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INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES

Volume 5, No 3, 2015

© Copyright by the authors - Licensee IPA- Under Creative Commons license 3.0

Research article ISSN 0976 – 4380

Submitted on October 2014 published on January 2015 404

Development of Unmanned Aerial Vehicle Systems for Terrain Mapping

and Geospatial Data Management

Balaji Sethuramasamyraja, Nick Simonian, and Darnell Austin

Department of Industrial Technology, California State University, Fresno

2255 E Barstow Ave, M/S IT 09, Fresno, CA 93740-8002, USA

Impact Marketing Enterprises & Hydroponic Mastery Inc.,

Fresno, CA 93740, USA

[email protected]

ABSTRACT

The application of unmanned aerial vehicles (UAV) for short range civilian applications has

reached new dimensions with the advent of modern yet affordable autonomous guidance

systems. Low altitude light weight UAV systems were developed that have capability for

semi-autonomous and autonomous way point navigation with a payload of sensing system.

While UAV I was utilized to test the feasibility of application with manual control using

radio control and first person wireless video, UAV II was built for autonomous flight and

field data collection. With UAV II, in addition to an autopilot navigation system, a Global

Positioning Satellite (GPS) receiver and fight stabilization sensors were utilized for system

stability and guidance during completely autonomous flights. Both the UAVs successfully

performed in flight conditions for terrain mapping of property, range, agricultural and natural

resource management with respect to initial test flights, flight parameter calibration and field

trials. In UAV II, average calibration parameters were +/-0.64 deg. for pitch, +/-3.35 deg. for

roll in speed range of 40 - 50 mph indicating good flight stability. The UAVs were tested

over a user set waypoint path on fields, facilities/properties, and urban locales, altitude

ranging 100 ft to 1000 ft, speed from 25 to 60 mph, and maneuvers including straight paths,

turns, and banks. An autonomous image acquisition system was developed that records

images and videos with onboard sensors at specified time, frequency and coverage area. Geo-

referenced aerial images were created to 4 in. resolution using mosaic images and post

processing software. UAV I and UAV II maintained intended flight path parameters within

55 ft. and 8.23 ft. spatial position or cross track error, respectively.

Key words: Unmanned Aerial Vehicle, Autonomous Navigation, Farm Automation.

1. Introduction

Unmanned aerial vehicles (UAV) have shown rapid development in the recent past due to

availability of technology such as, autonomous navigation hardware, software and sensing

systems. UAV is typically defined as self-powered aerial vehicle that carries no humans,

uses aerodynamic forces for vehicle lift, fly autonomously or be remotely piloted and carry a

payload (Bone and Bolkcom, 2003). UAVs can be deployed quickly, operated in remote

areas and have relatively low operating cost. UAVs are commonly associated with defense

applications while they are constantly gaining popularity in civilian applications as well.

UAVs can benefit civilian applications such as, facilities/property management,

environmental surveillance, and terrain mapping for range/agricultural management,

photogrammetry and surveying (Doherty et al. 2000).

Development of Unmanned Aerial Vehicle Systems for Terrain Mapping and Geospatial Data Management

Sethuramasamyraja B et al

International Journal of Geomatics and Geosciences

Volume 5 Issue 3, 2015 405

Park and Ro (2004) developed a military UAV with high mobility and quick provision of

bird’s eye view video over hostile terrain with semi autonomous control using ground

operator, which was not realistic for civilian applications due to size and cost issues. Beard et

al. (2005) investigated the feasibility of avoidance system for use in military UAV

applications using semi autonomous larger aircrafts that were cost prohibitive for civilian and

scientific applications as well. In general, UAVs developed for defense were too customized

for military applications and not suitable for transfer to civilian use due to size, technology

and cost.

