Accurate Relative Position Research Articles

Technology of static oblique lithography used to improve the fidelity of lithography pattern based on DMD projection lithography

This paper proposes a static oblique lithography (SOL) strategy based on conventional digital micromirror device (DMD) projection lithography. This strategy combines the oblique lithography on oblique scanning lithography with static lithography to ensure that we can produce high fidelity microstructures with very smooth curved edges in all directions and more accurate relative line positions. Not only does the single-axis high-precision micromotion stage of this strategy enable a significant reduction in fabrication cost, but also enables the lithography of subpixel-width curves with smooth edges used our improved sub-pattern quantization strategy. The intensity distribution of the actual diffracted light field at the focal point of the helical zone plate fabricated by the SOL is highly matched with the intensity distribution of the designed simulated light field. This shows that the SOL can play an important role in the field of micro-optical device fabrication.

Speckle Interferometry at SOAR in 2021

The speckle interferometry program at the the 4.1 m Southern Astrophysical Research Telescope (SOAR), which started in 2008, now has accumulated over 30,300 individual observations of 12,700 distinct targets. Its main goal is to monitor orbital motion of close binaries, including members of high-order hierarchies and low-mass dwarfs in the solar neighborhood. The results from 2021 are published here, totaling 2623 measurements of 2123 resolved pairs and nonresolutions of 763 targets. The median measured separation is 0.″21, and 75 pairs were closer than 30 mas. The calibration of scale and orientation is based on the observations of 103 wide pairs with well-modeled motion. These calibrators are compared to the latest Gaia data release, and minor (0.5%) systematic errors were rectified, resulting in accurate relative positions with typical errors on the order of 1 mas. Using these new measurements, orbits of 282 binaries are determined here (54 first determinations and 228 corrections). We resolved for the first time 50 new pairs, including subsystems in known binaries. A list of 94 likely spurious pairs unresolved at SOAR (mostly close Hipparcos binaries) is also given.

An Algorithm to Assist the Robust Filter for Tightly Coupled RTK/INS Navigation System

The Real-Time Kinematic (RTK) positioning algorithm is a promising positioning technique that can provide real-time centimeter-level positioning precision in GNSS-friendly areas. However, the performance of RTK can degrade in GNSS-hostile areas like urban canyons. The surrounding buildings and trees can reflect and block the Global Navigation Satellite System (GNSS) signals, obstructing GNSS receivers’ ability to maintain signal tracking and exacerbating the multipath effect. A common method to assist RTK is to couple RTK with the Inertial Navigation System (INS). INS can provide accurate short-term relative positioning results. The Extended Kalman Filter (EKF) is usually used to couple RTK with INS, whereas the GNSS outlying observations significantly influence the performance. The Robust Kalman Filter (RKF) is developed to offer resilience against outliers. In this study, we design an algorithm to improve the traditional RKF. We begin by implementing the tightly coupled RTK/INS algorithm and the conventional RKF in C++. We also introduce our specific implementation in detail. Then, we test and analyze the performance of our codes on public datasets. Finally, we propose a novel algorithm to improve RKF and test the improvement. We introduce the Carrier-to-Noise Ratio (CNR) to help detect outliers that should be discarded. The results of the tests show that our new algorithm’s accuracy is improved when compared to the traditional RKF. We also open source the majority of our code, as we find there are few open-source projects for coupled RTK/INS in C++. Researchers can access the codes at our GitHub.

Research on velocity measurement and relative positioning method of maglev train based on multi-sensor information fusion

This paper focuses on the optimal method of the non-contact velocity measurement and relative positioning system for medium and low speed maglev trains. By analyzing the working principle of the velocity measurement method based on counting sleepers and analyzing the speed system error and random error model, it is shown that the velocity measurement accuracy based on the counting sleeper speed measurement method needs to be further improved. Therefore, a method of using multi-sensor information fusion is proposed to improve the accuracy of velocity measurement and relative positioning. Firstly, aiming at the disturbance problem of traction, braking and suspension vibration in the attitude angle calculation and the cumulative error problem of attitude angle, a posture solution method combining the optimized second-order complementary filter and the velocity adaptation Unscented Kalman filter with maximum noise reduction is proposed; Then, in order to further reduce the accumulated error of the attitude angle and the high performance requirements of the gyroscope, the bias instability of the gyroscope is analyzed, and an adaptive wavelet de-noising algorithm based on threshold optimization is proposed; Finally, to weaken the colored noise interference caused by the suspension vibration and to weaken the velocity accumulation error, a fusion velocity measurement and positioning algorithm of multi-loop Kalman filter with acceleration fusion correction and velocity accumulation error correction is researched. The effectiveness of the proposed fusion method is verified through simulation comparison analysis and on-board engineering test. Compared with the velocity measurement method based on counting sleepers, the velocity measurement accuracy is improved by an order of magnitude, and its accuracy is comparable to the high-precision velocity measurement method based on the induction loop and GPS/INS. It has certain engineering applicability and application value.

