Vision-aided UAV navigation using GIS data

Data acquired from site surveys provide an essential resource to various exploration and production activities, such as field development planning, well positioning, and subsea equipment positioning. For many years, site survey data has been delivered as a Computer-aided Design (CAD) product. As operators move into a Geographic Information Systems (GIS) environment, shortfalls from CAD-based workflows have become obvious. This paper presents a method of managing site survey data based on geo-information management principles and practices. The methodology presented in this paper defines a GIS data model for seabed survey data interpretation that satisfies GIS query, presentation, and data exchange purposes. This paper also defines a standard for GIS seabed survey data deliverables from contractors to operators, including methods and processes to move the model to/from CAD-based applications. The implementation of the data model has proven to be a relative success and allows for a better integration of subsurface applications internally. The results have helped define an efficient contractor-operator data exchange process by eliminating data ambiguity and enabling seamless GIS data transfer. Results have also produced a more intelligent seabed survey GIS dataset with rules and behaviors capability offered by an ArcGIS Geodatabase embedded into the data model. Use of a standard GIS data model and a standard list of GIS data deliverables required from contractors to operators will allow project teams to fully embrace the power of GIS data integration and allow nearly seamless and lossless movement of CAD/GIS representations of the model. Introduction In order to reach maximum potential benefit of data provided from seabed site surveys throughout the E & P lifecylcle (Figure 1), there is a need to ensure that data delivered from survey contractors to operators is done so in a structured, efficient, and consistent manner.

Optical flow (OF) plays a decisive role in visual situation awareness, detection and obstacle avoidance systems for unmanned aerial vehicles (UAVs), which are cyberphysical systems (CPSs) that interact with the environment through sensors and actuators. The use of cameras allows the integration of computer vision (CV) algorithms with the inertial navigation systems (INS). The movement of characteristics of the image fused with the dynamic of the UAVs allows us to improve the process of remoting sense, avoid obstacles or estimate the position and velocity of the UAV. In the literature, there are various algorithms to locate characteristics points between two consecutive images. However, the computation time and consumption of physical resources such as memory features are due to embedded systems. This chapter shows (i) how to integrate the movement of the pixel textures (OF) in the image with INS data, (ii) compares different algorithms to match points between consecutive images, (iii) implements a process to encounter points between consecutive images and (iv) implements a computationally less expensive and with less memory consumption algorithm. A case study about using the field-programmable gate array (FPGA) as part of the visual servoing is discussed showing how to integrate results into the CV hardware system of a UAV and addressing the need to handle issues such as multi-resolution.

In this paper, we develop an alternative navigation system for Unmanned Aerial Vehicle (UAV) in Global Positioning Systems (GPS)-denied environment. We use two image inputs, one is acquired with an on-board camera placed on the UAV (which is the large-area image) and the other is from satellite images (which is small known image) with GPS information. We use a convolutional neural network (CNN) architecture based on Oxford’s Visual Geometry Group network (VGG-16) and utilize normalized variant mutual information between these two images to obtain position of the UAV. Satellite images are labelled and given to the UAV. When GPS information is lost, our algorithm starts to function and images from UAV camera are searched whether satellite image is seen by cameras on UAV image or not. If the UAV is in that area, our algorithm finds the GPS information from satellite image data.

The geographical information system (GIS) has provided the effective tools to capture, store, manage, analyze and express the spatial information. Gradually, the research region has included the dynamical mechanism and the spatialtime characteristics of the geography process, transmission mechanisms, uncertainty (multi-solutions), predictability, and so on. Corresponding, GIS modeling is one of hotspots of current GIS research. The paper is the embodiment of GIS modeling methods applied to geo-simulation research. Also it provides the model support for the management of the ground water resources. In this paper, the methods for constructing the ground-water conceptual model through 3D stratum model are discussed. Firstly, the constructing method of delaunay triangulation with constraint conditions is proposed, which is suitable to the ground-water flow simulation. Then stratum (or aquifer) multi-DEMs are completed. Based on the multi-DEMs, the Su-Xi-Chan hydrogeological conceptual model is reconstructed by irregular Tri-prism. In order to prevent dispersed irregular tri-prism voxels from disunity, the spatial index mechanism is established for groundwater model. Finally, the 3D groundwater simulation model is integrated with GIS, which can provide data for 3D numerical simulation. The results indicate that the integration of 3D GIS and geo-analysis model be convenient and can accelerate process simulation of geo-science.

Drones are becoming increasingly significant for vast applications, such as firefighting, and rescue. While flying in challenging environments, reliable Global Navigation Satellite System (GNSS) measurements cannot be guaranteed all the time, and the Inertial Navigation System (INS) navigation solution will deteriorate dramatically. Although different aiding sensors, such as cameras, are proposed to reduce the effect of these drift errors, the positioning accuracy by using these techniques is still affected by some challenges, such as the lack of the observed features, inconsistent matches, illumination, and environmental conditions. This paper presents an integrated navigation system for Unmanned Aerial Vehicles (UAVs) in GNSS denied environments based on a Radar Odometry (RO) and an enhanced Visual Odometry (VO) to handle such challenges since the radar is immune against these issues. The estimated forward velocities of a vehicle from both the RO and the enhanced VO are fused with the Inertial Measurement Unit (IMU), barometer, and magnetometer measurements via an Extended Kalman Filter (EKF) to enhance the navigation accuracy during GNSS signal outages. The RO and VO are integrated into one integrated system to help overcome their limitations, since the RO measurements are affected while flying over non-flat terrain. Therefore, the integration of the VO is important in such scenarios. The experimental results demonstrate the proposed system’s ability to significantly enhance the 3D positioning accuracy during the GNSS signal outage.