Hardin and Jackson (2005) developed a UAV with off-the-shelf components for low-altitude

large-scale photography for rangeland documentation. The stabilization technology available

then was limited and completely autonomous navigation was not viable which forced the use

of assisted stability control systems. Wang et al. (2008) developed a UAV system that adjusts

flight parameters in real time to provide detailed field information for decision making in oil-

gas application using neural network techniques that has become redundant now with

availability of standard flight stability and autonomous systems (hardware, firm ware and

software). Hirokawa et al. (2007) developed a simple UAV system for natural disaster

assessment and decision support on the fly through real time data relayed from onboard

sensors to a ground control station with the absence of flight stability study.

Herwitz et al. (2004) utilized a remotely controlled UAV for crop management of coffee

plantations including monitoring weeds, applying fertilizers & water, and also differential

harvesting based on fruit ripeness. This UAV was capable of completely autonomous flight

and could stay aloft for days while its large size and expensive development costs were

prohibitive for small scale civilian applications. Logan et al. (2005) reported the

technological challenges involved in small UAV development including size constraints,

development cost, lack of design and analysis tools and unique mission requirements. All

these factors add to the level of difficulty involved in the development of universal

lightweight UAVs that could be used in a variety of applications.

Although there are currently a variety of UAVs available in a range of sizes and navigation

methods which include full manual navigation control for the most basic aircraft as well as

fully autonomous navigation systems, a low cost and lightweight UAV that was easy to

deploy and completely autonomous with low fixed and operational costs for terrain mapping

in agriculture, natural resources and property management is uncommon. The basic goal of

this research was to develop a lightweight UAV that could be rapidly deployed and

autonomously controlled as applied economically in terrain mapping for property, range,

agricultural and natural resources management. The objectives were,

1. To assess feasibility of flight deployment in aerial terrain mapping, in relatively small

land parcels, < 100 acres while maintaining stability and consistent flight path

2. To design and develop of manual and autonomous aircrafts that collect geo-

referenced sensor data for decision making

2. Materials and Methods

2.1 UAV I – Manual Control

A fixed wing electric plane was designed from scratch and used an electric motor producing

200 W supplied by a lithium polymer 11.1 V 2.4 Ahr battery pack providing 12 min. flight

duration at single launch. UAV I has a 60 in. wingspan and total flying weight of 2.7 lbs

(Table 1).





Table 1: UAV I Specifications

Wingspan

(in.)

Length

(in.)

Weight

(lbs)

Power (w) Rang

e(mi

n)

Payload

(lbs)

Max speed

(mph)

60 45 2.7 200 12 1 54

UAV I was piloted using handheld Futaba 6 Channel 72 MHz radio controller (Futaba,

Champaign, IL). Video data was collected by a charged coupled device (CCD) camera, Sony

SN555 1/3rd CCD (Sony Corporation of America, NY) mounted to pan and tilt gimbals

allowing for 360 deg. pan and 90 deg. tilt. UAV I was remotely controlled via real time

video feed wirelessly sent by T524 2.4 GHz 500 mW transmitter with a maximum range of 7

miles, on board to a ground station head display unit allowing navigation control.

Figure 1: UAV I. a. Manual Flight, b: Remote Real Time Control with First Person View

(FPV)

A second still camera the Canon SD 950 IS 12.1 mega pixel (Canon U.S.A., Inc., Lake

Success, New York) triggered by Futuba radio controller, was utilized for high resolution

orthographic pictures. Panorama Tools – Graphical User Interface, PT GUI (New House

Internet Services B. V., Rotterdam, The Netherlands) application photogrammetric software

was utilized for geo-referencing and stitching mosaic images. UAV I (Figure 1) was utilized

for mosaic image collection of a 36 acre alfalfa field through manual control. UAV I

collected image and video data at 30 mph airspeed, 450 ft. above ground in north-south

pattern with turns completed to allow 100 yd. pass to pass spacing. The image acquisition

was triggered manually using handheld Futaba radio controller in 10 s intervals. A second

flight was carried out over a riverbed (Main Fork, Three River, CA) using a single pass at

900 ft. altitude in east-west direction every 15 s.

