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Autonomous visual navigation of unmanned aerial vehiclefor wind turbine inspection

Martin Stokkeland, Kristian Klausen, Tor A. Johansen

Abstract— An autonomous machine vision module forUnmanned Aerial Vehicle (UAV) navigation for inspectionof wind turbines is presented. The system estimates therelative position and distance between the UAV and thewind turbine, as well as the position of its blades, in orderto support the initial phase of autonomous inspectionbefore the UAV start to move along the blades to acquirepictures. The key algorithms used are Hough transformfor detection of the wind turbine tower, hub and blades,as well as the Kalman filter for tracking. Experimentaldata acquired by a UAV at a wind park is used toevaluate the accuracy and robustness of recognition andnavigation. It is found that under the tested conditions,the method gives sufficient accuracy for the task and canexecute in real time on a single board computer in theUAV.

I. INTRODUCTION

The commonly used approaches for wind tur-bine inspection are mainly inspection throughtelescopic lenses, by lift or climbing (includingmaintenance and repair), or recently by usingremotely piloted UAVs. Modern wind turbineshave dimensions of 100 meters or more, so anautonomous or remotely controlled UAV could beable to approach the inspection target closely withhigh accuracy compared to telescopic photography.Another factor is the cost, which is expected tobe significantly smaller with UAVs than manualinspection by climbing. For offshore wind turbinesthese arguments are even much stronger. For asimilar case, inspection of power lines, [1] de-scribed several reasons why using a small UAVis preferable.

We focus on multi-rotor UAVs, which can hoverand are typically simpler to operate and less influ-enced by vibrations than helicopters. Their primary

Center for Autonomous Marine Operations and Systems(AMOS), Department of Engineering Cybernetics, Norwegian Uni-versity of Science and Technology, Trondheim, Norway.

Corresponding author: tor.arne.johansen@itk.ntnu.no

limitation for wind turbine inspection is endurance.Such a UAV is typically set up to navigate with anInertial Measuring Unit (IMU), magnetic compass,and a Global Navigation Satellite System (GNSS),and can carry cameras to be used for navigationand inspection data acquisition. This paper willfocus on a machine vision system to autonomouslyrecognize the wind turbine’s relative yaw angle andposition of blades, which cannot be determinedwithout imaging sensors, manual operations, or adata interface to the wind turbine control system.The latter may be impractical, error-prone, or sim-ply unavailable (e.g. for an independent inspectionservice company).

Visual navigation is extensively studied formulti-rotors, in particular for navigation in GNSS-denied environments and collision avoidance [2],[3], automatic landing [4], [5], [6], [7], and visualservoing for tracking of features [8], [9], [10],[11], [12], [13], [14], [15], but is not much studiedfor wind turbine inspection application. Relatedapplications are power line inspection using UAVswhere Hough transform is used to detect the powerlines, [16], and bridge inspection using visualservoing [17].

The contribution of this paper lies in elaboratingon machine vision methods for which the windturbine can be recognized, and how its features canprovide orientation estimates. The paper focuseson the key visual features of the wind turbine, andthe use of key algorithms including the Hough-transform and Kalman-filtering. Videos of windturbines were recorded from a multi-copter UAVat Bessakerfjellet wind park for testing and valida-tion. Algorithms available in the OpenCV libraryare utilized. Emphasis is placed on keeping com-putational demand to a level which can be handledby the Single-Board Computer (SBC) which isoperating in real time on the UAV. Further detailsare found in [18].

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The scope of this research is limited to theinitial positioning and recognition phase of aninspection. That is, to approach from an initialpoint where the UAV has arrived in adjacency ofthe wind turbine using GNSS navigation or manualpiloting, to the inspection starting point which ischosen to be directly in front of the hub of thewind turbine. We point to [18], [19] for resultson camera-based tracking of position, velocity andattitude relative to the wind-turbine blades duringthe actual inspection phase.

II. AUTONOMOUS NAVIGATION

Wind turbines have typically three degrees offreedom for rotation. Their yaw angle about thetower’s vertical axis can change to optimize at-titude relative to wind directions. Second, theirblades rotate about an axis that is aligned withthe shaft that transfer power into the hub. Finally,the blade pitch angles may be possible to vary inorder to optimize power generation.

It is assumed that for inspection, the wind tur-bine is at standstill with arbitrary angles of toweryaw angle, blade rotation and pitch. The navigationrequirements for wind turbine inspection is dividedinto the following phases:

1) Initial positioning. It is assumed that GNSSand altimeter is used for initial positioningof the UAV, leading to a pre-defined absoluteposition that is at a given distance to thetower, but with unknown relative yaw-angleto the tower and blades. This distance mustbe larger than the blade radius to avoidcollision risk.

2) Initial relative positioning and recogni-tion. Using machine vision, the next step isto position the UAV directly in front of thehub, more specifically that it is located at agiven distance near a line that is orthogonalto the disc where the wind turbine bladesare, as illustrated in figure 1. In this way, theinspection starts at a known relative positionto the hub and disc.

3) Positioning and tracking for inspection.In this phase, the UAV moves along thewind turbine hub, blades and tower in a pre-defined motion pattern in order to performinspection, which typically consist of acqui-sition of pictures, navigation data and other

measurements that will be processed later.During this phase the guidance, navigationand control needs to keep the inspectionobject at the correct relative distance, attitudeand velocity for successful inspection.

