degree of the proportional control valve, pump output flow rate, and main pipeline flow rate, with an R2 ≥ 0.9525. The time to adjust to the target flow rate was experimentally tested. The breakpoint identification accuracy of the RTK-GNSS and RTK-GNSS + wheel odometer was experimentally assessed. A field spraying deposition variation experiment was conducted. According to the results, the system effectively eliminated missed spraying, with a maximum repeated spraying distance of ≤3.3 m, and it achieved a flow control error within 3%. This system also reduced the repeated spraying area and enhanced the pesticide spraying quality of breakpoint continuous spraying for automatic-guidance boom sprayers.

Ground Deformation Monitoring for Subway Structure Safety Based on GNSS

Ground deformation poses a serious threat to the safety of subway structures. Consequently, intelligent and efficient automated safety monitoring of ground deformation along the subway has become urgent. Traditional engineering observation methods have the disadvantages of difficulties with datum selection, non-automation, and poor reliability. A ground deformation monitoring system for subway structure safety based on the Global Navigation Satellite System (GNSS) was established and validated through experimental comparisons with traditional precision leveling in this study. Based on the GNSS monitoring points, the continuous kinematic observation GNSS data of ground deformation along the subway line were obtained; a joint robust local mean decomposition (RLMD)–singular value decomposition (SVD) noise-reduction processing method for GNSS signals was proposed to realize the real-time and high-precision monitoring of ground deformation. The results show that the proposed combined noise-reduction method can reduce the maximum noise amplitude by 86%. When compared with the accuracy of the traditional precision leveling method, it was determined that the vertical positioning accuracy of the deformation monitoring system is greater than 2.7 mm, the horizontal positioning accuracy is greater than 1.3 mm, and the measurement error is less than 1.5 mm. The deformation monitoring system has the advantages of convenience, automation, and high accuracy and can be applied to ground deformation monitoring for subway structures.

A Multi-Scale Anti-Multipath Algorithm for GNSS-RTK Monitoring Application.

During short baseline measurements in the Real-Time Kinematic Global Navigation Satellite System (GNSS-RTK), multipath error has a significant impact on the quality of observed data. Aiming at the characteristics of multipath error in GNSS-RTK measurements, a novel method that combines improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and adaptive wavelet packet threshold denoising (AWPTD) is proposed to reduce the effects of multipath error in GNSS-RTK measurements through modal function decomposition, effective coefficient sieving, and adaptive thresholding denoising. It first utilizes the ICEEMDAN algorithm to decompose the observed data into a series of intrinsic mode functions (IMFs). Then, a novel IMF selection method is designed based on information entropy to accurately locate the IMFs containing multipath error information. Finally, an optimized adaptive denoising method is applied to the selected IMFs to preserve the original signal characteristics to the maximum possible extent and improve the accuracy of the multipath error correction model. This study shows that the ICEEMDAN-AWPTD algorithm provides a multipath error correction model with higher accuracy compared to singular filtering algorithms based on the results of simulation data and GNSS-RTK data. After the multipath correction, the accuracy of the E, N, and U coordinates increased by 49.2%, 65.1%, and 56.6%, respectively.

THE FEATURES OF AERIAL PHOTOGRAPHY FOR ROAD REPAIR OR RECONSTRUCTION

This study formulates some of the actual problems that arise during engineering and geodetic survey for designing road repair and reconstruction. An analysis of the regulatory requirements currently in force in Ukraine for carrying out this type of work has been carried out, it has also been concluded that at present they do not keep up with the latest technologies. Studied publications describing the use of unmanned aerial vehicles (UAV). Classified navigation satellite receivers are installed on UAVs, for the purpose of determining coordinates. Particular attention is paid to the installation on the carrier of geodetic class navigation equipment using kinematic GNSS (Global Navigation Satellite System) technologies, such as RTK (Real-Time Kinematic) and PPK (Post-Processing Kinematic). These technologies allow us to determine the spatial coordinates of the centers of photos at the time of photographing with centimeter accuracy, which greatly simplifies the adjustment of digital photogrammetric models and increases their accuracy. But the most important advantage is a significant reduction in field surveying, which makes them less expensive and more efficient overall. In the course of the study, it was found that in conditions of limited access to nearby territories, insufficient stability of the results of determining the centers of images can affect the final quality of products. For cases where the maximum achievable accuracy is at the limit of the possibility of photogrammetric technology, the removal of the most critical elements is proposed to be combined by combining aerial photography technologies with ground technologies. The road’s hard surface surveying can be performed by ground geodetic methods (tacheometric or GNSS), and by modern laser scanning. At the same time, the adjacent territory, regarding the shooting of elements of which less stringent requirements are established, can be worked out by aerial photography. In further studies, it is necessary to determine the conditions for achieving the maximum accuracy of UAV photogrammetry. Keywords: aerial photography, photogrammetry, geodetic survey, reconstruction of highways.