2.2 UAV II – Autonomous Control

After successful initial results from UAV I a second UAV was developed to allow for

improvement of data and image collection. UAV II was designed with an electric motor

producing 425 W supplied by two lithium polymers 11.1 V 2.5 Ahr battery pack in parallel





totaling 5Ahr. UAV II has a top mounted wing design for high lift using an under camber

airfoil 60 in. in length weighing 4.3 lbs with a maximum payload of 1 lb (Table 2).

Table 2. Specifications for UAV

Wingspan

(in.)

Length

(in.)

Weight

(lbs)

Power (w) Rang

e(min

)

Payload

(lbs)

Max speed

(mph)

60 46 3.3 425 15 1 67

The intended waypoint flight path was constructed in Google Earth (Google Inc., Mountain

View, CA) software. Using the line path tool, a waypoint navigation flight path with airspeed,

altitude parameters, camera trigger location and time interval was created for autonomous

navigation system, AttoPilot v. 1.8 beta (AttoPilot LLC, Gilbert, AZ). UAV II was manually

controlled during initial hand launch and then switched to autonomous mode before the first

waypoint. Canon SD 950 IS 12.1 mega pixel camera was used for still image capture that

was auto-triggered by Attopilot according to presets.

2.3 Navigation and Stability System Calibration

During calibration flights, UAV II had high roll and pitch oscillations that required gain

settings adjustments to improve mosaic image quality. Table 3 lists the flight conditions

while capturing images. During calibration flight runs, the flight parameter log file is updated

at 5Hz frequency and pitch, roll, speed, heading, data fields as well as other log data was used

for precision adjustment of gain settings. The gain value (0 – 100) for PID controller was user

set (Table 4). Among the seven servo settings, camera stabilization servos were not used

while rudder servo was disabled to suit UAV II wing design.

Table 3: Flight Parameters during UAV II Calibration run to acquire still images

Photo

Number

Pitch

(deg.)

Roll

(deg.)

Altitude

(ft.)

Camera

trigger error

(ft.)

Heading

(deg.)

Speed

(mph)

1 -2.59 -8 606 6 150 42

2* 2.19 .0.64 570 6 138 43

3 5.35 -.16 544 0 142 41

4 4.93 -4 541 0 146 41

* Refers to < 3.5 degrees maximum allowed pitch or roll for photo collection.

A maximum allowed pitch or roll angle of 3.5 degrees for each image was used to determine

if that image would be usable for the creation of the final mosaic image. Pitch or roll angles

above 3.5 degrees tend to create large distortion of the mosaic image and degrade its usability

and accuracy. It’s clear that only one image met the minimum requirements requiring

further gain adjustments for oscillation dampening. Roll and pitch oscillations are evident in

log files and review of log files is used to make PID adjustments. (Figure 2a and 2b).





-20

-10

0

10

20

30

40

50

1 9 17 25 33 41 49 57 65 73 81 89 97 105 113 121

Degrees

Time (0.2 s Intervals)

Pitch and roll before adjustments

pitch

Roll

-20

-10

0

10

20

30

40

50

1 9 17 25 33 41 49 57 65 73 81 89 97 105 113 121Decimal Degrees

Time (0.2 s Intervals)

Pitch and roll after adjustments

pitch

roll

Figure 2: Pitch & Roll after adjustments - executes a 90 deg. turn into a 450 yd. straight line

path after PID fine tuning. a. P and D for Roll - 45 & 2. Pitch P and D for Pitch - 33 and 0,

and b. P and D for Roll - 34 & 10. Pitch P and D for Pitch - 58 and 18

UAV II flight stability requires adjustment of automatic servo response time using

Proportional-Integral-Derivative, PID controller gain setting. Integral gain values were auto-

generated based on user set proportional and derivative gain settings that enabled UAV II

react to its dynamic environment by producing controlled pitch, roll and speed changes for

path tracking.