In this chapter, navigation systems for unmanned aerial vehicles (UAVs) are given. In this context, inertial navigation, air data system, surface radar, satellite radio navigation systems, Doppler and altimeter radars, magnetic sensors, vision-based systems, and simultaneous localization and mapping are described. Moreover, measurement faults for these systems, when they are used in UAVs, are investigated and details for the fault modeling are presented.

In this paper, one approach of Geographic Information System (GIS) and Remote Sensing (RS) assessment of potential natural hazard areas (excess erosion, landslides, flash floods and fires) is presented. For that purpose Pehchevo Municipality in the easternmost part of the Republic of Macedonia is selected as a case study area because of high local impact of natural hazards on the environment, social-demographic situation and local economy. First of all, most relevant static factors for each type of natural hazard are selected (topography, land cover, anthropogenic objects and infrastructure). With GIS and satellite imagery, multi-layer calculation is performed based on available traditional equations, clustering or discreditation procedures. In such way suitable relatively “static” natural hazard maps (models) are produced. Then, dynamic (mostly climate related) factors are included in previous models resulting in appropriate scenarios correlated with different amounts of precipitation, temperature, wind direction etc. Finally, GIS based scenarios are evaluated and tested with field check or very fine resolution Google Earth imagery showing good accuracy. Further development of such GIS models in connection with automatic remote meteorological stations and dynamic satellite imagery (like MODIS) will provide on-time warning for coming natural hazard avoiding potential damages or even causalities.

Accurate and robust state estimation is critical for the heterogeneous agent systems, particularly when considering the challenges posed by Unmanned Aerial Vehicles (UAVs) operating in perceptually-degraded environments where access to Global Navigation Satellite System (GNSS) signals is lost. We can, however, actively increase the amount of optimal localization available to UAV by augmenting them with a small number of more expensive, but less resource-constrained, heterogeneous agents. In this paper, we propose a novel detection, localization, and tracking framework for UAV based on LiDAR. First, we present an innovative approach that integrates range image projection and Depth Cluster of LiDAR point clouds with UAV technology. Subsequently, we devise a multidimensional feature probability detection and tracking evaluation function, enabling the detection, estimation, and active tracking of UAV movement. Finally, we conduct comprehensive experiments using heterogeneous agent systems to assess the effectiveness and robustness of the developed framework. The experiments reveal a minimum 20% reduction in running time and an average localization accuracy error of 1.98[Formula: see text]cm.

In this paper the fusion between the data derived from the Global Positioning System (GPS) and the Inertial navigation System (INS) is integrated in a Sense And Avoid (S&A) system for Unmanned Aerial Vehicle (UAV) to increase its performances. The avoidance manoeuvre is defined in order to provide a minimum separation between the ownship and all the other agents during its overall execution in multiple flying threats scenarios. This is achieved by aiding the GPS measurements with the INS to estimate the ownship position using an Extended Kalman Filter (EKF) which uses a linear error model to estimate the errors in the INS states. The resolution manoeuvre are defined as step variations in the heading angle and altitude commands of the Flight Path Control System (FPCS). The value of these commands is optimised in order to get the minimum step command value necessary to keep a minimum predefined separation between the ownship and all the threats.

The classification of drones - both military and civilian according to the relevant criteria - is presented. Block diagrams of a typical unmanned aerial vehicle and its power system are shown. Typically, a drone power system of the "micro", "mini" or "short range" category, according to the UVS international classification, is a set of DC / DC converters with a microprocessor control system. The power source for such aircraft is usually a battery, less often - fuel cells. During the flight, the engine speed is variable, and its change depends not only on changes in flight speed or direction, but also on weather conditions, such as wind, as the stabilization system constantly aligns the drone. This leads to the fact that the power supply system of such a device is actually a significant part of the time in transition condition. This leads to a significant content of the component of exchange energy that battery consumed which in turn will increase losses, and thus reduces the range of the unmanned aerial vehicle. The phenomenon of the occurrence of exchange power in the power supply systems of electric vehicles which powered from DC sources and specifically in the power supply system of an unmanned aerial vehicle is analyzed. The time diagrams of current, voltage, active and inactive components of power consumed from the mains power supply network of electric locomotive DE1 are illustrated. It can be seen that with a sharp change in the modes of operation of traction units there is a significant component of exchange power in the motor-network system. Such processes are typical for almost any electric vehicle and are associated with the presence of a significant number of reactive elements in the power supply systems, as well as frequent changes in load parameters. A block diagram of a typical unmanned aerial vehicle is presented. The unmanned aerial vehicle system consists of three parts: the air part, the unmanned aerial vehicle itself, the ground control station, which can be autonomous or manned, the control system, which provides communication and data transmission. The block diagram of the power system of a typical unmanned aerial vehicle is presented A simplified schematic diagram of the DC motor power supply system is presented. The relations for determining the amount of exchange power in the power supply system of an unmanned aerial vehicle are derived. It is concluded that to reduce the impact of this phenomenon, it is necessary to modify the power supply system by adding compensation units of inactive power component.

