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Evaluation of Remote Sensing Aerial Systems In Existing Transportation Practices 1. Report No. 2. Government Accession No. 3. Recipient's Catalog No. 4. Title and Subtitle 5. Report Date Evaluation of remote Sending Aerial Systems In Existing Transportation October 2009 Practices 6. Performing Organization Code 7. Author(s) 8. Performing Organization Report No. Yu Gu WVU-2008-01 9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) West Virginia University 11. Contract or Grant No. PO Box 6103 Morgantown, WV 26505 DTRT07-G-0003 12. Sponsoring Agency Name and Address 13. Type of Report and Period Covered U.S. Department of Transportation Research and Innovative Technology Administration Final Report 07/01/2008-06/30/2009 UTC Program, RDT-30 14. Sponsoring Agency Code 1200 New Jersey Ave., SE Washington, DC 20590 West Virginia Department of Highway Donny Williams Building 5 1900 Kanawha Blvd E Charleston, WV 25305 15. Supplementary Notes 16. Abstract The application of small Remotely-Controlled (R/C) aircraft for aerial photography presents many unique advantages over manned aircraft due to their lower acquisition cost, lower maintenance issue, and superior flexibility. The extraction of reliable information from these images could benefit DOT engineers in a variety of research topics including, but not limited to work zone management, traffic congestion, safety, and environmental. During this effort, one of the West Virginia University (WVU) R/C aircraft, named 'Foamy', has been instrumented for a proof-of-concept demonstration of aerial data acquisition. Specifically, the aircraft has been outfitted with a GPS receiver, a flight data recorder, a downlink telemetry hardware, a digital still camera, and a shutter-triggering device. During the flight a ground pilot uses one of the R/C channels to remotely trigger the camera. Several hundred high-resolution geo-tagged aerial photographs were collected during 10 flight experiments at two different flight fields. A Matlab based geo-reference software was developed for measuring distances from an aerial image and estimating the geo-location of each ground asset of interest. A comprehensive study of potential Sources of Errors (SOE) has also been performed with the goal of identifying and addressing various factors that might affect the position estimation accuracy. The result of the SOE study concludes that a significant amount of position estimation error was introduced by either mismatching of different measurements or by the quality of the measurements themselves. The first issue is partially addressed through the design of a customized Time-Synchronization Board (TSB) based on a MOD 5213 embedded microprocessor. The TSB actively controls the timing of the image acquisition process, ensuring an accurate matching of the GPS measurement and the image acquisition time. The second issue is solved through the development of a novel GPS/INS (Inertial Navigation System) based on a 9-state Extended Kalman Filter (EKF). The developed sensor fusion algorithm provides a good estimation of aircraft attitude angle without the need for using expensive sensors. Through the help of INS integration, it also provides a very smooth position estimation that eliminates large jumps typically seen in the raw GPS measurements. 17. Key Words 18. Distribution Statement Remote Controlled Aircraft, Aerial Photography, Aerial data accusation No restrictions. This document is available from the National Technical Information Service, Springfield, VA 22161 19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of Pages 22. Price Unclassified Unclassified 1 Evaluation of Remote Sensing Aerial Systems In Existing Transportation Practices Abstract The application of small Remotely-Controlled (R/C) aircraft for aerial photography presents many unique advantages over manned aircraft due to their lower acquisition cost, lower maintenance issue, and superior flexibility. The extraction of reliable information from these images could benefit DOT engineers in a variety of research topics including, but not limited to work zone management, traffic congestion, safety, and environmental. During this effort, one of the West Virginia University (WVU) R/C aircraft, named 'Foamy', has been instrumented for a proof-of-concept demonstration of aerial data acquisition. Specifically, the aircraft has been outfitted with a GPS receiver, a flight data recorder, a downlink telemetry hardware, a digital still camera, and a shutter-triggering device. During the flight a ground pilot uses one of the R/C channels to remotely trigger the camera. Several hundred high-resolution geo-tagged aerial photographs were collected during 10 flight experiments at two different flight fields. A Matlab based geo-reference software was developed for measuring distances from an aerial image and estimating the geo-location of each ground asset of interest. A comprehensive study of potential Sources of Errors (SOE) has also been performed with the goal of identifying and addressing various factors that might affect the position estimation accuracy. The result of the SOE study concludes that a significant amount of position estimation error was introduced by either mismatching of different measurements or by the quality of the measurements themselves. The first issue is partially addressed through the design of a customized Time-Synchronization Board (TSB) based on a MOD 5213 embedded microprocessor. The TSB actively controls the timing of the image acquisition process, ensuring an accurate matching of the GPS measurement and the image acquisition time. The second issue is solved through the development of a novel GPS/INS (Inertial Navigation System) based on a 9-state Extended Kalman Filter (EKF). The developed sensor fusion algorithm provides a good estimation of aircraft attitude angle without the need for using expensive sensors. Through the help of INS integration, it also provides a very smooth position estimation that eliminates large jumps typically seen in the raw GPS measurements. 2 Table of Contents Abstract ........................................................................................................................................... 2 Table of Contents ............................................................................................................................ 3 List of Figures ................................................................................................................................. 4 List of Tables .................................................................................................................................. 4 1. Introduction ................................................................................................................................. 5 1.1. Background .......................................................................................................................... 5 1.2. Regulations .......................................................................................................................... 8 1.3. Objective of the Project ..................................................................................................... 10 1.4. Organization of the Report................................................................................................. 11 2. Aerial Platform Development and Flight Testing ..................................................................... 11 3. Data Processing and Error Analysis ......................................................................................... 16 3.1 Geo-Referencing Software.................................................................................................. 16 3.2. Source of Error ................................................................................................................... 23 3.3. Time-Synchronization Board ............................................................................................. 27 4. GPS/INS Sensor Fusion ............................................................................................................ 29 5. Conclusions ............................................................................................................................... 