UAV II autonomous stabilization was achieved with thermopile sensors that send analog

feedback signal to the Attopilot system, operating in 5.5 and 15 microns infrared spectrum

based on the difference between sky and ground temperature. The Attopilot system also has

an absolute barometric pressure sensor and pitot tube airspeed sensor as a feedback loop to

increase precision and minimize failure.

UAV I and UAV II path tracking performance could be evaluated using cross track error

parameter which is defined as the difference between intended and the actual path or mean

actual distance in ft. of separation between the intended and actual path. In the case of manual

flight, a set of reference points or ground markers were utilized as the intended path while the

actual path was recorded using an onboard GPS system. In the event of autonomous mapping,

a true geo-referenced intended path was loaded to the UAV II system prior to launch which

was compared to the actual path tracked by the GPS system.





Table 4: Attopilot Gain Settings

Servo Proportional Gain

Settings

Derivative Gain

Settings

Control

Parameter

1 34 10 Aileron (Roll)

2 58 18 Elevator (Pitch)

3 24 0* Throttle

4 Disabled Disabled Rudder (Yaw)

5 Not Used Not Used Camera

Stabilization

6 Not Used Not Used Camera

Stabilization

7 100 0* Camera Trigger

* For Throttle: manufacturer recommended ‘0’due to lack of precision in sensor that

determines rate of change of air speed with respect to time and For Camera Trigger: D gain

setting was not required

2.4. Way Point Navigation Path Set Up

GPS was utilized for geo-referencing and waypoint tracking, mounted on top of UAV II with

clear sky view receiving radio signals from satellites for navigation, maintaining ground

speed, compass heading, and altitude. Google Earth software was utilized for waypoint

generation with geographical coordinates and altitude using feature generation tools. Under

optimal conditions, the intended waypoints for flight path generated in Google Earth software

for autonomous navigation results up to 1 yd. accuracy with respect to actual flight. A

handheld GPS locator was also utilized for assistance in waypoint placement and ground

checking.

Figure 3. Intended way point navigation flight path of UAV II





Typically, Google Earth line tool was used to create the intended flight path in the area of

interest (Figure 3). After creating a Keyhole Markup Language, KML format file with geo-

coordinates, it was post processed using an executable Attopilot Software and the resulting

text file was appended with airspeed and camera triggers for each waypoint before loading to

Attopilot system onboard UAV II.

3. Results and discussion

3.1 UAV I – Manual Control

UAV I with a GPS receiver and a wireless video camera/transmitter were utilized for flight

path simulation in a small parcel area, specifically an alfalfa field (Figure. 4). The GPS

receiver on board operating at 1 Hz. frequency in the east-west direction collected actual

flight path geographical coordinate at the rate of 1 point per second as overlaid on a Google

earth image of alfalfa field (Figure. 4). Flying 400 ft. above ground at 30 mph, UAV I

captured a data geographical coordinate using GPS receiver every 20 yd. at 1 s interval

indicating feasible spatial data collection using sensors. The total time elapsed to cover 66

acres with 1 Hz. frequency data collection was only 20 min including launch, set up and

flight path acquisition. UAV I was successful in maneuvering turns as tight as 20 yd radius,

which is good spatial resolution for sensor data on relatively small land parcels like

agricultural fields. Video feed aided manual navigation purposes from a bird’s eye view for

ease of control by the flight operator at ground.

Figure 4. UAV I manual flight path captured by on board GPS receiver at 1 Hz. frequency

while flying at 30 mph & 400 ft above ground flight path over Google Earth Image.

Figure 5. Mosaic image of 36-acre alfalfa field captured manually. Mosaic created from

approximately 20 photos. Large distortion and misalignment is evident.

Manual Path (GPS Recording)





UAV I with a still image camera on board, manually triggered image data collection every 10

s resulting in 0.1 Hz. frequency while flying 450 ft above ground covering 36 acres in 12

min. Twenty usable images were used for building a mosaic image (Figure 5) of the alfalfa

field, which were geo-referenced and stitched using remote sensing post processing using PT

GUI software.