City/regional authorities are responsible for designing and structuring the urban morphology based on the desired land use activities. One of the key concerns regarding urban planning is to establish certain development goals, such as the real gross domestic product (GDP). In Canada, the gross national income (GNI) mainly relies on the mining and manufacturing industries. In order to estimate the impact of city development, this study aims to utilize remote sensing and Geographic Information System (GIS) techniques to assess the relationship between the built-up area and the reported real GDP of seven major cities in Canada. The objectives of the study are: (1) to investigate the use of regression analysis between the built-up area derived from Landsat images and the industrial area extracted from Geographic Information System (GIS) data; and (2) to study the relationship between the built-up area and the socio-economic data (i.e., real GDP, total population and total employment). The experimental data include 42 multi-temporal Landsat TM images and 42 land use GIS vector datasets obtained from year 2005 to 2010 during the summer season (June, July and August) for seven major cities in Canada. The socio-economic data, including the real GDP, the total population and the total employment, are obtained from the Metropolitan Housing Outlook during the same period. Both the Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI) were used to determine the built-up areas. Those high built-up values within the industrial areas were acquired for further analysis. Finally, regression analysis was conducted between the real GDP, the total population, and the total employment with respect to the built-up area. Preliminary findings showed a strong linear relationship (R2= 0.82) between the percentage of built-up area and industrial area within the corresponding city. In addition, a strong linear relationship (R2= 0.8) was found between the built-up area and socio-economic data. Therefore, the study justifies the use of remote sensing and GIS data to model the socio-economic data (i.e., real GDP, total population and total employment). The research findings can contribute to the federal/municipal authorities and act as a generic indicator for targeting a specific real GDP with respect to industrial areas.

Geographic Information Systems (GIS) data are crucial for many applications such as disaster response, damage assessment, and recovery efforts. The 2010 Haiti earthquake destroyed buildings where GIS data were stored and many of the relief organizations ultimately depended on data retrieved from outside the country to do their work. The lesson learnt from this experience is that open access data is necessary in assisting tasks in such situations and therefore the evaluation of such data for its quality is important. This research evaluates OpenStreetMap (OSM) data that can be and is used by various organizations. The methodology followed is based on the International Organization for Standardization (ISO) Geographic Information-Quality Principles (ISO19113). According to the ISO19113 standard, the data quality elements are: completeness, logical consistency, positional accuracy, thematic accuracy, and temporal accuracy. The first four elements noted above were tested in this study. OSM data was compared with official data from Abu Dhabi city. Results show that the positional accuracy of OSM data meets the American Society for Photogrammetry and Remote Sensing (ASPRS) standards for 1:20,000-scale maps. This indicates that OSM data could be used for many applications, taking into account the hurdles in GIS data acquisition, cost, availability, licensing, distribution policies, and documentation. The article concludes with a call for incorporation of geospatial data services and data quality initiatives in libraries.

Geographic Information System (GIS) is a method of expressing objects in a space. Currently, many research and developments are being conducted to implement 3D GIS. In previous studies, 3D GIS applications have been developed using Unity 3D, which is a 3D engine with good development accessibility. However, it requires manual work to enter various formats of GIS data, making it difficult to immediately reflect GIS data that change frequently. To improve this problem, this study developed a method for automatically reading and outputting various GIS data from the existing Unity 3D application. The improved application could read Satellite Images, Aerial Photographs, Digital Elevation Models (DEM) and Shapefiles with no transformation through other commercial programs, and they could be implemented as 3D objects. This study automated the GIS data conversion which had been manually performed and as a result, the manpower, time, and resources required for 3D GIS implementation can be saved.

A GIS model for personal exposure to PM10 for Dublin commuters

The objective of this paper is to argue for a new model of Geographic Information System (GIS) implementation, Web-based GIS, for supporting decision-making in local government. In this paper, the GIS implementation in Ascension Parish Government, Louisiana is used as a case study to show how Web-based GIS implementation can improve the system use and maximize the value of GIS infrastructures for the community. The author first defines the concept of implementation and role of GIS in local government as a base for further discussion. Special attention is given to analyze the variables that influence the parish employees’ acceptance and use of the GIS in the work. A three-level evaluation framework is established to evaluate GIS implementation strategy and GIS impacts on decision-making in local government. Research findings will be used to direct future implementation strategy. The paper concludes that Web-based GIS, by combining GIS technology and Internet technology, can distribute GIS resources and geo-processing tools to a wider range of potential users. In addition, flexible user-oriented GIS applications can best be developed with intensive user participation and feedback. One of the arguments proposed is that the Web-based GIS can be used to help the local government employees to provide better service for the public and improve the performance of a specific local government agency in processing land development permit applications.