36 References ..................................................................................................................................... 38 3 List of Figures Fig. 1. Foamy Aircraft Configuration........................................................................................... 12 Fig. 2. A Close Up of the Aircraft Internal Configuration ........................................................... 12 Fig. 3. Flight Trajectory Over the Friendship Hill....................................................................... 15 Fig. 4. A Single Aerial Photo ........................................................................................................ 15 Fig. 5. A Mosaic of 16 Aerial Images ........................................................................................... 16 Fig. 6. GUI for Point-and-Click Acquisition of Points, Black Arrow is the North-Direction ...... 20 Fig. 7. Aerial Photo of Two Cessna Aircraft ................................................................................ 22 Fig. 8. Output of the Script ........................................................................................................... 23 Fig. 9. Angle Errors vs Position Error ......................................................................................... 25 Fig. 10. Superimposition of Object Before and After Distorsion Correction ............................... 27 Fig. 11. Time-Synchronization Board (Circuitry) ........................................................................ 28 Fig. 12. Board Connected to the Camera ..................................................................................... 29 Fig. 13. EKF Roll Angle Estimation ............................................................................................. 35 Fig. 14. EKF Pitch Angle Estimation ........................................................................................... 35 Fig. 15. EKF Position Estimation (Z-axis) ................................................................................... 36 List of Tables Table 1. General Specifications of the WVU 'Foamy' Aircraft .................................................... 11 Table 2. Camera Specifications .................................................................................................... 13 Table 3. Eagle Tree Specifications ............................................................................................... 14 4 Evaluation of Remote Sensing Aerial Systems In Existing Transportation Practices 1. Introduction 1.1. Background Aerial photography has long been used for tactical assessment and planning purposes, relying primarily on manned aircraft. It has also found various applications in transportation infrastructure planning and monitoring in the civilian sector. However, the higher cost and risks to humans associated with the use of manned aircraft for such purposes has made it prohibitive for extensive applications. The associated logistic burden and preparation time for a full-scale aircraft also limited its use during emergency situations. The rapid evolution of the small Remotely Piloted Vehicle (RPV) and Unmanned Aerial Vehicle (UAV), coupled with the miniaturization of sensors, computers, and communication equipments, has led to an increased use of this class of platforms in the civilian sector, especially within the framework of Intelligent Transportation Systems (ITS). Typical data that are collected with an airborne platform are aerial photographs of roads and other transportation infrastructure. For example, researchers from Bridgewater State College used regular off the shelf film cameras to capture images of parking lots associated with park and ride shuttles. The camera was mounted on a small hand launched Micro Aerial Vehicle (MAV) controlled by a ground-based pilot with the aid of a video downlink from the vehicle [1]. In another application, UAVs were used to provide a cost effective "system solution" for use in aerial surveillance, right of way monitoring and leak detection of pipelines [2]. The use of UAVs in law enforcement related to traffic management has been gaining traction, especially in foreign countries. In Israel, real time 5 video footage of traffic violations are captured from small unmanned helicopters and transmitted to patrol cars to apprehend traffic violators [3]. The video footage from the UAV is considered to be admissible evidence in a court of law and is used to support the testimony of law enforcement personnel trained in traffic patrolling from the air. Another application is the use of UAVs in support of the first responders. MIRAMAP and E-Producties in Europe have designed a helicopter system that can be rapidly deployable and is able to collect real-time color and thermal infrared video images that are sent to a command and control center. It is also capable of collecting high-resolution geo-referenced digital imagery that is made available to first responders on existing GIS platforms in open GIS formats in a short time after a disaster [4]. Unmanned helicopters have been used in the United States for Photogrammetric mapping and monitoring of the conditions of unpaved roads, sponsored by the US DOT [5]. This study was aiming for timely identification and rectification of road deformation through loss of crown or damage to the road base. The automatically controlled helicopter can fly along predefined flight paths; and is equipped with a Global Positioning System (GPS)/Inertial Measurement Unit (IMU) and a geomagnetic sensor to detect the position, attitude and velocity of the platform. The University of South Florida (USF) has been actively involved in cooperative research projects with the Florida Department of Transportation to investigate and design an Airborne Traffic Surveillance System (ATSS). ATSS is based on an Aerosonde UAV platform, and features digital video encoding, and transmission of data and multimedia video streams over FDOT's microwave IP networks. This is designed to be an improvement over the current DOT practices such as fixed tower mounted cameras or embedded detectors, and provides a "bird's 6 eye view" to obtain data on traffic trends as well as to monitor and control traffic, monitor road conditions, and coordinate emergency response [6,7,8]. In addition to typical UAV deployment scenarios for remote traffic monitoring and offline analysis of traffic patterns [9-12], several recent efforts have also attempted to add reasoning capabilities to UAVs used for traffic surveillance. In one such application, with the central idea of traffic surveillance over a widely varying geographical terrain (covering networks in city, suburban and rural areas, both densely and sparsely populated), the UAV is equipped to "understand" the situation on the ground [13]. Such a capability aimed to interpret patterns such as conventional maneuvers of a vehicle, dangerous or otherwise exceptional maneuvers, and structure of the traffic such as congestion. The UAV is also capable of performing tasks that are assigned by the ground operator or automatically triggered by the observations it interprets. For example, to follow a vehicle that flees from the scene of an apparent crime, or to assist a vehicle through difficult traffic conditions or to re-route traffic and get to a particular destination as quickly as possible. In another such application, a system was implemented to achieve high-level situation awareness about traffic situations in an urban area [14]. This uses sequences of color and thermal images from the UAV as inputs to construct and maintain qualitative object structures and to recognize the traffic behavior in real time. Along the same lines, UAVs with capabilities to conduct autonomous search and track missions have been used in surveillance operations include inspecting and monitoring river boundaries, bridges and coastlines [15]. In this project, a fixed wing UAV equipped with on-board vision or infrared sensors is applied to search and map littoral boundaries based on visual feedback. 