The mosaic image generated could spot areas of variability with respect to soil, vegetation

and weed outbreaks. However, mosaic image creation was bound to high degree of distortion

due to variation in bank angle caused by pitch and roll. This explains the manual control

stability was difficult in high wind turbulence as executing smooth equal turning radius was

not achievable to high degree of precision of +/- 2.5 deg. along the intended route due to

constant crosswind corrections, lack of aircraft attitude feedback and pilot error. Further

difficulty resulted from attempting to hold course while maintaining pitch and roll near 0 deg.

during image capture. Essentially, due to high degree of pitch and roll during each image

capture, mosaic generated had degraded quality in the alfalfa field. The cross track error

between the actual flight path and intended flight path using reference ground markers along

the path of data acquisition is an estimated average of 55 ft excluding the turns. The extent of

this estimated error is highly variable based on pilot skill level as well as weather conditions.

A second test with UAV I with a still image camera on board, manually triggered image data

collection every 10 s resulting in 0.1 Hz. frequency while flying 120 acres in less than 12

min. Ten usable images were used for building a mosaic image (Figure 6) of the Main Fork

River, Three Rivers, CA using the PT GUI software. Distortion of the mosaic image

collected on this application was less due to reduced wind and a simpler straight path not

requiring turns (Figure 6).

Figure 6. Singe pass mosaic image of Main Fork Riverbed, Three Rivers, CA

3.2 UAV II – Autonomous Control

A multi pass waypoint intended path was developed for Town Arena, Three Rivers, CA for

property/event management (Table 5). The flight length was 2500 ft per pass and 278 ft

between passes while capturing digital images every 295 ft. While the total flight path is 9500

ft, the actual photo coverage length is only 4500 ft due to turns involved.





Table 5: Multi-pass mosaic image collection parameters for Town Arena, Three Rivers, CA

Relative

Altitude

(ft)

Speed

(mph)

Total

flight

path (ft)

Camera

trigger

distance

(ft)

Major length

and Width of

Flight path

(ft)

Distance of total

flight path used for

photo collection (ft)

984 41 9500 295 2500 & 560 4500

UAV II was hand launched from near the target location and manually piloted to 984 ft.

above ground and then switched to autonomous mode. The UAV maintained preset heading,

airspeeds, and altitudes while navigating the course and collected images every 295 ft along

intended waypoint flight path. (Figure 7) A total of fifteen images were collected over the

Town Arena, Three Rivers, CA.

Figure 7. UAV II Intended (Lighter) vs. Actual (Darker) Flight path over Town

Arena, Three Rivers, CA

Table 6: Roll, pitch and heading of image captured during multi pass test run of Town Arena,

Three Rives, CA

Image Pitch

(deg.)

Roll

(deg.)

Heading

(deg.)

Actual Air

Speed

(mph)

Actual

Altitude

(ft)

1 -1.29 +13.6 312 (NW) 47.8 1106

2 -1.02 +8.48 327 (NW) 46.6 1073

3* -0.39 +0.06 330 (NW) 44.1 1063

4* +0.09 +0.25 324 (NW) 44.1 1063

5 -0.27 +4.95 324 (NW) 43.4 1057

6* +0.37 -2.96 147 (SE) 41 1023

7* +0.85 -1.56 144(SE) 40.4 1023

8* +0.21 +0.01 144(SE) 41 1020

9* +2.23 -0.98 144(SE) 40.4 1020





10* +0.93 -0.85 145(SE) 40.4 1017

11 +0.50 -4.39 325(NW) 38.5 1017

12 -0.17 +8.23 320(NW) 40.4 1004

13* -0.03 +3.56 326(NW) 41 1007

14* +0.14 +0.39 327(NW) 40.4 1010

15* -1.11 +0.09 326(NW) 40.4 1020

Average 0.64 3.35 NA 42 1034 * Refers to < 3.5 deg. Cut Off for better image quality