Wearable IMMU-Based Relative Position Estimation between Body Segments via Time-Varying Segment-to-Joint Vectors.

In biomechanics, estimating the relative position between two body segments using inertial and magnetic measurement units (IMMUs) is important in that it enables the capture of human motion in unconstrained environments. The relative position can be estimated using the segment orientation and segment-to-joint center (S2J) vectors where the S2J vectors are predetermined as constants under the assumption of rigid body segments. However, human body segments are not rigid bodies because they are easily affected by soft tissue artifacts (STAs). Therefore, the use of the constant S2J vectors is one of the most critical factors for the inaccurate estimation of relative position. To deal with this issue, this paper proposes a method of determining time-varying S2J vectors to reflect the deformation of the S2J vectors and thus to increase the estimation accuracy, in IMMU-based relative position estimation. For the proposed method, first, reference S2J vectors for learning needed to be collected. A regression method derived a function outputting S2J vectors based on specific physical quantities that were highly correlated with the deformation of S2J vectors. Subsequently, time-varying S2J vectors were determined from the derived function. The validation results showed that, in terms of the averaged root mean squared errors of four tests performed by three subjects, the proposed method (15.08 mm) provided a higher estimation accuracy than the conventional method using constant vectors (31.32 mm). This indicates the proposed method may effectively compensate for the effects of STAs and ultimately estimate more accurate relative positions. By providing STA-compensated relative positions between segments, the proposed method applied in a wearable motion tracking system can be useful in rehabilitation or sports sciences.

Field Testing and Evaluation of Single-Receiver GPS Odometry for Use in Robotic Navigation

Mobile robots rely on odometry to navigate in areas where localization fails. Visual odometry (VO), for instance, is a common solution for obtaining robust and consistent relative motion estimates of the vehicle frame. In contrast, Global Positioning System (GPS) measurements are typically used for absolute positioning and localization. However, when the constraint on absolute accuracy is relaxed, accurate relative position estimates can be found with one single-frequency GPS receiver by using time-differenced carrier phase (TDCP) measurements. In this paper, we implement and field test a single-receiver GPS odometry algorithm based on the existing theory of TDCP. We tailor our method for use on an unmanned ground vehicle (UGV) by incorporating proven robotics tools such as a vehicle motion model and robust cost functions. In the first half of our experiments, we evaluate our odometry on its own via a comparison with VO on the same test trajectories. After 4.3 km of testing, the results show our GPS odometry method has a 79% lower drift rate than a proven stereo VO method while maintaining a smooth error signal despite varying satellite availability. GPS odometry can also make robots more robust to catastrophic failures of their primary sensor when added to existing navigation pipelines. To prove this, we integrate our GPS odometry solution into Visual Teach and Repeat (VT&R), an established visual, path-following navigation framework. We perform further testing to show it can maintain accurate path following and prevent failures in challenging conditions including full camera dropouts. Code is available at https://github.com/utiasASRL/cpo.

An improved posture evaluation method for cylindrical intersecting holes on large aerospace components based on monocular vision