7 Although there have been several successful demonstrations of small RPVs and UAVs in monitoring and managing traffic flow, detecting pipeline leaks, and collecting imagery for environmental, safety, security, and emergency management applications there still exist a number of barriers to a wide scale UAV deployment. This could hamper any planned demonstrations in the future and prevent rapid technology transfer into transportation practice. Currently, there is an effort to identify barriers to near-term UAV deployment for diverse transportation safety and security applications and the eventual development of a simple set of guidelines, or Standard Operating Practices (SOP) for UAV deployment by states and local transportation agencies, or by other transportation system owners or operators [16]. 1.2. Regulations While the use of small UAVs in traffic monitoring has become increasingly popular, the widespread deployment of such systems in the civilian airspace is still restricted by the Federal Aviation Administration (FAA) regulations. FAA have so far issued two Interim Operational Approval Guidance (AFS-400 UAS Policy 05-01 [17], dated September 16, 2005, and 08-01 [18], dated March 13, 2008) regulating the Unmanned Aircraft Systems (UAS) operations in the U. S. National Airspace System (NAS). Particularly, Interim Operational Approval Guidance 08-01 states that: "In general, specific authorization to conduct unmanned aircraft operations in the NAS outside of active Restricted, Prohibited, or Warning Area airspace must be requested by the applicant. Airspace inside buildings or structures is not considered to be part of the NAS and is not regulated. The two methods of approval are either a certificate of waiver or authorization (COA) or the issuance of a special airworthiness certificate." 8 "The applicability and process to be used in a UAS operational approval is dependent on whether the applicant is a civil user or a public user. A public user is one that is intrinsically governmental in nature (i.e., federal, state, and local agencies). Public applicants should utilize the COA application process. Civil applicants must apply for an airworthiness certificate." In general, the FAA's primary concern is that Unmanned Aircraft (UA) operates safely among non-cooperative aircraft and other airborne operations not reliably identifiable by RADAR, i.e. balloons, gliders, etc. For the use of Remote Control (R/C) model aircraft, FAA does not provide specific regulations. Advisory Circular (AR 91-57) [19], dated June 9, 1981 refers to the subject of 'model aircraft operating standards', specifically outlining the compliance with safety standards for model aircraft operators. The operating standards describe include: a. "Select an operating site that is of sufficient distance from populated areas. The selected site should be away from noise sensitive areas such as parks, schools, hospitals, churches, etc;" b. "Do not operate model aircraft in the presence of spectators until the aircraft is successfully flight tested and proven airworthy;" c. "Do not fly model aircraft higher than 400 feet above the surface. When flying aircraft within 3 miles of an airport, notify the airport operator, or when an air traffic facility is located at the airport, notify the control tower, or flight service station;" 9 d. "Give right of way to, and avoid flying in the proximity of, full-scale aircraft. Use observers to help if possible;" e. "Do not hesitate to ask for assistance from any airport traffic control tower or flight service station concerning compliance with these standards." The operation of R/C model aircraft should also follow the rules of the AMA (Academy of Model Aeronautics), which requires keeping the weight of the UAV under 55 lbs. and flying within visual range of the ground pilot under a 400 ft altitude ceiling [20]. According to FAA Notice of Policy FAA-2006-25714 [21]: "The FAA has undertaken a safety review that will examine the feasibility of creating a different category of unmanned "vehicles" that may be defined by the operator's visual line of sight and are also small and slow enough to adequately mitigate hazards to other aircraft and persons on the ground. The end product of this analysis may be a new flight authorization instrument similar to AC 91-57, but focused on operations which do not qualify as sport and recreation, but also may not require a certificate of airworthiness. They will, however, require compliance with applicable FAA regulations and guidance developed for this category." 1.3. Objective of the Project The main objective of this project is to evaluate the possibility of implementing flexible, intelligent, and low-cost remotely controlled data acquisition solutions complementing existing DOT measurement systems by supplying high-quality aerial imagery for various aspects of the highway research and operations. 10 1.4. Organization of the Report This report is divided into five chapters. Chapter 1 describes the background of the research, the FAA and AMA regulations, and the objectives of the project. Chapter 2 discusses the development of the aerial platform, specifications for key components, and results of the flight-testing operations. Chapter 3 discusses the development of the geo-referencing software, followed by a detailed analysis of various Sources of Errors (SOE) and the associated corrective procedures. Chapter 4 outlines a sensor fusion algorithm able of estimating the aircraft attitude angle as well as improving positioning accuracy based on information from both GPS and a low- cost INS (Inertial Navigation System). Chapter 5 provides the conclusions and outlines the follow-up research activities. 2. Aerial Platform Development and Flight Testing Within this effort, one of the West Virginia University (WVU) remotely piloted aircraft, named 'Foamy', has been customized for data acquisition purpose. The 'Foamy' aircraft was selected because of its low cost and flexibility in accommodating different sensor payloads. A brief description of the 'Foamy' platform is provided in Table 1. Length 70.1" (1.8 m) Wingspan 67" (1.7 m) Wing Cord Root 19.7" (0.5 m), Tip 7.87" (0.2 m) Weight (Aircraft only) 9.92 lb (4.5 Kg) Maximum Takeoff Weight 13.50 lb (6.1 Kg) Engine GMS 0.76, 2-stroke glow engine, 2.5 hp Typical Flight Duration 10 Minutes Table 1. General Specifications of the WVU 'Foamy' Aircraft 11 The overall layout of the instrumented 'Foamy' aircraft is shown in Figures 1-2. The current avionic components include a Remote Control (R/C) system, a GPS receiver, a flight data recorder, downlink telemetry hardware, a digital still camera, and a shutter-triggering device. During the flight the ground pilot use one of the R/C channels to remotely trigger the camera. Engine Digital Camera GPS Receiver Radio Control Telemetry Fig. 1. Foamy Aircraft Configuration Digital Still Camera Time Synchronization Board Telemetry Hardware R/C Receiver Flight Data Recorder GPS Antenna (Back Side) Fig. 2. A Close Up of the Aircraft Internal Configuration 12 Two digital cameras are used for this project. The first camera is 'off-the-shelf' Canon Digital Rebel XTi mainly used for visible spectrum photography. Another camera is a Canon Digital Rebel T1i. This camera was professionally modified to have the internal Infrared-block filter removed and replaced with an external Infrared-pass filter. This camera is mainly used for near-Infrared photography. Both cameras have similar footprints so they are interchangeable inside the aircraft and they share the same shutter triggering mechanism. Two camera lenses were tested during initial flights: a 50mm F1.8 fixed focal length lens and an 18-55mm F3.5-5.6 zoom lens. The 18-55mm lens when locked at 18mm was eventually proven to be more suitable for this particular application and was used in most subsequent flight tests. Key specifications for both cameras that are directly related to aerial photography are shown in Table 2. Canon Digital Rebel XTi Canon Digital Rebel T1i Max resolution 3888 x 2592 4752 x 3168 Effective pixels 10.1 million 15.1 million Sensor size 22.2 x 14.8 mm (3.28 cm²) 22.3 x 14.9 mm (3.32 cm²) ISO rating 100-1600 Auto, 100-3200 Max shutter 1/4000 sec 1/4000 sec Focal length multiplier 1.6 1.6 Continuous Drive 3.0 fps, 27 JPEG 3.4 fps, 170 JPEG Movie Clips N/A 1920 x 1080 @ 20 fps, 1280 x 720 @ 30 fps Remote control E3 connector, InfraRed E3 connector, InfraRed Battery 720mAh Li-Ion 1050mAh Li-Ion Weight (inc. batteries) 19.6 oz (556 g) 18.3 oz (520 g) Dimensions 5" x 3.7" x 2.6" 5.1" x 3.9" x 2.4" (127 x 94 x 65 mm) (129 x 98 x 62 mm) Table 2. Camera Specifications The flight data recorder is based on an Eagletree® Seagull Wireless Dashboard Telemetry and Data Recorder System. It provides basic functions of logging GPS data, record pilot command, and sending data to the ground station. General Specifications for the Eagletree system are listed in Table 3. 