Skyline based camera attitude estimation using a digital surface model

Quadrotor is one type of unmanned aerial vehicle that has the ability to vertical takeoff and landing. In this research, a system designed to stabilize quadrotor during flight condition by maintaining at angle of roll, pitch, yaw, and x, y, and z axis position using LQR full state feedback with artificial neural network (ANN).The LQR full state feedback method uses 12 states with each K constant being tuned with ANN. This research implements ANN method to change feedback constant at angle of roll, pitch, and yaw and x, y, and z axis. The artificial neural network method uses 12 input layers, 12 hidden layers, and 1 output layer.Testing with ANN improved the rise time to ± 2.18 seconds at the roll angle, ± 1.23 seconds at the pitch angle, and ± 0.31 seconds at the yaw angle. Improved settling time value up to ± 2.41 seconds at roll angle, ± 1.23 seconds at pitch angle, and ± 1.07 seconds at yaw angle. Improved steady state eror value of ± 0.61% at roll angle, ± 4.88% at pitch angle, and ± 0.82% at the yaw angle.

The purpose of this research is to realize precise straight motion. The straight motion system used in the paper has an ability to control five degrees of freedom of a moving table; vertical and horizontal positions, and roll, pitch and yaw angles. For the purpose, two references are introduced; motion reference and absolute reference. Table motions are controlled relatively to the motion reference. A solid master is used for the motion reference because of its stability against changes of environment. A geometrical error of the motion reference causes motion errors of the table. To compensate these errors, motion errors relative to the absolute reference are measured. The paper proposes the liquid surface for the absolute reference. The liquid surface is used only to measure motion errors caused by the geometrical error because the liquid surface is not robust enough. After the compensation, straight motion accuracies of 0.2μm in vertical position, 0.1arc sec in roll angle and 0.3arc sec in pitch angle were achieved.

Standard helicopters use the propeller angle to alter the aerodynamic force and have been shown to be feedback linearizable with the pitch, yaw and roll angles being the flat outputs. In this work, a-two-degree-of-freedom, laboratory-scale helicopter-like system (termed the toycopter), where the aerodynamic force is manipulated using the propeller speed, is considered. For such a system the yaw and pitch angles are not flat outputs contrarily of the real helicopters. Moreover, the system does not seem to be flat and is shown to admit at most a one dimensional defect. Various properties of this system are investigated to illustrate the difficulties that arise in the control of the toycopter.

This paper proposes new concepts of configuration and attitude control of underwater towed vehicle to perform operation in both vertical and horizontal axis with stable attitude.The principal configuration composed of fuselage, main wing, horizontal tail, vertical canard, upper and lower vertical tails designed taking into account stability and manoeuvrability in vertical and horizontal planes. The manoeuvrability was estimated by use of lamped mass method for equilibrium equations of motion of towed cable and vehicle. The dynamic stability was analysed by use of eigenvalue analysis based on linear equations of motion.CCV (Control Configured Vehicle) technology was adopted to perform the attitude control. Three types of decoupled mode were considered, i) pitch angle and vertical velocity, ii) pitch angular velocity and vertical velocity, iii) roll angle, yaw angle and side slip velocity. Fuzzy algorithm was applied to CCV attitude control.The effect of motion of towing mother ship in waves was simulated numerically based on non-linear equations of motion of cable and vehicle to examine the effectiveness of attitude control in vertical axis. Not only drifts of velocity components and pitch angle, but also oscillation of pitch angle are remarkably suppressed.The effect of tidal current was simulated numerically to examine the attitude control in horizontal axis. Oscillations of side slip velocity, roll angle and yaw angle are almost eliminated.