Cylindrical intersecting holes (CIHs) are common connection and location reference features in the assembly of large aerospace structures such as missiles and rocket cabins. The posture accuracy of assembly holes significantly impacts the relative position accuracy of joined parts and the fatigue strength of the finished product. At present, monocular vision measurement is widely used in the automatic drilling of assembly holes due to its integration simplicity and lower cost, but in most research, only the front face edge of the hole is used in the measurement model, and the hole end surface is usually assumed to be plane, which inevitably leads to precision loss. In this research, a novel posture measurement method for CIHs is proposed. Firstly, by introducing an ambiguity removal strategy, a coarse posture estimation method based on the plane hypothesis of the two end surfaces of CIHs is suggested. Secondly, considering that there is no simple explicit expression for a CIH’s edge, it is difficult to adopt the conventional model projection-based pose optimization method. In view of this, the 3D points corresponding to the edge pixels of the CIHs’ image are derived, and the pose optimization model is established by minimizing the deviations between the distance from the points to the CIHs’ axis and the hole radius. Moreover, to better control the direction parameters of CIHs during the global optimization process, the approximately perpendicular and intersection constraints between the CIHs’ axis and cylindrical component axis are involved in the solution. The effectiveness of the posture measurement method is verified by comparative experiments with current methods and coordinate measuring machine, which demonstrates improvements in both measurement accuracy and robustness.

Mobile Mapping System for Automatic Extraction of Geodetic Coordinates for Traffic Signs Based on Enhanced Point Cloud Reconstruction

Lidar sensors are commonly equipped on a mobile mapping system (MMS) to establish point clouds for HD map creation. However, the point clouds themselves do not contain object attributes. Therefore, human operators have to manually obtain objects’ position to assign attributes for further HD map conversion, inevitably resulting in time-consuming processes and significant labor costs. To solve the above problems, in this paper, we present an MMS equipped with non-survey grade Lidar, commercial grade camera, and entry level GNSS/INS, which incorporates ground control points (GCPs) with a Normal Distribution Transform Simultaneously Localization and Mapping (NDT SLAM) refinement and fluctuation adjustment to secure both absolute position accuracy and relative position accuracy of the reconstructed point cloud. Meanwhile, a deep neural network for image detection is employed to obtain the bounding box of traffic signs from each image frame. By applying the translation and rotation transformation between Lidar points and camera pixels, intersection of the detected object in the image and Lidar scan points can be found. By accumulating extracted Lidar points of the traffic sign in several detection frames, we can then obtain an accurate 3D geodetic coordinate of the traffic signs. Experimental results show that point clouds can be reconstructed with an average 3D RMSE of only 8.6cm, and center geodetic coordinates of traffic signs can be further extracted in sub-meter accuracy to significantly reduce labor work in HD map creation.

Planar Pose Estimation System Design via Explicit Spatial Representation Model of Concurrent AC Magnetic Fields

This study proposes a new approach to pose estimation algorithm and system implementation based on concurrent AC magnetic fields from an array of magnetic coils. The typical dipole model used in the existing research has inappropriate prediction characteristics when the operating range of dynamic objects is widened, as in the cases of mobile robots or drones. An enlarged circular loop with a meter-sized coil dimension and its corresponding excitation electronics are developed to overcome this shortcoming. In designing a pose estimation algorithm, a compact mathematical model is derived through the suggested spatial representation and the partial derivatives with respect to states. As a result, an accurate and computationally efficient pose estimation framework has been achieved, which validates the feasibility of the developed spatial representation model with actual magnetic measurements. While the proposed method can provide a general formula for a 3D dynamic case, yet a reduced model is specifically presented for a typical indoor 2D application. The performance of the proposed algorithm is demonstrated through the ground experiment in an indoor building environment with 50m2 coverage. Compared with the ground truth, static error variation from the low-order polynomial case is computed to be sub-centimeter, which allows an onboard algorithm implementation. Finally, the experiment via dynamic trajectory tests demonstrated the relative position accuracy of 1.2 % and heading error of about 2 degrees within the operating range.

A new method for resolving phase ambiguity in radio interferometry using Earth rotation synthesis

As a key technique in deep space navigation, radio interferometry can be used to determine the accurate location of a spacecraft in the plane-of-sky by measuring its signal propagation time delay between two remote stations. To improve the measurement accuracy, differential phase delay without phase ambiguity is usually desired. Aiming at the difficulties of resolving phase ambiguity with few stations and narrowband downlink signals, a new method is proposed in this work by taking advantage of the Earth rotation. The high accurate differential phase delay between the spacecraft and a calibrator can be achieved not only in the in-beam observation mode but also in the out-of-beam observation mode. In this paper we firstly built the model of phase ambiguity resolution. Then, main measurement errors of the model are analyzed, which is followed by tests and validations of the model and method using the tracking data of the Cassini mission and Chang’E-3 mission. The results show that the phase ambiguities can be correctly resolved to generate a 10-picosecond level accuracy differential phase delay. Angular measurement accuracy of the Cassini reaches the milli-arc-second level, and the relative position accuracy between the Chang’E-3 rover and lander reaches the meter level.