13 Data Recorder Operational Voltage 4.35V to 7.0V Weight 1 oz (28 g) Dimensions 1.97" x 1.38" x 0.67" (50 x 35 x 17 mm) Record Time Approx. 20 minutes FCC 900 MHz, 200mW Transmitter Power: Power Taken from Recorder/Receiver Battery Current Draw Transmitter + Recorder, average < 70 milliamp Frequency Range 902 – 928 MHz Operating Range (Line of Sight) Up to 1.2 miles w/included antenna Weight Approx 0.5 oz (14g, Transmitter only) Dimensions 2.75" x 1.25" x 0.25" (70 x 32 x 6 mm) GPS Expander Update Rate 5Hz WAAS and EGNOS Support Yes Time to Fix Less Than 1 Second Hot, 36 Second Cold (Typical) Speed Accuracy Approx 0.1 m/s Current draw Less Than 40 mA Steady State Weighs Approx 0.4 oz (11g) Dimensions 1.4" x 0.6" x 0.3" (35x16x8mm) Position accuracy Approx 8.2 ft (2.5m) CEP Table 3. Eagle Tree Specifications Two flight sessions with a total of six flights have been conducted at the WVU Jackson's Mill flight-testing facility. An additional flight session with eight flights was performed at Friendship Hill, Point Marion, PA. A GPS trajectory overlaying a 3D Google Earth map is presented in Figure 3, shown the aircraft circling over Friendship Hill. 14 Fig. 3. Flight Trajectory Over the Friendship Hill Several hundred high-resolution geo-tagged aerial photographs were taken during these flight experiments. A single frame of an aerial photograph collected at Jackson's Mill is presented in Figure 4; the image provides a high-resolution coverage of a construction site. Fig. 4. A Single Aerial Photo 15 A mosaic of 16 aerial images collected from a different flight test is also shown in Figure 5. Fig. 5. A Mosaic of 16 Aerial Images 3. Data Processing and Error Analysis 3.1 Geo-Referencing Software To utilize the collected aerial photos for DOT applications, a geo-referencing software was developed within this effort. By definition, geo-referencing a photo implies the derivation of a correct mapping between the center of the photo and its geographical coordinates, commonly in terms of Latitude and Longitude. Following a successful geo-reference for one point, it is then theoretically possible to geo-reference any other point in the image through simple geometric 16 calculations. Although conceptually straightforward, the measurement of a distance on an aerial image, along with the consequent positioning of objects in the picture, presents several technical challenges and it is sensitive to several Sources of Errors (SOE). A Matlab® script was developed for computing distances and latitude/longitude for points selected by the user on a picture, from a central point of known coordinates. The script requires some parameters to be set up by the user, which are: hs, vs: height and width of sensor or film of the camera used. These parameters are needed for computation of Field of View (FOV) and Angle of View (AOV) from a certain height above the ground; f: focal length of the lens used when taking the picture; H: height above the ground when the picture was taken. The determination of ratios between the physical dimension and the pixel count depend on the proper setting of above parameters. Additionally, the transformation of longitude and latitude measurements in degrees to distance in meters is based on equations supplied by the National Geospatial – Intelligence Website [22]. If φ is the current latitude, then the length of a degree of latitude φ 1° can be calculated by: ( ab) 2 M (φ ) = (1) ((a cos(φ )) 2 + (b sin(φ )) 2 )3/ 2 π φ 1° = M (φ ) (2) 180 17 where φ 1° is in meters; a and b are, respectively, the equatorial and polar radii of Earth, they can be approximated to be 6378137m and 6356752m based on the datum used for earth shape modeling. These radii are the ones used on the WGS84 datum ellipsoid, which is the most common for GPS applications. The length of a degree of longitude τ 1° is obtained by the formula: π a 4 cos(φ ) 2 + b 4 sin(φ ) 2 τ 1° = cos(φ ) (3) 180 ° ( a cos(φ )) 2 + (b sin(φ )) 2 Then longitude and latitude of the central position are loaded from the GPS measurement, in the following format: lon/lat = [deg min.decimal]. The heading of the aircraft, hdg in radians, is also estimated from GPS readings. The FOV from a certain distance are estimated to be: hs hFOV = H (4) f vs vFOV = H (5) f with hFOV being the horizontal FOV in meters, and vFOV is the vertical FOV. The calculation of the AOV is performed using: ⎛ h ⎞ hAOV = 2 arctan ⎜ s ⎟ (6) ⎝2f ⎠ ⎛ v ⎞ v AOV = 2 arctan ⎜ s ⎟ (7) ⎝2f ⎠ 18 After loading the photo into Matlab, a conversion factor between pixels and "real" length can be computed as: hFOV p 2m = (8) hIMAGE with hIMAGE being the horizontal number of pixels of the image. Since square pixels were used in our cameras, vertical and horizontal factors are the same, even if this is not true for the length as acquired from the lens. This means that a source of error is the fact that the lens at low focal distances (f) tends to transform squares into rectangles; therefore, the 90° angles are not exactly preserved. The distances from the central position are calculated as: hIMAGE hDIST = xP − (9) 2 v IMAGE vDIST = yP − (10) 2 where xP and yP are the pixel coordinates of the points, as selected by the user with a simple point-and-click GUI shown in Figure 6. 19 500 1000 1500 2000 2500 500 1000 1500 2000 2500 3000 3500 Fig. 6. GUI for Point-and-Click Acquisition of Points, Black Arrow is the North-Direction The number of points to be clicked and acquired can be selected by the user. The length of the distance vector from the center is computed as: tDIST = hDIST + v 2 2 DIST (11) All the pixel distances can then be converted to real distances, with factors described earlier. The next step is to compute the rotation between the heading and the vector to point, so that we can pass from the local reference system to a global reference system (North-East). If the direction angle of the distance vector is: ⎛ hDIST ⎞ ξ = arctan ⎜ ⎟ (12) ⎝ − vDIST ⎠ Then the angle between true North and this vector, which will be the angle needed for projecting the distances from local to global system, is: 20 ω = hdg − ξ (13) Consequently, the distances in the global reference are: N DIST = tDIST ⋅ cos (ω ) (14) E DIST = −tDIST ⋅ sin (ω ) (15) Finally, the correction in terms of degree for longitude and latitude can be computed with the length inserted above, as: EDIST Δ LON = (16) τ 1° N DIST Δ LAT = (17) φ 1° Therefore, the longitude and latitude of the points will be: LON P = lon + Δ LON (18) LATP = lat + Δ LAT (19) The software can be used either to offset the coordinate of a point from the center or as a tool for measuring the distances; the concepts are the same, and only a conversion from metric distances to degree of angle is involved. As an example of the use of this software, we tried to estimate the wingspan of a Cessna 172. The picture shown in Figure 7 was taken from a height of approximately 51 m (171 ft) with a Canon Rebel XTi, mounting a standard 18-55 mm zoom lens locked at 18mm. 21 Fig. 7. Aerial Photo of Two Cessna Aircraft With two clicks on each wingtip the program performs the calculations and the result is b=12.187m. Known that the actual wingspan of a Cessna 172 is 11m the associated estimation error is approximately 10%, compatible with the error expected on the measurement of the height of the aircraft due to the time synchronization. The output of the script is in Figure 8, 22 Horizontal field of view with 18 mm lens from 52.1208 meters: 64.2823 Vertical field of view with 18 mm lens from 52.1208 meters: 42.8549 dist_21 = 12.1870 lon_point = -80.0000 28.2006 -80.0000 28.1990 lat_point = 39.0000 5.6578 39.0000 5.6643 Fig. 8. Output of the Script where lat_point and lon_point are the coordinates of the selected points. The initial evaluation of the script shows a typical position estimation error of 40-50 meters when compared with the ground 'true' measurement using a handheld GPS unit. If a landmark with known position is shown in the same image, the position estimation error can be substantially reduced. Efforts have then focused on the identification of potential sources of errors and design corrective measures accordingly. 