We demonstrate the creation of a large area of high-resolution (260 × 209 km2 at 1 m/pixel) DTM mosaic from the Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) images over the Chang’E-4 landing site at Von Kármán crater using an in-house deep learning-based 3D modelling system developed at University College London, called MADNet, trained with lunar orthorectified images and digital terrain models (DTMs). The resultant 1 m DTM mosaic is co-aligned with the Chang’E-2 (CE-2) and the Lunar Orbiter Laser Altimeter (LOLA)—SELenological and Engineering Explorer (SELENE) blended DTM product (SLDEM), providing high spatial and vertical congruence. In this paper, technical details are briefly discussed, along with visual and quantitative assessments of the resultant DTM mosaic product. The LROC NAC MADNet DTM mosaic was compared with three independent DTM datasets, and the mean differences and standard deviations are as follows: PDS photogrammetric DTM at 5 m grid-spacing had a mean difference of −0.019 ± 1.09 m, CE-2 DTM at 20 m had a mean difference of −0.048 ± 1.791 m, and SLDEM at 69 m had a mean difference of 0.577 ± 94.940 m. The resultant LROC NAC MADNet DTM mosaic, alongside a blended LROC NAC and CE-2 MADNet DTM mosaic and a separate LROC NAC, orthorectified image mosaic, are made publicly available via the ESA planetary science archive’s guest storage facility.

Dynamic responses of a floating dock under corrosion-induced accidents are studied using a numerical method. The numerical model is proposed to calculate the dynamic responses of the floating dock during operations. It includes a six-degree-of-freedom (6-DOF) model, a hydrostatic force model, a hydrodynamic force model, and a hydraulic model. The effects of the corrosion-induced holes on the stability of the floating dock are investigated and the results show that the maximum pitch and roll angles are 0.18° and 0.69° respectively when there is one hole located at one tank. The maximum pitch and roll angles become 0.42° and 2.04° respectively when there are two holes located at different tanks. The results indicate that situations involving more than one corroded hole result in large roll and pitch angles, which ultimately increase the risk of the vessel capsizing. This analysis not only emphasizes potential hazards but also presents an opportunity for the maritime sector to enhance safety, operational efficiency, and environmental responsibility.

Wind Engineering Analysis of Parabolic Trough Collectors to Optimise Wind Loads and Heat Loss

The gyroscope deterministic error can be estimated through laboratory test using rate table. However, the setting misalignment, if exists, degrade the estimation accuracy severely. To solve this problem, for the simple error model which contains sensor misalignment and bias, a setting misalignment error compensation method is proposed in this paper. This method based on the fact that the setting misalignment can be represented by Euler angle: roll, pitch, and yaw angles. The identification of the three angles is performed step by step. For each pitch and yaw angle, we use the fact that the one specific element of misalignment matrix multiplied by roll matrix is exactly 1 and find pitch and yaw angle which make the estimate of the specific element closely 1. Next, we estimate roll angle with the same procedure. Finally, we estimate the sensor misalignment matrix using the estimated Euler angles. Thorough simulation, the proposed method is verified

We have been developing a rice-duck farming robot. The robot has functions of cackling, wing flapping, and feeding for gathering and leading real ducks. Our previous research results showed that these functions effectively gather and lead real ducks for weed areas in a paddy field. The robot has a tilt sensor and an acceleration sensor for measuring pitch, roll, and yaw angles of the robot's body, and has a front view camera for autonomous locomotion control. We designed and implemented a pitch angle control system and a roll angle control system for the robot. By using the roll angle of the robot's body, control parameters for crawlers are revised for stabilization of autonomous locomotion. By using the pitch angle of the robot's body, pitch angle of the front view camera is revised for stabilization of appropriate image processing and autonomous locomotion. The stabilization of autonomous locomotion in marshy paddy fields was verified through experiments in an indoor rice paddy.