UAV II intended and actual flight paths during image capturing showed consistent

performance along the intended flight path shown in white. The intended flight path is loaded

to the UAV at launch and is executed immediately upon switching to autonomous flight. The

intended path is the exact course the UAV should attempt to follow. Ideally the actual path

should not deviate from the intended path however due to a necessary turn radius a noticeable

deviation for intended path is expected whenever the UAV must change direction to stay on

course. The cross track error between the actual flight path and intended flight path along the

path of image capture is an average 8.23 ft excluding the turns as no image was captured

during turns enabling accurate mosaic image acquisition. Table 6 lists UAV II image

collection parameters of 7 min data acquisition including take off and landing.

Figure 8. Mosaic image of Arena, Three Rivers, CA created using the 10 images listed in

Table 6.

Of the 15 images shown in table 6 only 10 images were used for the mosaic image creation

(Figure. 8) of Town Arena, Three Rivers, CA. These 10 images were selected because they

had a roll and pitch angle equal to or less than 3.5 degrees. Due to the low roll and pitch error

and the ability to maintain target altitude and heading a mosaic image with minimal distortion

was rapidly collected. An average 0.64 deg. pitch, 3.35 deg. roll, 42 mph actual air speed

against intended 41 mph and 1034 ft. actual altitude against intended 984 ft. were achieved.

UAV II collected about ten different mosaic images and video from applications such as

property/event management, cemetery, golf course, arena, agricultural field and ranges both

during calibration runs, testing and image/video data collection.

4. Conclusion

UAVs were developed to working prototypes, one of which, UAV I with manual control was

utilized for feasibility study and the other, UAV II with autonomous control capable of GPS





based waypoint navigation with on board cameras that collected high resolution images and

video for decision making. Remote sensing post processing software was used to geo-

reference and stitch individual images to mosaics. The mosaic images and video data

collected in various applications including facility sites, ranges, agricultural fields and other

terrains were utilized for management decision making by discipline experts.

UAV II showed consistent results with an average cross track error of 8.23 ft and provides a

viable system for autonomous aerial image acquisition. UAV II average calibration

parameters were +/-0.64 deg. for pitch, +/-3.35 deg. for roll in speed range of 40 - 50 mph

airspeed enabling a stable platform for waypoint path tracking with intended target speed and

altitude. UAV I and II with on board sensing systems successfully collected about 150

different runs for mosaic images, videos and data collection including property/event

management, agricultural fields and ranges during calibration runs, testing and data collection.

The total flying time (excluding field set up, ground testing, calibration and troubleshooting)

during this research with UAVs were 3 hr in manual mode and 12.5 hrs in autonomous mode,

respectively.

Acknowledgements

Partial support for this research was provided by Dr. Jim Yager (Impact-Ag, Fresno, CA), Mr.

Gino Fargovsa (Fargovsa Farms, West Fresno), and Department of Industrial Technology

(California State University, Fresno). Partial funding was provided by our industry sponsor

Digi-Star, Fort Atkinson, WI.

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2. Bone, E., and Bolkcom, C. (2003), Unmanned Aerial Vehicles: Background and

Issues for Congress. Report for Congress. pp 1-47.

3. Doherty, P.; Granlund, G.; Kuchcinski, K.; Nordberg, K.;Sandewall, E.; Skarman, E.;

and Wiklund, J. (2000), WITAS unmanned aerial vehicle project. the 14th European

Conference on Artificial Intelligence. 747–755. 4. Almond Lifecycle and Growth.

(2010). Almond Board of California, Modesto, CA, USA.

4. Hardin P.J and M. W. Jackson (2005), An Unmanned Aerial Vehicle for Rangeland

Photography. Rangeland Ecology & Management 58(4), pp 439-442.

5. Herwitz, S. R., Johnson, L. F., Dunagand, S. E., Higgins, R. G., Sullivand, D.V.,

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7. Logan, M, Vranas, T, Motter, M, Shams, Q, and Pollock, D. (2005). Technology

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