Local Superimpositions Facilitate Morphometric Analysis of Complex Articulating Structures.

SynopsisArticulating structures, such as the vertebrate skeleton or the segmented arthropod exoskeleton, comprise a majority of the morphological diversity across the eukaryotic tree of life. Quantifying the form of articulating structures is therefore imperative for a fuller understanding of the factors influencing biological form. A wealth of freely available 3D data capturing this morphological diversity is stored in online repositories such as Morphosource, but the geometric morphometric analysis of an articulating structure is impeded by arbitrary differences in the resting positions of its individual articulating elements. In complex articulating structures, where the angles between articulating elements cannot be standardized, landmarks on articulating elements must be Procrustes superimposed independently (locally) and then recombined to quantify variation in the entire articulating structure simultaneously. Here, we discuss recent advances in local superimposition techniques, namely the “matched local superimpositions” approach, which incorporates anatomically accurate relative sizes, positions, and orientations of locally-superimposed landmarks, enabling clearer biological interpretation. We also use simulations to evaluate the consequences of choice of superimposition approach. Our results show that local superimpositions will isolate shape variation within locally-superimposed landmark subsets by sacrificing size and positional variation. They may also create morphometric “modules” when there are none by increasing integration within the locally-superimposed subsets; however, this effect is no greater than the spurious between-module integration created when superimposing landmark subsets (i.e., articulating elements) together. Taken together, our results show that local superimposition techniques differ from conventional Procrustes superimpositions in predictable ways. Finally, we use empirical datasets of the skulls of wrasses and colubriform snakes to highlight the promise of local superimpositions and their utility. Complex articulating structures must be studied, and the only current solution to do so is local superimpositions.

CONSIDERING VARIANCES OF QUASI-RANDOM EFFECTS IN RELATIVE GPS POSITIONING PERFORMED DURING DAYTIME AND NIGHTTIME PERIODS – A NOVEL TWO-STAGE APPROACH

In this paper, a new two-stage approach, involving an integral treatement of all quasi-random effects limiting the accuracy of relative GPS positioning and the method of moments to obtain final variance components regarding the effects of short-term (“far-field”) multipath (factor b), joint action of long-term (“near-field”) multipath and receiver antenna phase center offset and variations (factor a1), as well as joint action of tropospheric and ionospheric refraction (factor a2), is presented. In the study, GPS data collected on five baselines were used. Variance components of the quasirandom effects were obtained for the three relative GPS coordinates (e, n and u) using individually monthly datsets including daytime- and those including nighttime-wise ambiguity-fixed baseline solutions. The related results show that statistically significant inequality exists when comparing corresponding variances obtained for daytime and nighttime periods. It turned out that the following standard deviation estimates intervals are present (by the coordinates e, n and u, respectively): (a) daytime period: 3.3–6.9, 4.6–9.0 and 9.1–20.3 mm (factor b); 1.5–4.7, 1.9–7.0 and 3.4–21.9 mm (factor 1a ); 0.0116– 0.3282, 0.0103–0.2365 and 0.1222–0.7818 mm/km (factor a2); (b) nighttime period: 3.2–4.9, 4.7–7.3 and 8.4–15.4 mm (factor b); 0.8–3.8, 2.1–5.0 and 3.1–15.8 mm (factor a1); 0.0118–0.2734, 0.0097–0.2289 and 0.0752–0.6315 mm/km (factor a2).

How NLOS Signals affect GNSS relative positioning

Owing to the existence of non-line-of-sight (NLOS) signals in global navigation satellite system (GNSS) challenged environment, the accuracy of GNSS relative positioning is seriously damaged. This paper demonstrates the effects of NLOS measurement error on GNSS relative positioning for short-baseline in both theory and field test. The results show that the effects of NLOS signal in both pseudorange and carrier phase make the ambiguity unable to be successfully fixed. In addition, we have found that after removing the labelled NLOS observations, the positioning accuracy can be significantly improved. The more the available LOS satellites, the more the robustness against NLOS satellites of the ambiguity resolution. This implies that GNSS Multi-Frequency Multi-System technology might improve the performance of GNSS relative positioning because there would be sufficient LOS signals after the NLOS signals are removed.