3.2. Source of Error The possible Sources of Error (SOE) in the measurement of distances from aerial photos can be roughly divided into two classes: "Before Acquisition -BA" and "After Acquisition - AA". The BA class includes all those errors that do not depend on the picture itself, such as GPS error, positioning error, and other hardware errors. Errors from the AA class are related to the 23 fact that distances are measured from a picture; therefore, AA errors include lens distortions, imprecision, and general problems in relating a pixel distance to a real distance. In the following sections each identified SOE is discussed, along with an estimate of its magnitude, whenever possible. Matching Image with GPS positioning This is the error associated with the matching of an image with corresponding GPS measurement. Since the on-board GPS provides the initial position of the central point of the picture, it is very important to achieve an accurate synchronization of the camera shutter with a recorded GPS position. If this is not done properly, the final position estimation error can be quite large. Assuming a synchronization error of 1 sec. between the time of the acquisition and the recorded position, we introduce an error that depends on the ground speed of the aircraft. Typical airspeed for the 'foamy' aircraft is around 20m/s, leading to a position estimation error of approximately 20m at the center of the image. To reduce this error, a time-synchronization board has been designed and developed for an accurate control of the time matching process. A detailed discussion of the time-synchronization board is provided in Section 3.3. Attitude of the Aircraft When the image is taken, the aircraft is likely not flying in a straight and level condition. This is a known problem for geo-referencing from an aerial photograph. As an estimate, if the aircraft flies at 52m above the ground, an angle δ [deg] will cause an error e = H tan (δ ) on the position estimation of the center of the picture. The relationship between angle and error is shown in Figure 9. 24 Attitude angle vs error of the central position 10 9 8 7 6 e [m] 5 4 3 2 1 0 0 1 2 3 4 5 6 7 8 9 10 δ [deg] Fig. 9. Angle Errors vs. Position Error Figure 9 shows that, for small angles, the relationship is close to be linear. From a height of 52m, an error of 5° leads to an error of approximately 5 meters. If an error of 5° is assumed on both pitch and roll, then a total error would be around 7 m on the diagonal positioning. A possible solution for reducing these errors would be to mount the camera on an inertial (gimbaled) platform with the goal of maintaining the orientation of the camera for a range of aircraft attitude angles. This approach would effectively reduce the aforementioned errors, but with a penalty on weight, cost, and complexity. A more elegant and low-cost solution is to introduce a set of inertial sensors and sensor fusion techniques providing attitude angle estimates for post flight image transformation. Details about the sensor fusion algorithm development are provided in Section 4. 25 GPS Error Any error during the GPS measurement will directly transfer itself to the final position estimation error. The GPS manufacture's specifications list a GPS measurement accuracy of 2.5m CEP (Circular Error Probability). However, this is believed to be a highly optimistic estimation, especially during dynamic flight conditions. Summing the aforementioned SOEs, a total error of approximately 30m on the final estimated position can be categorized as BA-Error. Lens Distortion This is a very common error and it originates from the distortion of most commercial lenses, especially wide-angle lenses. The type and severity of the distortion is strictly related to the specific lens used. There are two approaches for reducing this error: the first approach is based on the use of a longer focal length lens (e.g. 24mm instead of the 18mm setting used); however, this will have the drawback of restricting the field of view. A second approach is based on the use of specialized post-editing software. Since generating "orthophotos" from aerial images is a very wide field of study, there are many specialized codes for mapping "ortophotos". In particular, several commercial software packages feature a database of the correction parameters for many common commercial lenses. The use of the commercially available software "PTLens" allows us to correct lens distortions in this study. The difference between the position of an object before and after the correction is presented in Figure 10, which superimposed the results with false color. 26 Fig. 10. Superimposition of Object Before and After Distortion Correction Altitude Error The altitude error is not a direct error by itself, as in theory the position of the center is not affected by the altitude of the aircraft. However, it becomes a source of error when the position of an object not on the centerline is measured. The relation between apparent dimensions and real dimension is strongly dependent on the distance. Again, there is a linear relationship between the error in altitude and the final estimation error. 3.3. Time-Synchronization Board To reduce the error introduced by time gaps between the GPS measurements and the measured acquisition time of the images, a Time-Synchronization Board (TSB) has been developed for active control of the image acquisition process. The main idea is to develop a circuit board with an embedded microchip controlling the shutter of a camera, while sending PWM (Pulse Width Modulation) signals to the flight data-recorder. Since GPS Data-Recording hardware can record PWM signals, as they are used to control and register the position of electric actuators, a system like this allows the recording of a progressive number coming from the TSB 27 as an index of the picture taken at a certain time. This number is then associated with the GPS measurement to provide an accurate geo-tagging of each acquired image. The TSB board is built around a NetBurner MOD-5213 embedded microprocessor, which is an integrated microchip used for control and communication applications. This device shows a great flexibility in generating PWM signals of the desired duty cycle to encode the image index. Associated with a variation in the PWM output of the MOD5213, the board can also send a voltage command to the camera, either turning on the autofocus or activating the shutter. The interval between pictures can be set up in the embedded software, written in C language. Anytime a picture is taken, the associated PWM reading channel on the flight data recorder will read an increased value from the PWM signal. Since all of these channels are logged, it is then possible to match pictures and GPS locations with desirable accuracy. The design for the circuitry of the TSB is presented in Figure 11. Fig. 11. Time-Synchronization Board (Circuitry) The physical realization of this board is presented in Figure 12, where it is connected to a Canon Digital Rebel T1i camera. 