Deep learning‐based crop row detection for infield navigation of agri‐robots

AbstractAutonomous navigation in agricultural environments is challenged by varying field conditions that arise in arable fields. State‐of‐the‐art solutions for autonomous navigation in such environments require expensive hardware, such as Real‐Time Kinematic Global Navigation Satellite System. This paper presents a robust crop row detection algorithm that withstands such field variations using inexpensive cameras. Existing data sets for crop row detection do not represent all the possible field variations. A data set of sugar beet images was created representing 11 field variations comprised of multiple grow stages, light levels, varying weed densities, curved crop rows, and discontinuous crop rows. The proposed pipeline segments the crop rows using a deep learning‐based method and employs the predicted segmentation mask for extraction of the central crop using a novel central crop row selection algorithm. The novel crop row detection algorithm was tested for crop row detection performance and the capability of visual servoing along a crop row. The visual servoing‐based navigation was tested on a realistic simulation scenario with the real ground and plant textures. Our algorithm demonstrated robust vision‐based crop row detection in challenging field conditions outperforming the baseline.

Development of an automated mobile grain size mapping of a mixed sediment beach

AbstractGrain sizes influence beach morphologies, processes, and states (e.g., dissipative, reflective and intermediate), however previous studies analysing beach grain size are often spatially and temporarily sparse. In this study, an automated mobile grain size estimation method using digital photographs (mobile digital grain size, MDGS) was developed and tested at a mixed sediment beach consisting of sand and gravels. The data collection system consisted of a downward‐looking camera, Real Time Kinematic Global Navigation Satellite System, and a camera‐triggering single‐board computer, attached to an all‐terrain vehicle. Digital photographs were collected monthly from January 2021 to March 2022 over a section spanning ~700 m alongshore and processed automatically using an existing (non‐mobile) DGS estimation method. The MDGS results were generally consistent with manually estimated grain sizes (n = 962, r2 = 0.74, RMSE = 5.9 mm) and UAV orthomosaic imagery, although the MDGS sometimes overestimated manually estimated grain sizes particularly for photographs with relatively small sediments. Modelled nearshore waves and subaerial beach survey data from mobile terrestrial LiDAR were used to explore using MDGS in morphological studies. Seasonal grain size trends, observed relationships between nearshore wave height and spatially averaged beach grain size, and the general increasing trends of grain sizes with both beach elevation and local roughness/slope were consistent with previous studies, highlighting the use of MDGS for morpho‐sedimentary mixed sediment beach research. The MDGS provides a unique method to conduct spatially and temporarily high‐resolution mapping of gravel size sediments over large areas (>102 m alongshore). Potential improvements of the MDGS include additional low elevation cameras for improved fine (e.g., sand) grain size estimates, decreased time between photo triggering, and automated removal of erroneous photographs containing surface irregularities (e.g., tire tracks) and non‐sediment features (e.g., seaweed). Overall, the MDGS provides improved surface grain size mapping and investigations of complex grain size–morphology relationships of mixed sediment beaches.

Low-cost multi-GNSS, single-frequency RTK averaging for marine applications: accurate stationary positioning and vertical tide measurements

The use of Real Time Kinematic (RTK) Global Navigation Satellite System (GNSS) for accurate horizontal and vertical measurements in the marine environment has been considered since the late-1980’s and tested from the 1990’s when GPS and GLONASS were the only operational constellations available and high-cost multi-frequency receiver equipment was required. This paper modernizes the conversation using multi-constellation, low-cost, single-frequency RTK GNSS measurements and proves their value with accurate positioning and tide measurements. Our tests show average stationary horizontal positioning measurements using this equipment are suitable (95% CI) for the most stringent International Hydrographic Organization (IHO) Standard S-44 ‘Exclusive Order’ at base station ranges of up to 27 km. Vertical observations on a moving platform, smoothed using a baseline distance-dependent moving average filter show the equipment and method are comparable with traditional electronic tide gauge observations over the same base station range. All of our measurement results show the potential to improve total uncertainty calculations undertaken by hydrographers, engineers and scientists in the marine realm, while the low-cost equipment raises the possibility that more measurements can be taken, leading to improvements in monitoring, modelling and understanding the marine environment.