<p>Gully erosion is regarded as one of the worst land degradation processes in the world. Rapid identification of the location of gully features is urgently required, to aid in recognizing regions where gully erosion is prominent. Manual digitizing of gully features is both time consuming and prone to bias. Generating semi-automated or automated workflows to detect gully erosion allows quick and unbiased mapping of gully features over large extents.</p><p>In the Sandspruit catchment, South Africa, contour banks with a combined length of approximately 25000km have been constructed to mitigate soil erosion. Gullies are now mostly confined to narrow slivers in the natural vegetation, fynbos and Renosterveld, between agricultural fields. The morphological similarity and proximity of contour banks and gullies in this region provides a good test site to evaluate whether a semi-automated detection workflow could map gullies in complex, rough agricultural terrain.</p><p>Here, a Digital Surface Model (DSM) with a spatial resolution of 2m was used to test a semi-automated detection workflow in a Geographical Information System (GIS) environment. Two main building blocks were generated from the DSM: 1) a normalized DSM, created by subtracting a convolved mean DSM with a designated filter size from the original DSM, and 2) local slope generated from the normalized DSM. Subsequently, using expert knowledge, mapped gully polygons were refined and smoothed, by threshold determination, masking features not related to drainage, and pixel-based growing and shrinking. The semi-automated workflow was completed for two different spatial resolutions: 1) the native 2m-resolution and 2) a 0.5m-resolution DSM, upsampled without producing artificial values from interpolation methods. A GeoEye-1 image with a spatial resolution of 0.5m was included at the backend of the workflow as an additional step, to test whether gully mapping from using terrain attributes only, could be improved upon.     </p><p>Gully detection from terrain attributes only, achieved an overall accuracy of 0.68 (0.5m DSM) and 0.74 (2m DSM) with kappa values ranging from 0.36 (0.5m DSM) and 0.35 (2m DSM). The upsampled 0.5m DSM performed worse than the native 2m DSM due to increased noise detection. Although reasonable performance was obtained from the 2m DSM, issues encountered include: 1) vegetation that caused some inaccuracies in gully boundary delineation and discontinuities along gully channels and 2) false positive detection of contour banks. The addition of the GeoEye-1 image increased overall accuracy to 0.79 and kappa value to 0.5, mostly because of the elimination of false positives in agricultural fields.</p><p>The accuracy statistics indicate that the semi-automated detection workflow developed here shows promise as a tool to detect gully erosion on a catchment scale. Furthermore, due to the workflow being built upon the distinct morphology of gully features, it could be transferable to other regions that are dissimilar to the Sandspruit catchment. The transferability of the workflow should be tested in future, in addition to how accuracy would be affected if the DSM were substituted with a Digital Terrain Model (DTM) of similar spatial resolution.</p>

This article presents a review of the combined analysis of digital terrain models (DTMs) and remotely sensed data in landscape investigations. The utilization of remotely sensed data with DTMs has become an important trend in geomatics in the past two decades. Models of more than ten quantitative topographic variables are employed as ancillary data in the treatment of images. The article reviews the methods for DTM derivation and the basic problems of DTM operation that are important for handling DTMs with imagery, namely: 1) the choice of a DTM network type; 2) DTM resolution; 3) DTM accuracy; and 4) the precise superimposition of DTMs and images. The processing of remotely sensed data and DTMs in combination is used in the following procedures: 1) the image correction of the topographic effect; 2) the correction of geometric image distortion; 3) image classification; 4) statistical and comparative analyses of landscape data; and 5) three-dimensional landscape modelling. These procedures are applied to solve a wide range of problems in geobotany, geochemistry, soil science, geology, glaciology and other sciences. The joint use of imagery and DTMs can increase the total amount of information extracted from both types of data. The trend has been towards the incorporation of the combined analysis of remotely sensed data and DTMs into mixed environmental models. The following potential applications of the treatment of imagery in association with DTMs are identified: 1) the prediction of the migration and accumulation zones of water, mineral and organic substances moved by gravity along the land surface and in the soil; 2) the investigation of the relationships between topographically expressed geological structures and landscape properties; 3) the improvement of geological engineering in industrial planning (e.g., the construction of nuclear power stations, oil and gas pipelines and canals); and 4) the monitoring of existing industries. Digital models of plan, profile, mean and total accumulation curvatures, and nonlocal and combined topographic attributes should be included in data processing both to solve the problems indicated and to improve the outcome of some regular tasks (for example, the prediction of soil moisture distribution and fault recognition).