28 Fig. 12. Board Connected to the Camera 4. GPS/INS Sensor Fusion The SOE analysis from the previous chapter shows that a significant amount of position estimation errors was introduced by either mismatching different measurements, or by the (low) quality of the measurements. The first issue is partially addressed through the design of a Time- Synchronization Board; the second issue can be solved using new sensors and novel methods for integrating their measurements. The integration of information from multiple-sources is a key approach toward improving the measurement reliability and to reduce the overall size, weight, and power consumption of an airborne system. Within this research, two sets of information are required to further improve the position estimation performance: 1) aircraft attitude angles, and 2) accurate aircraft position information. The GPS receiver on-board the aircraft can only provide a coarse (in term of both spatial and 29 temporal resolution) measurement of the aircraft position. An alternative method used by most large aircraft and spacecraft is through the use of Inertial Navigation System (INS), which provides both attitude angle and position estimation. However, a navigation-grade INS is well beyond the price and weight range of our test-bed aircraft. Low cost Inertial Measurement Units (IMU) based on Micro-Electro-Mechanical Systems (MEMS) technology are commercially available at a reasonable price. However, low cost IMU has limited performance (drifting characteristics) and cannot be used for direct navigation purposes. By properly integrating GPS and inertial sensor measurements, the unbiased nature of the GPS signals can limit the size of the low frequency errors in the inertial system. Similarly, the continuity of the INS can be used to fill in position information gaps between GPS updates and reduce the effect of high frequency GPS errors [23,24]. Therefore, the GPS/INS sensor fusion allows for substantial improvement of the performance and reliability of the position measurement system. Additionally, the velocity measurement from the GPS receiver can be used to estimate the aircraft acceleration vector. This vector is different from the acceleration vector measured by the accelerometers inside the IMU, which also include the acceleration due to Earth's gravity. By comparing the two sources of acceleration measurements, it is possible to compute the earth's gravitational vector with respect to the aircraft's body frame, leading to an estimation of aircraft attitude angles. In this study, an Extended Kalman Filter based sensor fusion algorithm was developed to provide attitude angle estimation as well as more refined position estimation with a higher update rate. As described earlier, there are two types of sensors that could provide the acceleration information. One is the IMU, which directly measures the linear acceleration in the aircraft body 30 frame. Another source of information is provided by the GPS receiver, which measures 3-axis position and velocity in the Earth-Centered-Earth-Fixed (ECEF) frame. The accelerations along the ECEF frame can then be calculated by differentiating the GPS velocity solutions. The relationship between the two types of acceleration measurements is dependent on the aircraft Euler angles: ⎡ Vx ⎤ ⎡cosψ − sinψ 0⎤ ⎡ cos θ & 0 sin θ ⎤ ⎡1 0 0 ⎤ ⎡ ax ⎤ ⎢ & ⎥ ⎢ ⎥⎢ ⎥ ⎢0 cos φ ⎢ V y ⎥ = ⎢ sinψ cosψ 0⎥ ⎢ 0 1 0 − sin φ ⎥ ⎢ a y ⎥ ⎥⎢ ⎥⎢ ⎥ ⎢Vz + g ⎥ ⎢ 0 ⎣ & ⎦ ⎣ 0 1⎥ ⎢ − sin θ ⎦⎣ 0 cosθ ⎥ ⎢0 sin φ ⎦⎣ cos φ ⎥ ⎢ a z ⎥ ⎦⎣ ⎦ ⎡cosψ cos θ − sinψ cosψ sin θ ⎤ ⎡1 0 0 ⎤ ⎡ ax ⎤ = ⎢ sinψ cos θ cosψ sinψ sin θ ⎥ ⎢0 cos φ − sin φ ⎥ ⎢ a y ⎥ (20) ⎢ ⎥⎢ ⎥⎢ ⎥ ⎢ − sin θ ⎣ 0 cosθ ⎥ ⎢0 ⎦⎣ sin φ cos φ ⎥ ⎢ a z ⎥ ⎦⎣ ⎦ ⎡cosψ cos θ − sinψ cos φ + cosψ sin θ sin φ sinψ sin φ + cosψ sin θ cos φ ⎤ ⎡ a x ⎤ = ⎢ sinψ cos θ cosψ cos φ + sinψ sin θ sin φ − cosψ sin φ + sinψ sin θ cos φ ⎥ ⎢ a y ⎥ ⎢ ⎥⎢ ⎥ ⎢ − sin θ ⎣ cosθ sin φ cosθ cos φ ⎥ ⎢ az ⎥ ⎦⎣ ⎦ where a is the acceleration in the body frame measured by IMU, which is a combination of linear accelerations and the gravitational acceleration g ; V is the velocity in the ECEM frame; θ , φ , and ψ are the aircraft pitch, roll, and heading angles, respectively. The aircraft kinematics nonlinear differential equations are then formulated into a 9-state continuous state-space model: ⎡x⎤ ⎢ y⎥ ⎢ ⎥ ⎡ax ⎤ ⎡x⎤ ⎢z⎥ ⎢a ⎥ ⎢y⎥ ⎢ ⎥ ⎢ y⎥ ⎢ ⎥ ⎢Vx ⎥ ⎢a ⎥ ⎢z⎥ x = ⎢V y ⎥ , u = ⎢ z⎥, z=⎢ ⎥ (21) ⎢ ⎥ ⎢ p⎥ ⎢Vx ⎥ ⎢Vz ⎥ ⎢q⎥ ⎢V y ⎥ ⎢φ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢r⎥ ⎣ ⎦ ⎢Vz ⎥ ⎣ ⎦ ⎢θ ⎥ ⎢ψ ⎥ ⎣ ⎦ 31 ⎡x⎤ ⎡ & Vx ⎤ ⎢ y⎥ ⎢ & Vy ⎥ ⎢ ⎥ ⎢ ⎥ ⎢z⎥ ⎢ & Vz ⎥ ⎢&⎥ ⎢ ⎥ ⎢Vx ⎥ ⎢cosψ cos θ a x + ( − sinψ cos φ + cosψ sin θ sin φ )a y + (sinψ sin φ + cosψ sin θ cos φ )a z ⎥ x = ⎢V y ⎥ = ⎢ sinψ cos θ a x + (cosψ cos φ + sinψ sin θ sin φ )a y + ( − cosψ sin φ + sinψ sin θ cos φ )a z ⎥ + w & & ⎢&⎥ ⎢ ⎥ ⎢Vz ⎥ ⎢ − sin θ a x + cosθ sin φ a y + cosθ cos φ a z − g ⎥ ⎢φ ⎥ ⎢ & p + q sin φ tan θ + r cos φ tan θ ⎥ ⎢ &⎥ ⎢ ⎥ ⎢θ ⎥ ⎢ q cos φ − r sin φ ⎥ ⎣ &⎦ ⎢ ⎢ψ ⎥ ⎣ ( q sin φ + r cos φ )sec θ ⎥ ⎦ (22) ⎡1 0 0 0 0 0 0 0 0⎤ ⎢0 1 0 0 0 0 0 0 0⎥ ⎢ ⎥ ⎢0 0 1 0 0 0 0 0 0⎥ z = Hx + v = ⎢ ⎥x+v (23) ⎢0 0 0 1 0 0 0 0 0⎥ ⎢0 0 0 0 1 0 0 0 0⎥ ⎢ ⎥ ⎢0 ⎣ 0 0 0 0 1 0 0 0⎥ ⎦ where x is the state vector to be estimated; u is the input vector, and z is the measurement vector, which is provide by the raw GPS measurement. This model is then discretized to be a set of nonlinear stochastic difference equations: xk = f ( xk −1 , uk −1 , wk −1 ) z k = h ( xk , v k ) ⎡ xk ⎤ ⎡ xk −1 + TsVx k −1 ⎤ ⎢y ⎥ ⎢ yk −1 + TsV y k −1 ⎥ ⎢ k⎥ ⎢ ⎥ ⎢ zk ⎥ ⎢ zk −1 + TsVz k −1 ⎥ ⎢ ⎥ ⎢ ⎥ ⎢Vxk ⎥ ⎢Vxk −1 + Ts(cosψ k −1 cosθ k −1a xk −1 + ( − sinψ k −1 cos φk −1 + cosψ k −1 sin θ k −1 sin φk −1 )a yk −1 + (sinψ k −1 sin φk −1 + cosψ k −1 sin θ k −1 cosφk −1 )a zk −1 ) ⎥ ⎢V y ⎥ = ⎢ Vy + Ts(sinψ k −1 cosθ a x + (cosψ k −1 cos φk −1 + sinψ k −1 sin θ k −1 sin φk −1 )a y + ( − cosψ k −1 sin φk −1 + sinψ k −1 sin θ k −1 cosφk −1 )a z ) ⎥ + wk −1 ⎢ k ⎥ ⎢ k −1 k −1 k −1 k −1 ⎥ ⎢Vzk ⎥ ⎢ Vzk −1 + Ts ( − sin θ k −1a x k −1 + cosθ k −1 sin φk −1a yk −1 + cosθ k −1 cos φk −1a z k −1 − g ) ⎥ ⎢φ ⎥ ⎢ φk −1 + Ts( pk −1 + qk −1 sin φk −1 tan θ k −1 + rk −1 cos φk −1 tan θ k −1 ) ⎥ ⎢ k⎥ ⎢ ⎥ ⎢ θk ⎥ ⎢ θ k −1 + Ts( qk −1 cos φk −1 − rk −1 sin φk −1 ) ⎥ ⎢ψ ⎥ ⎢ ⎥ ⎢ ⎥ ⎣ ⎣ k⎦ ⎢ ψ k −1 + Ts( qk −1 sin φk −1 + rk −1 cosφk −1 )secθ k −1 ⎥ ⎦ (24) zk = Hxk + vk (25) 32 where w and v are process and measurement noises, respectively. Both the process noise covariance matrix Q and measurement noise covariance matrix R are estimated based on the level of noise on previously acquired flight data using the same sensors: ⎡0 ⎤ ⎢ 0 ⎥ ⎢ ⎥ ⎢ 0 ⎥ ⎢ ⎥ ⎢ a xnoise ⎥ Q=⎢ a ynoise ⎥ ⎢ ⎥ ⎢ a znoise ⎥ ⎢ pnoise ⎥ ⎢ ⎥ ⎢ qnoise ⎥ ⎢ rnoise ⎥ ⎣ ⎦ ⎡0 ⎤ ⎢ 0 ⎥ ⎢ ⎥ ⎢ 0 ⎥ ⎢ ⎥ ⎢ 0.0014 ⎥ =⎢ 0.0046 ⎥ ⎢ ⎥ ⎢ 0.0091 ⎥ ⎢ 1.8665e-004 ⎥ ⎢ ⎥ (26) ⎢ 6.0397e-004 ⎥ ⎢ ⎣ 1.5484e-004 ⎥ ⎦ ⎡ xnoise ⎤ ⎢ ynoise ⎥ ⎢ ⎥ ⎢ znoise ⎥ R=⎢ ⎥ ⎢ Vxnoise ⎥ ⎢ V ynoise ⎥ ⎢ ⎥ ⎢ ⎣ Vznoise ⎥ ⎦ (27) ⎡ 0.0738 ⎤ ⎢ 0.0036 ⎥ ⎢ ⎥ ⎢ 0.0638 ⎥ =⎢ ⎥ ⎢ 0.0017 ⎥ ⎢ 0.0011 ⎥ ⎢ ⎥ ⎢ ⎣ 0.0078⎥ ⎦ 33 For the EKF application, the nonlinear stochastic difference equations (5) and (6) are linearized at each time step. The Jacobian matrix of partial derivatives of f with respect to x is given by: ⎡1 0 0 Ts 0 0 0 0 0 ⎤ ⎢0 1 0 0 Ts 0 0 0 0 ⎥ ⎢ ⎥ ⎢0 0 1 0 0 Ts 0 0 0 ⎥ ⎢ ⎥ ⎢ ˆ Ts ((sin ψ sin φ + cosψ sin θˆ cos φ )a y ˆ ˆ ˆ Ts (− cosψ sin θˆax + cosψ cosθˆ sin φ a y ˆ ˆ Ts (− sinψ cosθˆax − (cosψ cos φ + sinψ sin θˆ sin φ )a y ⎥ ˆ ˆ ˆ ˆ ˆ ⎢0 0 0 1 0 0 ⎥ ⎢ ˆ +(sinψ cos φ − cosψ sin θˆ sin φ ) az ) ˆ ˆ ˆ + cosψ cosθˆ cos φ az ) ˆ ˆ + (cosψ sin φ − sinψ sin θˆ cos φ )az ) ˆ ˆ ˆ ˆ ⎥ ⎢ ˆ ⎥ ⎢ ˆ Ts ((− cosψ sin φ + sinψ sin θˆ cos φ )a y ˆ ˆ ˆ Ts (− sin ψ sin θˆax + sin ψ cos θˆ sin φ a y ˆ ˆ ˆ Ts(cosψ cosθˆax + (− sinψ cos φ + cosψ sin θˆ sin φ ) a y ⎥ ˆ ˆ ˆ ˆ Ak = ⎢0 0 0 0 1 0 ⎥ ⎢ ˆ −(cosψ cos φ + sin ψ sin θˆ sin φ )az ) ˆ ˆ ˆ + sinψ cos θˆ cos φ az ) ˆ ˆ +(sin ψ sin φ + cosψ sin θˆ cos φ ) az ) ˆ ˆ ˆ ˆ ⎥ ⎢ ⎥ ⎢0 Ts (cosθˆ cos φ a y ˆ −Ts (cosθˆax + sin θˆ sin φ a y ˆ ⎥ ⎢ 0 0 0 0 1 0 ⎥ ⎢ ˆ − cos θˆ sin φ az ) ˆ + sin θˆ cos φ az ) ⎥ ⎢0 0 0 0 0 0 1 + Ts tan θˆk −1 (qk −1 cos φk −1 − rk −1 sin φk −1 ) ˆ ˆ 2 ˆ ˆ ˆ Ts sec θ k −1 (qk −1 sin φk −1 + rk −1 cos φk −1 ) 0 ⎥ ⎢ ⎥ ⎢0 0 0 0 0 0 ˆ ˆ −Ts ( qk −1 sin φk −1 + rk −1 cos φk −1 ) 1 0 ⎥ ⎢ ⎥ ⎢0 0 0 0 0 0 ˆ − r sin φ ) Ts (qk −1 cos φk −1 k −1 ˆ ˆ tan θˆ (q sin φ + r cos φ ) Ts secθ k −1 ˆ ˆ 1 ⎥ ⎣ k −1 k −1 k −1 k −1 k −1 k −1 ⎦ (28) Jacobian matrix of partial derivatives of f with respect to w is given by: Wk = I 9×9 (29) Jacobian matrix of partial derivatives of h with respect to x is given by: Hk = H (30) Jacobian matrix of partial derivatives of h with respect to v is given by: Vk = H (31) Once the state-space model is formulated, the EKF follows the conventional time update equations: xk− = f ( xk −1 , uk −1 ,0) ˆ ˆ (32) Pk− = Ak Pk −1 AkT + Qk −1 (33) and measurement update equations: K k = Pk− H kT ( H k Pk− H kT + Vk RkVkT ) −1 (34) xk = xk− + K k ( zk − h( xk− ,0)) ˆ ˆ ˆ (35) 34 Pk = ( I − K k H k ) Pk− (36) Figures 13-15 show the results from the EKF estimation, which is based on actual flight data acquired with one of the WVU YF-22 research aircraft. A high quality Goodrich Sensor Systems VG34® vertical gyro is also carried on-board to provide the reference