Development and experimental evaluation of visual-acoustic navigation for safe maneuvering of unmanned surface vehicles in harbor and waterway areas

Ship maneuvering in narrow harbor environments requires accurate navigation data to operate safely. Since Global Navigation Satellite Systems (GNSS) can be unreliable and inaccurate in such environments, other aiding techniques should be used for increased redundancy and reliability. To this end, we present a GNSS-free sensor suite consisting of visual, acoustic, and inertial sensors that can be used to navigate small vehicles in a harbor environment. The proposed method is aided by two navigation systems: a doppler velocity log (DVL) for low-drift velocity aiding and a visual fiducial system to estimate the drift-free position and attitude of a camera onboard the vehicle with respect to easily identifiable tags at the dockside. The visual and acoustic measurements are fused with inertial data using an error-state Kalman filter for robust state estimation. To benchmark the performance, we employ an unmanned surface vehicle equipped with a dual-antenna real-time kinematic GNSS receiver for accurate positioning and heading. The benefits of supplementing landmark-based navigation with acoustic measurements are demonstrated by conducting adverse scenarios that include noisy measurements and sensor dropouts in the harbor environment. As a result, the proposed visual-acoustic system can supplement satellite-based navigation systems for safety-critical harbor maneuvering.

High-precision plant height measurement by drone with RTK-GNSS and single camera for real-time processing

Conventional crop height measurements performed using aerial drone images require 3D reconstruction results of several aerial images obtained through structure from motion. Therefore, they require extensive computation time and their measurement accuracy is not high; if the 3D reconstruction result fails, several aerial photos must be captured again. To overcome these challenges, this study proposes a high-precision measurement method that uses a drone equipped with a monocular camera and real-time kinematic global navigation satellite system (RTK-GNSS) for real-time processing. This method performs high-precision stereo matching based on long-baseline lengths (approximately 1 m) during the flight by linking the RTK-GNSS and aerial image capture points. As the baseline length of a typical stereo camera is fixed, once the camera is calibrated on the ground, it does not need to be calibrated again during the flight. However, the proposed system requires quick calibration in flight because the baseline length is not fixed. A new calibration method that is based on zero-mean normalized cross-correlation and two stages least square method, is proposed to further improve the accuracy and stereo matching speed. The proposed method was compared with two conventional methods in natural world environments. It was observed that error rates reduced by 62.2% and 69.4%, for flight altitudes between 10 and 20 m respectively. Moreover, a depth resolution of 1.6 mm and reduction of 44.4% and 63.0% in the error rates were achieved at an altitude of 4.1 m, and the execution time was 88 ms for images with a size of 5472 × 3468 pixels, which is sufficiently fast for real-time measurement.

Real-time unmanned aerial vehicle surveying using spatial criteria: a simulated study

Precise coordinate estimation is a fundamental engineering challenge in many mapping applications. Conventional surveying techniques based on global navigation satellite system (GNSS), light detection and ranging (LiDAR), or total stations involve costly equipment and time-consuming methodologies and may suffer from restrictions, such as occlusions and low satellite availability. In this study, a surveying approach, based on a custom-equipped unmanned aerial vehicle (UAV) and ArUco markers distributed in an unknown area as the potential target mapping points, is proposed and evaluated through simulation. The UAV incorporates a real-time kinematic GNSS receiver and a gimbal unit, with a simple camera and an electronic rangefinder module. The system demonstrates a real-time hierarchy targeting system that allows the UAV to engage with the ground targets through the camera, measure corresponding distances, record UAV coordinates, and then perform a multilateration-based target coordinate estimation. To evaluate the flexibility, efficiency, and onboard performance of the proposed target positioning approach, the method was developed as a robotic operating system software package and tested in the Gazebo robotic simulator on an NVIDIA Jetson TX2. Several mapping environments, along with varying flight type scenarios, were created to evaluate the resulting coordinate estimation errors. The achieved positioning accuracy is very promising and demonstrates a possible use for circular flying trajectories. Along these lines, the proposed methodology may pave the way toward a precise surveying alternative, as it may establish the main surveying method for common mapping applications in the near future and even provide coordinate estimations in demanding and, until now, unattainable areas.