Highly reliable relative navigation for multi-UAV formation flight in urban environments

Formation flight of multiple Unmanned Aerial Vehicles (UAVs) is expected to bring significant benefits to a wide range of applications. Accurate and reliable relative position information is a prerequisite to safely maintain a fairly close distance between UAVs and to achieve inner-system collision avoidance. However, Global Navigation Satellite System (GNSS) measurements are vulnerable to erroneous signals in urban canyons, which could potentially lead to catastrophic consequences. Accordingly, on the basis of performing relative positioning with double differenced pseudoranges, this paper develops an integrity monitoring framework to improve navigation integrity (a measure of reliability) in urban environments. On the one hand, this framework includes a fault detection and exclusion scheme to protect against measurement faults. To accommodate urban scenarios, spatial dependence in the faults are taken into consideration by this scheme. On the other hand, relative protection level is rigorously derived to describe the probabilistic error bound of the navigation output. This indicator can be used to evaluate collision risk and to warn collision danger in real time. The proposed algorithms are validated by both simulations and flight experiments. Simulation results quantitatively reveal the sensitivity of navigation performance to receiver configurations and environmental conditions. And experimental results suggest high efficiency and effectiveness of the new integrity monitoring framework.

Assessing the Applicability of Mobile Laser Scanning for Mapping Forest Roads in the Republic of Korea

Forest roads are an essential facility for sustainable forest management and protection. With advances in survey technology, such as Light Detection and Ranging, forest road maps with greater accuracy and resolution can be produced. This study produced a 3D map for establishment of a forest road inventory using a Mobile Laser Scanning (MLS) device mounted on a vehicle in four study forest roads in Korea, in order to review its precision, accuracy and efficiency based on comparisons with mapping using Total Station (TS) and Global Navigation Satellite System (GNSS). We counted the points that consist of the cloud data of the maps to determine the degree of precision density, and then compared this with 50 points at 20-m intervals on the centerlines bisecting the widths of the study forest roads. Then, we evaluated the relative positional accuracy of the MLS data based on three criteria: the total length of each forest road; the Root Mean Square Error (RMSE) obtained from coordinate values of the MLS and TS surveys compared to the GNSS survey; and the ratios of the centerlines extracted by the MLS and TS surveys overlaid to the buffer zone by the GNSS survey. Finally, we estimated the time and cost per unit length for producing the map to examine the efficiency of MLS mapping compared to the other two surveys. The results showed that the point cloud data acquired by the MLS survey on the study forest roads had very high precision and so is sufficient to produce a 3D forest road map with high-precision density and a low RMSE value. Although the equipment rental cost is somewhat high, the fact that information targeting on all spatial elements of forest roads can be obtained with a low cost of labor is a benefit when evaluating the efficiency of MLS survey and mapping. Our findings are expected to provide a quantitative assessment of both maintaining sustainable effectiveness and preventing potential environmental damage of forest roads.

Composite formation flying strategy for distributed space telescopes with an ultralarge aperture

There is increasing demand for space telescopes with ultralarge apertures in the order of 100 to 1000 m for space exploration. It is difficult to expand a space telescope beyond 100 m because of the extensive structural controls that are needed to satisfy optical interference. Large apertures can be achieved using classical formation flying satellites, but it is difficult to achieve suitable relative position and altitude control. Therefore, a novel composite formation flying strategy for distributed space telescopes that provides an ultralarge aperture and ultrahigh accuracy is presented. The proposed strategy combines coarse and fine formations (FFs) provided by support and payload modules (PMs), respectively. The support and PMs are actively connected by noncontact voice coil actuators (VCAs) in each formation satellite. A model and a VCA collision avoidance control approach are also provided for the proposed satellites. The coarse formation is controlled by small thrusters and gives the relative position accuracy in the order of centimeters. The FF is controlled by the VCAs and gives the relative position accuracy in the order of micrometers. The performance is evaluated using simulation studies.

RESEARCH OF POSITIONING ACCURACY BASED ON PHOTOGRAMMETRY

The methods of ground and space geodesy allow to determine with millimeter accuracy the position of separate geodetic points on the Earth's surface. The hardware and software of the photogrammetry method make it possible to build a 3D digital model of the observed geodetic area. The purpose of this work is to compare and evaluate the accuracy of relative positioning using geodesy and photogrammetry methods within the Bishkek geodynamic area.