attitude angle data for comparison purposes. Figures 13 and 14 demonstrate that the GPS/INS sensor fusion algorithm was able to provide a good estimation of aircraft attitude angle without the need for using expensive sensors. Roll Angle Roll Angle 80 Estimation Estimation Measurement 40 Measurement 60 40 20 Roll Angle(deg) Roll Angle(deg) 20 0 0 -20 -20 -40 -40 -60 -60 -80 0 100 200 300 400 500 600 700 800 900 400 420 440 460 480 500 Time(s) Time(s) Fig. 13. EKF Roll Angle Estimation Pitch Pitch 80 Estimation 25 Estimation Measurement Measurement 60 20 40 15 20 10 0 5 -20 0 -40 -5 -60 -10 -80 0 100 200 300 400 500 600 700 800 900 410 420 430 440 450 460 470 480 490 Time(s) Time(s) Fig. 14. EKF Pitch Angle Estimation 35 Figure 15 presents the GPS/INS position estimation when compared with the raw GPS measurements. Through the help of INS integration, the GPS/INS provides a very smooth position estimation that eliminated the large jumps typically seen in the raw GPS measurements. GPS Z-Position GPS Z-Position 200 Estimation 174 Estimation 180 Measurement Measurement 172 160 170 140 168 120 166 Z-axis(m) Z-axis(m) 100 164 80 162 60 160 40 158 20 156 0 154 -20 0 100 200 300 400 500 600 700 800 900 215 220 225 230 235 Time(s) Time(s) Fig. 15. EKF Position Estimation (Z-axis) 5. Conclusions The successful completion of the project demonstrated that a low cost aerial platform could serve as a flexible tool for acquiring high-resolution geo-tagged images for ground areas of interest. The extraction of reliable information from these images could benefit DOT engineers in a variety of research areas including, but not limited to work zone management, traffic congestion, safety, and environmental. The development of the project also provided excellent opportunities for students to perform hands on research, and get educated in areas such as flight- testing, electronics, and software development. 36 Throughout this effort, valuable experience has been acquired on how to instrument and calibrate an aerial platform for imaging purpose, and how to plan and execute a flight-test session at remote locations. The post-flight image analysis has showed that it is possible to geo- reference ground assets based on a minimum Camera+GPS hardware configuration, although an improved position estimation performance can be achieved through a more delicate time- synchronization process and improved sensor measurements. A Time-Synchronization Board (TSB) and a GPS/INS sensor fusion algorithm have been developed for enabling these capabilities. The validation of these new capabilities will be performed in the near future. 37 References [1] Harman, L.J., Shama, U., Dand, K., Kidwell, B., "Remote Sensing And Spatial Information For Transportation Demand Management (Tdm) Assessment", Proceedings of the Integrating Remote Sensing at the Global, Regional and Local Scale. Pecora 15/Land Satellite Information IV Conference, Denver, Colorado, 2002. [2] "Use of Unmanned Air Vehicle (UAV) for Pipeline Surveillance to Improve Safety and Lower Cost", Project Report. Electricore, Inc. PHMSA Research and Development Contract Number: DTPH56-05-T-0004. [3] HaLevi, E., "UAV's Deployed in Israel's Roads to Catch Violators", News Article, 09/15/05. Available: http://www.israelnationalnews.com/News/News.aspx/89924 [4] Haarbrink, R.B., Koers, E., "Helicopter UAV for photogrammetry and rapid response". Second International Workshop: The Future of Remote Sensing, Antwerp, October 2006. [5] Zhang, C., "An UAV-based Photogrammetric Mapping System for Road Condition Assessment", Proceedings of Commission V, ISPRS Congress Beijing 2008. [6] Farradyne, P.B. "Use of Unmanned Aerial Vehicles in Traffic Surveillance and Traffic Management - Technical Memorandum", May, 2005. 38 [7] Srinivasan, S., Latchman, H., Shea, J., Wong, T., and McNair, J. 2004. "Airborne Traffic Surveillance Systems: Video Surveillance of Highway Traffic," In Proceedings of the ACM 2nd international Workshop on Video Surveillance &Amp; Sensor Networks (New York, NY, USA, October 15 - 15, 2004). VSSN '04. ACM, New York, NY, 131-135. DOI= http://doi.acm.org/10.1145/1026799.1026821 [8] Puri,A., "A Survey of Unmanned Aerial Vehicles for Traffic Surveillance", A Technical Report, University of South Florida, 2004. [9] Gebre-Egziabher, D., "RPV/UAV Surveillance for Transportation Management and Security", Research Report No. CTS 08-27, December 2008. [10] Coifman, B., McCord, M.R., Mishalani, R., Morris, S., Redmill, K., Ozguner, U., "Traffic Surveillance from an Uninhabited Aerial Vehicle", US DOT Workshop on Integration of PNT, RS, and Transportation The Ohio State Univeristy November 30, 2005. [11] Heintz, F., Rudol, P., Doherty, P., "From Images to Traffic Behavior - A UAV Tracking and Monitoring Application". Proceedings of the Tenth International Conference on Information Fusion 2007, Québec, Canada, July, 2007. [12] Puri, A. , Valavanis, K. P., Kontitsis, M., "Statistical Profile Generation for Traffic Monitoring Using Real-time UAV-based Traffic Video Data", Mediterranean Conference on Control and Automation - MED'07. 39 [13] Chen, Y. M., Dong, L., and Oh, J.-S., "Real-Time Video Relay for UAV Traffic Surveillance Systems Through Available Communication Networks", in Proc. IEEE Wireless Communications and Networking Conference (WCNC), Mar. 2007. [14] "Traffic Surveillance", Comet Projet (IST-2001-34304). Avaliable : http://www.comets- uavs.org/applications/traffic.shtml [15] Rathinam, S., Almeida, P., Kim, Z., Jackson, S., Tinka, A., Grossman, W., Sengupta, R., "Autonomous Searching and Tracking of a River using an UAV", Proceedings of the 2007 American Control Conference, New York City, USA, July 2007. [16] Brecher, A., Noronha, V., Herold, M., "A Roadmap For Deploying Unmanned Aerial Vehicles (UAVs) In Transportation - Summary Of Findings", Specialist Workshop, December 2, 2003, Santa Barbara, CA. [17] FAA AFS-400 UAS POLICY 05-01 Unmanned Aircraft Systems Operations in the U. S. National Airspace System - Interim Operational Approval Guidance. September 16, 2005, Available: http://www.eoss.org/faa/AFS_400_UAS_POLICY_05_01.pdf [18] FAA Interim Operational Approval Guidance 08-01, Unmanned Aircraft Systems Operations in the U. S. National Airspace System, March 13, 2008. Available:http://www.faa.gov/aircraft/air_cert/design_approvals/uas/reg/media/uas_guida nce08-01.pdf 40 [19] FAA AC 91-57, Model Aircraft Operating Standards, June 09, 1981 Available:http://rgl.faa.gov/Regulatory_and_Guidance_Library/rgAdvisoryCircular.nsf/0 /1acfc3f689769a56862569e70077c9cc/$FILE/ATTBJMAC/ac91-57.pdf [20] K. Ro, J.-S. Oh, and L. Dong, "Lessons Learned: Application of Small UAV for Urban Highway Traffic Monitoring", in Proc. 45th AIAA Aerospace Sciences Meeting and Exhibit, Jan. 2007. [21] FAA, Unmanned Aircraft Operations in the National Airspace System, 14 CFR Part 91, Docket No. FAA-2006-25714, February 6, 2007. [22] National Geospatial – Intelligence, Length of a Degree of Latitude and Longitude, Available: http://www.nga.mil/MSISiteContent/StaticFiles/Calculators/degree.html [23] Grewal, M. S., Weill, L.R., and Andrews, A.P., Global Positioning Systems, Inertial Navigation & Integration, 2nd ed., Wiley & Sons, New Jersey, Chap. 1, 2007. [24] Ford, T., Neumann, J., Bobye, M., and Fenton, P., "OEM4 Inertial: A Tightly Integrated Decentralized Inertial/GPS Navigation System", Proceedings of ION GPS '01, Salt Lake City, Utah, Sept. 2001. 41
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1992 Pontiac Bonneville Q&A
1992 Pontiac Bonneville Question: looking to replace the dashboard of 1992 Bonneville did the dash change in 93-98
have found molded dash boards that fit over original dash but not for my 92. Any help would be appreciated. it has 85,000 original miles and runs like a champ would like to get the interior replaced & painted. the dash is holding me back. not interested in putting carpet over the dash. thanks - mrajv
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Daily VideoAugust 21, 2009
Oklahoma Youth Find New Career Prospects in Wind Energy
While Oklahoma has long been associated with oil and natural gas production, some of the state's young people are turning to its burgeoning wind turbine industry for jobs amidst a recession and global energy crisis.