Impact of Additional Antenna Groundplanes on RTK-GNSS Positioning Accuracy of UAVs

Abstract. Precise position information is important for terrestrial and airborne surveying systems, such as unmanned aerial vehicles (UAVs). Those systems often rely on real-time kinematic (RTK) global navigation satellite systems (GNSSs) for position determination, where the GNSS antenna mounting environment impacts the GNSS position accuracy to a great extent. This paper investigates the impact of different supplementary groundplane shapes, sizes, and materials on multi-band patch and helical GNSS antennas at both, the UAV rover and RTK base station with respect to the achievable position accuracy. The groundplanes consist of solid aluminum sheets or copper plated printed circuit boards (PCBs) and are mounted directly underneath the GNSS antennas. Appropriate supplementary groundplanes are found to significantly improve the GNSS position accuracy in the majority of test cases.

LiDAR-Based Sensor Fusion SLAM and Localization for Autonomous Driving Vehicles in Complex Scenarios

LiDAR-based simultaneous localization and mapping (SLAM) and online localization methods are widely used in autonomous driving, and are key parts of intelligent vehicles. However, current SLAM algorithms have limitations in map drift and localization algorithms based on a single sensor have poor adaptability to complex scenarios. A SLAM and online localization method based on multi-sensor fusion is proposed and integrated into a general framework in this paper. In the mapping process, constraints consisting of normal distributions transform (NDT) registration, loop closure detection and real time kinematic (RTK) global navigation satellite system (GNSS) position for the front-end and the pose graph optimization algorithm for the back-end, which are applied to achieve an optimized map without drift. In the localization process, the error state Kalman filter (ESKF) fuses LiDAR-based localization position and vehicle states to realize more robust and precise localization. The open-source KITTI dataset and field tests are used to test the proposed method. The method effectiveness shown in the test results achieves 5-10 cm mapping accuracy and 20-30 cm localization accuracy, and it realizes online autonomous driving in complex scenarios.

An Experiment in Determining the High-water Mark

As a boundary of the seashore, the high-water mark (HWM) is relevant to the public, the State, and other rights holders in the coastal zone. Unlike most fixed property boundaries that are surveyed and beaconed, the HWM is subject to dynamic natural coastal processes and moves over time. Its location is difficult to determine, and the precision of this determination is unknown. This paper reports on an experiment to measure the precision (variability/repeatability) of the location of the HWM at a variety of sites near Cape Town, by volunteer participants. Four sites were chosen along stable (non-mobile) shores along the open, high energy oceanic shores south of Cape Town on the Cape Peninsula. One of these sites exhibits large variation in coastal terrain and type – at this site three sets of measurements were undertaken, bringing the total number of experimental sites to six. Surveying was undertaken in the South African national control survey system using network real-time kinematic global navigation satellite systems (GNSS).
 This experiment shows that the professional land surveyors located the HWM to ±1,6m, coastal engineers to ±4,1m, and the group of ‘others’ to ±4,2m. The professional land surveyors determined the height of the HWM to ±0,4m over all sites, compared to ±0,7m for all participants. The HWM is likely to be about 4 - 5m above the lowest astronomic tide. However, the line is not a contour – it is affected by weather and local variations in the coastline such as slope and seashore composition. The averaged heights of the HWM at each of the sites for all participants showed a range of 1,3m. Since the boundaries of the seashore cannot be determined with precision, property, cadastral and environmental law needs to continue to respect the nature of this environment and the limitations of locating the HWM.

Application and Accuracy of Unmanned Aerial Survey Imagery after Salvage Logging in Different Terrain Conditions

The accuracy of the positioning of the data collected by remote sensing platforms is of great importance in forest and wildlife surveys, salvage logging, soil disturbances after felling operations, fire risk management and many other forestry-based research. The significance of bark beetles in silver fir and Norway spruce stands is an essential factor that can affect the increase in biomass quantity and the reduction of its quality. Due to an Ips typographus (L.) outbreaks in the central part of Croatia from 2016 to 2021, salvage logging was performed in an area of 11,940 ha, with terrain slopes varying from 0 to 172.83%. Two plots of similar sizes (<5 ha) and different terrain conditions (flat vs. sloped terrain) were chosen and were measured in June 2022. Measurements included a total station, a real-time kinematic (RTK) GNSS (Global Navigation Satellite System) terrestrial receiver and an unmanned aerial system (UAS) in order to determine the accuracy of a digital terrain model (DEM) generated by a photogrammetric UAS. In total, 175 checkpoints were marked in the field. Four different GCP (Ground Control Point) classes (5, 10, 15 and 20) were used to compare validation points acquired from the original point clouds, imagery and orthophotos to the reference positions. This study showed that, in terms of a forest area recognition after conducted salvage logging, the use of 10 GCPs for terrain evaluation is necessary even in small areas below 5 ha and regardless of terrain slope.