The correct representation of the topography of terrain is an important requirement to generate photogrammetric products such as orthoimages and maps from high-resolution (HR) or very high-resolution (VHR) satellite datasets. The refining of the digital elevation model (DEM) for the generation of an orthoimage is a vital step with a direct effect on the final accuracy achieved in the orthoimages. The refined DEM has potential applications in various domains of earth sciences such as geomorphological analysis, flood inundation mapping, hydrological analysis, large-scale mapping in an urban environment, etc., impacting the resulting output accuracy. Manual editing is done in the presented study for the automatically generated DEM from IKONOS data consequent to the satellite triangulation with a root mean square error (RMSE) of 0.46, using the rational function model (RFM) and an optimal number of ground control points (GCPs). The RFM includes the rational polynomial coefficients (RPCs) to build the relation between image space and ground space. The automatically generated DEM initially represents the digital surface model (DSM), which is used to generate a digital terrain model (DTM) in this study for improving orthoimages for an area of approximately 100 km2. DSM frequently has errors due to mass points in hanging (floating) or digging, which need correction while generating DTM. The DTM assists in the removal of the geometric effects (errors) of ground relief present in the DEM (i.e., DSM here) while generating the orthoimages and thus improves the quality of orthoimages, especially in areas such as Dehradun that have highly undulating terrain with a large number of natural drainages. The difference image of reference, i.e., edited IKONOS DEM (now representing DTM) and automatically generated IKONOS DEM, i.e., DSM, has a mean difference of 1.421 m. The difference DEM (dDEM) for the reference IKONOS DEM and generated Cartosat-1 DEM at a 10 m posting interval (referred to as Carto10 DEM) results in a mean difference of 8.74 m.

A digital elevation model (DEM) is a digital model or 3D representation of a terrain’s surface. There are many methods to create DEM such as LiDAR, stereo photogrammetry and topographic maps. DEMs are very important for many applications such as extracting terrain parameters for geomorphology and modeling water flow for hydrology or mass movement. A number of websites are available to provide DEM such as SRTM, GTOPO30 and ASTER GDEM but their accuracy differs from one to another and also selecting a small DEM size (high resolution) gives accurate information, but the analysis takes long time. This paper aims to analyze the impact of using different available DEMs on watershed geomorphological properties on order to provide guidelines for users to select the most suitable DEM that obtain an accurate analysis in less time. Three programs; watershed modeling systems: WMS, Global Mapper and Google Earth were used in this study. Three case studies were studied to check the accuracy of these models and select the most accurate one for application. Satellite images downloaded from Google Earth were used as a guide reference for the comparison due to their accuracy and high resolution. The results indicated that the SRTM model was more accurate (95%) for all case studies according to our comparison between its delineation and satellite images. ASTER GDEM is the second most accurate model with an accuracy of 87%, the GTOPO30’s accuracy is 80%.

The study at the Sanctuary of Eukleia in Aigai (Vergina, Greece) evaluates the planimetric and vertical accuracy of Digital Surface Model (DSM) generated by a Hesai XT32M2X LiDAR system mounted on UAS WingtraOne GEN II. The paper begins by outlining the evolution of UAS-LiDAR, then describing the acquisition of RGB, multispectral (MS) images and LiDAR data. Twenty-two Check Points (CPs) were measured using an RTK-GNSS receiver, which also served to establish the PPK calibration base point. This is followed by processing the images to generate DSMs and orthophotomosaics, as well as processing the LiDAR point cloud to produce both DSM and DTM products. The DSMs and orthophotomosaics were evaluated by comparing field-measured CP coordinates with those extracted from the products, computing mean values and standard deviations. RGB images yielded DSMs and orthophotomosaics with planimetric accuracy of 1.4 cm (with a standard deviation σ = ±1 cm) in X, 0.9 cm (with σ = ±0.9 cm) in Y and a vertical accuracy of 2.4 cm (with σ = ±1.7 cm). The LiDAR-derived DSM achieved similar planimetric accuracy and an overall vertical accuracy of 7.5 cm (with σ = ±6 cm). LiDAR’s ability to penetrate vegetation enabled near-complete mapping of a densely vegetated streambank, highlighting its clear advantage over images. While high-precision RGB-PPK products can surpass LiDAR in vertical accuracy, UAS-LiDAR remains indispensable for under-canopy terrain mapping.

The installation conditions of fluidic oscillators embedded in a hump surface were numerically investigated to improve the separation control of the flow over the hump. Unsteady flow analysis was performed using three-dimensional Reynolds-averaged Navier–Stokes equations with a modified shear stress transport model. The numerical results were validated by comparing them to experimental data. Five geometric parameters were studied in a parametric study: the installation location, staggered location, pitch angle, roll angle, and yaw angle of the fluidic oscillators. The highest flow control performance was obtained when in-line fluidic oscillators were located just downstream of the separation point. Nonzero roll and yaw angles had favorable effects on the separation control, and the flow control was most sensitive to the pitch angle among the installation parameters.