Oklahoma's secretary of commerce and tourism, Natalie Shirley expects the wind turbine industry to generate $2 billion in business revenues over the next 10 years. A naturally windy state, she predicts that Oklahoma could become the second-highest wind energy provider in North America by 2030.
In this installment of the Generation Next series, which follows young people as they forge new paths in tough economic times, NewsHour correspondent Judy Woodruff talks to young people from Oklahoma who are looking for career prospects in the wind industry.
"We can't always depend on oil. There's been booms, there's been busts with the oil industry. You don't know when the next one is, for a boom. You don't know when the next bust is, either. But wind industry looks like it's going to be something that's going to be a constant. You know, the wind never really stops blowing in Oklahoma. It's always windy here." – Nathan Wright, Jr.
"People are excited about the opportunities that exist in green energy, and they want to know, what can I do for my environment? What can I do for my family? And what can I do for my state? And wind answers all three of those." – Natalie Shirley, Oklahoma secretary of Commerce and Tourism.
"We've encouraged our kids, you know, don't put all your ducks in one tub, you know? Have a backup plan. Don't just have one skill. You know, be flexible, where you're not just stuck with one thing to do, you know, like we were with him with the oil field and then having to find something else to do." – Lisa Wright, mother
"I think it's going to affect our generation a lot. We have to be wiser with our decisions than how we handle things with the economy, with energy than previous generations have, because if we don't change how we're doing something, we keep going down the same road, it's not going to get any better, it's not going to improve." – Nathan Wright, Jr.
Warm Up Questions
1. What is alternative energy? What different kinds of alternative energy are there?
2. What is wind energy?
Discussion Questions
1. The United States is largely dependent on oil and natural gas for its energy production. How does that affect our economy? Our environment?
2. Do you think its important for young people to go into alternative energy? Do you think it is something you should be studying in school? Explain.
3. In what ways is the recession changing your community? Do you know people who are looking for new career paths as a result of the recession?
4. Are you interested in a career that has been affected by the recession?
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IT WAS a sad but humane outcome for a three-metre pygmy sperm whale which beached itself on Back Beach, Brooms Head on Thursday.
After at least 20 hours of struggle, the whale was put down by lethal injection as the small group of volunteers, who made its last few hours more comfortable, looked on.
The whale was first noticed by Susie Johnston of Brisbane who was running along the beach about 5pm on Wednesday.
"I thought it was a shark," she said. "It was on the edge of the water, just in a gully."
Mrs Johnston said she ran back to the family's holiday house at Broom's Head and a whole group returned and worked to turn the whale around and take advantage of the high tide.
"It seemed to be bleeding a lot and it was much bigger than it is today," she said.
The extended family group, including six children, managed to get the whale to swim away from the beach at one stage.
"We let out a big cheer, but then it turned around and came back," she said.
"The kids were so upset, four of them were crying and the older two were fighting back the tears."
The family continued their mission until dark but eventually had to leave.
Expecting to find the whale had died over night, Mrs Johnston and her sister-in-law Teresa Eyre returned to the site on Thursday morning about 9am and found it still alive but fully beached in the mid-tide.
They began dousing the whale with sea water using a bucket and a container to keep it hydrated.
National Parks and Wildlife Service (NPWS) ranger Dave McFarlane arrived on the scene and helped co-ordinate the bucketing of sea water onto the stranded beast and managed to recruit some more volunteers.
He covered the animal with a cotton sheet and towel and built an ad hoc shade for its head.
Though it was still breathing and flapping its tail occasionally, the whale's skin had peeled significantly and there were some abrasions around its right eye.
By the time Pet Porpoise Pool veterinarian Duan March arrived about 12.30pm it was showing signs of weakness.
Within minutes Dr March assessed that the most humane thing to do was to euthanise the animal.
He said given that the whale had spent a whole night on the beach and was bleeding profusely, it had very poor prospects for recovery.
He took blood samples which, he said, would be sent off to the US for analysis and gave the animal a lethal injection in the tail.
It died peacefully a short time later.
"Usually if there is a single whale stranded, it is either sick or injured," he said.
He said he couldn't tell what caused the sperm whale to beach itself but one possibility was the morbillavirus which had become more prevalent in marine animals recently.
It is understood the whale will be buried at a nearby sand dune but Mr McFarlane said a local museum may be interested in keeping the skull.
"These whales aren't exactly common around here and they keep a low profile well below the surface mostly, unlike dolphins."
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Nielsen's top programs for July 1-7
NEW YORK (AP) — Prime-time viewership numbers compiled by Nielsen for July 1-7. Listings include the week's ranking and viewership.
1. "Under the Dome," CBS, 11.82 million.
2. "America's Got Talent" (Tuesday), NBC, 9.56 million.
3. "NCIS," CBS, 8.34 million.
4. "60 Minutes," CBS, 7.65 million.
5. "Macy's 4th of July Fireworks" (Thursday, 8 p.m.), NBC, 7.18 million.
6. "The Bachelorette," ABC, 6.34 million.
7. "Big Brother 15" (Sunday), CBS, 6.25 million.
8. "Rizzoli & Isles," TNT, 5.9 million.
9. "The Big Bang Theory" (Monday, 9:30 p.m.), CBS, 5.83 million.
10. "Blue Bloods" (Friday, 10 p.m.), CBS, 5.75 million.
11. Auto Racing: Sprint Cup/Daytona, TNT, 5.66 million.
12. "Big Brother 15" (Tuesday), CBS, 5.63 million.
13. "Dateline NBC," NBC, 5.54 million.
14. "Big Brother 15" (Wednesday), CBS, 5.42 million.
15. "Macy's 4th of July Fireworks" (Thursday, 10 p.m.), NBC, 5.35 million.
16. "The Big Bang Theory," CBS, 5.17 million.
17. "America's Got Talent" (Wednesday, 8 p.m.), NBC, 5.11 million.
18. "American Ninja Warrior," NBC, 5.04 million.
19. "2 Broke Girls," CBS, 5.01 million.
20. "Blue Bloods" (Friday, 9 p.m.), CBS, 4.78 million.
ABC is owned by The Walt Disney Co.; CBS is a division of CBS Corp.; NBC is owned by NBC Universal. TNT is owned by Time Warner Inc.
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