Mapping inland water bathymetry with Ground Penetrating Radar (GPR) on board Unmanned Aerial Systems (UASs)

Bathymetry of inland water bodies is essential for river maintenance and flood risk management. Traditionally, in shallow water bodies, bathymetry is retrieved by operators wading through the water body with Real Time Kinematic (RTK) Global Navigation Satellite System (GNSS), whilst in deeper waters, it is retrieved with sonar instruments on manned or unmanned boats. In the past, researchers have documented the use of Ground Penetrating Radar (GPR) on boats (i.e. water-coupled GPR) for monitoring the bathymetry of frozen and non-frozen water bodies. Furthermore, GPR has been used on helicopters for monitoring ice and snow thickness. However, deployment of GPR on board Unmanned Aerial Systems (UASs) in non-frozen inland water bodies with electric conductivity higher than 100 μS/cm (as is common in most inland waterbodies in non-polar regions) is unexplored. In this paper, we document the possibility to use drone-borne and water-coupled GPR in several cross-sections located in three different waterbodies (1 lake and 2 rivers) in Denmark. These waterbodies had different bed sediment materials and vegetation conditions, an electric conductivity varying from 200 to 340 μS/cm and depths up to 2.5 m. Drone-borne GPR showed accuracy similar to water-coupled GPR when compared to RTK GNSS ground-truth measurements, with a Mean Absolute Error (MAE) of approx. 8 cm. The only limitations of drone-borne GPR were i) more restrictive minimum depth requirement (typically 0.8–1.1 m for drone-borne GPR, while 0.3–0.4 m for water-coupled GPR) ii) requirement to fly the GPR antenna at altitudes of approx. 0.5 m above the water surface to avoid high spreading losses and strong surface clutter events hiding the signal. Finally, GPR measurements were benchmarked against traditional sonar measurements, showing that GPR measurements significantly outperform sonar measurements in waterbodies with medium or high density of aquatic vegetation.

Performance tests of geodetic receivers with tilt sensors in obstructed environments using the NRTK GNSS technique

Abstract The Department of Lands (DOL), Thailand, has adopted the Network-based Real-Time Kinematic (NRTK) Global Navigation Satellite System (GNSS) surveying technique using a Virtual Reference Station (VRS) to support cadastral surveys since 2011. Determining accurate coordinates of parcel boundary markers at building corners or near fences and walls is difficult because a GNSS range pole cannot be leveled with a circular bubble. This study aims to evaluate the performance of the receivers equipped with tilt sensors for horizontal and vertical positioning. Two types of tilt sensors used for evaluation were a magnetometer and micro-electro-mechanical system (MEMS) and an inertial measurement unit (IMU). Conducting the NRTK GNSS surveying tests was based on the pole tilt angles of 0°, 15°, 25°, 35°, and 45° from a plumb line in controlled and obstructed environments. The IMU-based tilt sensor had more advantage of accurately positioning over the MEMS sensor. The results showed that using the IMU, better than 4 cm horizontal positioning accuracy was achievable when the pole was tilted by 15° or less under non-multipath and open-sky conditions. The vertical accuracy was of a few centimeter levels and least sensitive to tilt angles using either type of sensor. However, none of the sensors precisely compensated for pole tilt in strong-multipath and complex environments, causing increased horizontal errors in decimeter levels.

Capturing complex star dune dynamics—repeated highly accurate surveys combining multitemporal 3D topographic measurements and local wind data

AbstractMorphologies of highly complex star dunes are the result of aeolian dynamics in past and present times. These dynamics reflect climatic conditions and associated forces like sediment availability and vegetation cover, as well as feedbacks with adjacent environments. However, an understanding of aeolian dynamics on star dune morphometries is still lacking sufficient detail, and their influence on formation and evolution remains unclear. We therefore investigate the dynamics of a complex star dune (Erg Chebbi, Morocco) by analysing wind measurements compared to morphometric changes derived from multitemporal high‐accuracy 3D observations during two surveys (October 2018 and February 2020). Using real‐time kinematic global navigation satellite system (RTK‐GNSS) measurements and terrestrial laser scanning (TLS), the reaction of a star dune surface to an observed constant unimodal sand‐moving wind is presented. TLS point clouds are used for morphometric analysis as well as direct surface change analysis, which relates to sand transport. RTK‐GNSS measurements enable the assessment of horizontal crest movement. Observed surface changes lead to the identification of an overall shielding effect, resulting in sand accumulation mainly on windward slopes. Our results point to a self‐sustained dune growth, which has not yet been described in such spatial detail. Steep slopes, often found on star dunes around the globe, seem to partly hinder upslope sand transport. Though a comparatively short observation period, we therefore hypothesize that, besides wind intensity alone, slope angles are more decisive for sand transport than previously assumed. Our methodological approach of combining meteorological data and high‐resolution multitemporal 3D elevation models can be used for monitoring all dune forms and contributes to a general understanding of dune dynamics and evolution.