Graded Warning for Rear-End Collision: An Artificial Intelligence-Aided Algorithm

Realizing the ultra-low latency and high-accuracy solutions for rear-end collision is still challenging, especially under the condition in which many uncertainties exist. This paper proposes an artificial intelligence-based warning algorithm for rear-end collision avoidance. Three key issues are addressed by applying the neural network approach, including noises in positioning, inaccurate risk assessment, and enhanced comfort level of passengers. First, to filter the noises in positioning, wireless vehicular communications are leveraged; accurate relative lane positioning can be achieved to justify when two vehicles are in the same lane. Second, an online neural network model is developed to assess the risk of collisions in real time while driving. The algorithm can converge fast to a globally optimal solution and adapt to different traffic environments. Third, to maximize the comfort of passengers during the braking process, a graded warning strategy is developed at the prerequisite of guaranteed safety. With the above schemes sewed in to one framework, our proposal can achieve rear-end warning with reduced missing alarm rate, accurate risk assessment and enhanced comfort to passengers. The extensive simulations validate the effectiveness and accuracy of our proposal in terms of relative lane positioning, risk assessment, and collision avoidance.

An Innovative Smartphone-based Solution for Traffic Rule Violation Detection

This paper introduces a novel smartphone-based solution to detect different traffic rule violations using a variety of computer vision and networking technologies. We propose the use of smartphones as participatory sensors via their cameras to detect the moving and stationary objects (e.g., cars and lane markers) and understand the resulting driving and traffic violation of each object. We propose novel framework which uses a fast in-mobile traffic violation detector for rapid detection of traffic rule violation. After that, the smartphone transmits the data to the cloud where more powerful computer vision and machine learning operations are used to detect the traffic violation with a higher accuracy. We show that the proposed framework detection is very accurate by combining a) a Haar-like feature cascade detector at the in-mobile level, and b) a deep learning-based classifier, and support-vector machine-based classifiers in the cloud. The accuracy of the deep convolutional network is about 92% for true positive and 95% for true negative. The proposed framework demonstrates a potential for mobile-based traffic violation detection by especially by combining the information of accurate relative position and relative speed. Finally, we propose a real-time scheduling scheme in order to optimize the use of battery and real-time bandwidth of the users given partially known navigation information among the different users in the network, which us the real case. We show that the navigation information is very important in order to better utilize the battery and bandwidth for each user for a small number of users compared to the navigation trajectory length. That is, the utilization of the resources is directly related to the number of available participants, and the accuracy of navigation information.

Automated Georectification and Mosaicking of UAV-Based Hyperspectral Imagery from Push-Broom Sensors

Hyperspectral systems integrated on unmanned aerial vehicles (UAV) provide unique opportunities to conduct high-resolution multitemporal spectral analysis for diverse applications. However, additional time-consuming rectification efforts in postprocessing are routinely required, since geometric distortions can be introduced due to UAV movements during flight, even if navigation/motion sensors are used to track the position of each scan. Part of the challenge in obtaining high-quality imagery relates to the lack of a fast processing workflow that can retrieve geometrically accurate mosaics while optimizing the ground data collection efforts. To address this problem, we explored a computationally robust automated georectification and mosaicking methodology. It operates effectively in a parallel computing environment and evaluates results against a number of high-spatial-resolution datasets (mm to cm resolution) collected using a push-broom sensor and an associated RGB frame-based camera. The methodology estimates the luminance of the hyperspectral swaths and coregisters these against a luminance RGB-based orthophoto. The procedure includes an improved coregistration strategy by integrating the Speeded-Up Robust Features (SURF) algorithm, with the Maximum Likelihood Estimator Sample Consensus (MLESAC) approach. SURF identifies common features between each swath and the RGB-orthomosaic, while MLESAC fits the best geometric transformation model to the retrieved matches. Individual scanlines are then geometrically transformed and merged into a single spatially continuous mosaic reaching high positional accuracies only with a few number of ground control points (GCPs). The capacity of the workflow to achieve high spatial accuracy was demonstrated by examining statistical metrics such as RMSE, MAE, and the relative positional accuracy at 95% confidence level. Comparison against a user-generated georectification demonstrates that the automated approach speeds up the coregistration process by 85%.