A new digital elevation model (DEM) dataset of the entire Antarctic continent derived from ICESat-2

Abstract. Antarctic digital elevation models (DEMs) are essential for fieldwork, ice motion tracking and the numerical modelling of the ice sheet. In the past 30 years, several Antarctic DEMs derived from satellite data have been published. However, these DEMs either have coarse spatial resolution or aggregate observations spanning several years, which limit their further scientific applications. In this study, the new generation satellite laser altimeter Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is used to generate a new Antarctic DEM for both the ice sheet and ice shelves. Approximately 4.69 × 109 ICESat-2 measurement points from November 2018 to November 2019 are used to estimate surface elevations at resolutions of 500 m and 1 km based on a spatiotemporal fitting method. Approximately 74 % of Antarctica is observed and the remaining observation gaps are interpolated using the normal kriging method. The DEM is formed from the estimated elevations in 500 m and 1 km grid cells, and is finally posted at the resolution of 500 m. National Aeronautics and Space Administration (NASA) Operation IceBridge (OIB) airborne data are used to evaluate the generated Antarctic DEM (hereafter called the ICESat-2 DEM) in individual Antarctic regions and surface types. Overall, a median bias of −0.19 m and a root-mean-square deviation of 10.83 m result from approximately 5.2 × 106 OIB measurement points. The accuracy and uncertainty of the ICESat-2 DEM vary in relation to the surface slope and roughness, and more reliable estimates are found in the flat ice sheet interior. The ICESat-2 DEM is comparable to other DEMs derived from altimetry, stereophotogrammetry and interferometry. Similar results are found when comparing to elevation measurements from kinematic Global Navigation Satellite System (GNSS) (GPS and the Russian GLONASS) transects. The elevations of high accuracy and ability of annual updates make the ICESat-2 DEM an addition to the existing Antarctic DEM groups, and it can be further used for other scientific applications. The generated ICESat-2 DEM (including the map of uncertainty) can be downloaded from National Tibetan Plateau Data Center, Institute of Tibetan Plateau Research, Chinese Academy of Sciences at https://data.tpdc.ac.cn/en/disallow/9427069c-117e-4ff8-96e0-4b18eb7782cb/ (last access: 27 June 2022) (Shen et al., 2021a, https://doi.org/10.11888/Geogra.tpdc.271448).

Under the Sand: Navigation and Localization of a Micro Aerial Vehicle for Landmine Detection with Ground-Penetrating Synthetic Aperture Radar

Ground-penetrating radar mounted on a micro aerial vehicle (MAV) is a promising tool to assist humanitarian landmine clearance. However, the quality of synthetic aperture radar images depends on accurate and precise motion estimation of the radar antennas as well as generating informative viewpoints with the MAV. This paper presents a complete and automatic airborne ground-penetrating synthetic aperture radar (GPSAR) system. The system consists of a spatially calibrated and temporally synchronized industrial grade sensor suite that enables navigation above ground level, radar imaging, and optical imaging. A custom mission planning framework allows generation and automatic execution of stripmap and circular GPSAR trajectories controlled above ground level as well as aerial imaging survey flights. A factor graph based state estimator fuses measurements from dual receiver real-time kinematic (RTK) global navigation satellite system (GNSS) and an inertial measurement unit (IMU) to obtain precise, high-rate platform positions and orientations. Ground truth experiments showed sensor timing as accurate as 0.8 µs and as precise as 0.1 µs with localization rates of 1 kHz. The dual position factor formulation improves online localization accuracy up to 40 % and batch localization accuracy up to 59 % compared to a single position factor with uncertain heading initialization. Our field trials validated a localization accuracy and precision that enables coherent radar measurement addition and detection of radar targets buried in sand. This validates the potential as an aerial landmine detection system.