4) Relative terminal positioning. Movingaway from the last inspection position to agiven (safe) distance from the wind turbinehub and blades.

In this paper we focus on the 2nd phase, relativeinitial positioning, that provides an initial configu-ration for the 3rd and 4th phases. In addition, theremight be emergency operating modes that are notconsidered here.

A. Initial relative positioning and recognition

This phase contains three separate tasks (T1,T2 and T3). Each task is given a priority fromT1 at highest to T3 at lowest. Only when theconditions for one task is fulfilled the programmoves to the next. Furthermore, if during onetask the conditions for a higher prioritized task isinvalidated the method reverts back to completethe higher prioritized task. A summary of theprocedure is given by the pseudo-code:

while not reached desire distance doif visual target not in boundary then . T1

if visual target above/below thenmove vertically

if visual target too far left/right thenrotate about the yaw axis

else if |relative yaw| > tolerance then . T2move laterally

else . T3move forward

The first task, T1, makes sure that the UAV isfacing the target position i.e. the hub of the windturbine. If the visual target is inside the boundary,the conditions for this task are fulfilled and thenext task is initiated.

For the second task, T2, the objective is toachieve the desired relative yaw angle of the windturbine by flying laterally such that the relative an-gle is reduced until a certain tolerance is reached.Naturally, by moving laterally one will also ex-perience the wind turbine moving horizontally inthe camera frame, albeit slowly compared to theeffect of UAV yaw rotation. Consequently, once

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the target moves outside the rectangle describedin the first task, the first task becomes prioritizedand adjusts the attitude to bring the target backinside the boundary. The result of the combinedalteration of T1 and T2 is thus a circular motionaround the wind turbine, as illustrated in figure 1by the segments marked T1 and T2. As the relativeyaw is reduced past a certain threshold indicatedby the green triangle, the relative yaw is deemedas sufficiently small.

T1

T2T1

T2

T3

T1

T2

Fig. 1: Top-down perspective showing an examplescenario of the navigation path (red line). Indi-cations show when each task is run. The greentriangle illustrates the tolerated range for relativeyaw.

The third task, T3, commands the UAV to moveforward towards the target until the desired dis-tance has been reached. A correct course is ensuredand maintained by the former tasks. Should adisturbance cause the UAV to lose its heading orget a too big yaw angle, it reverts to T1 or T2,respectively, to correct the error.

It is assumed that the UAV altitude is keptconstant at a suitable level near the top of thetower.

B. Estimation of Distance to Tower

The navigation approach relies on being ableto detect when the UAV has reached the desireddistance. This can be achieved by applying thepinhole camera model (e.g. [20]). By rearranging

this model with respect to a given object’s distanceto the camera, xc, one obtains

xc =f

yiyc, xc =

f

zizc (1)

where f is the focal length, (yi, zi) are imagecoordinates in the image plane, and (xc, yc, zc) arelocal coordinates in the coordinate frame alignedwith the camera mounted on the UAV. Now, letthe measured width and height of the given objectbe given by ∆yi and ∆zi in the image plane. Ifthe actual width or height of the same object isknown and given by ∆yc and ∆zc, the distanced = xc can be estimated by either of the followingequations:

d =f

∆yi∆yc =

f

∆zi∆zc (2)

A suitable target object would be the tower havinga known width. The tower has easily distinguish-able edges that provides a reliable basis for thisapproach. The tower is usually wider at its base,therefore a specific area of the tower should betargeted. The uppermost area was found to be thebest choice because this area persists inside thefield of view at all distances.

C. Estimation of Tower Yaw Angle

One can observe from Figure 2 that as the yawangle increases, two points which are behind eachother when viewed from straight ahead, tend todrift apart. In particular, the center of the hub andthe top of the wind turbine tower, which are amongthe easiest points to determine, form the distanceδ in the image plane.

Fig. 2: The gap between the hub center and towerincreases as the yaw angle increases.

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Consider the pinhole camera model:

δ =f

d|∆pw| sinψ (3)

where ∆pw is the actual distance between thetwo points (assume to be a known constant fromthe geometry of the wind turbine), and ψ is thedifference in yaw angle between the wind turbineand camera coordinate frames. Hence,

ψ = arcsin

(δd

f |∆pw|

)(4)

D. Estimation of blade orientationFor a wind turbine with three blades it is known

that there is a 120 degree angle beween them.For the initial phase of the positioning in orderto start tracking blades, it is necessary to estimatethe blades orientation relative to the fixed verticaltower as considered in Section III-D.

III. MACHINE VISION

The camera-based autonomous navigation algo-rithm runs in a loop where a new iteration isinitiated at the arrival of a new image frame, seeFigure 3.

A. Wind turbine visual featuresIn order to successfully recognize and track

the features needed to perform the estimation andnavigation functions described above, it is of keyimportance to identify the characteristics whichdistinguish the main parts of the wind turbine(tower, blades and hub) from its surroundings.

1) Texture and color: The smooth surface ofa wind turbine has virtually no texture, makingthe wind turbine more distinctively separated fromthe background. Despite being white in color, awind turbine can appear in any shade of gray orweak color depending on the lighting conditionsand reflections from its environment. However, noparticular color tone should be prominent.

2) Geometry: The distinct shape of the windturbine is of great importance as it possesses manyfeatures which can be applied by image analysisalgorithms. In particular, the straight edges of therotor blades and the tower are favorable subjectsfor edge or line detection algorithms. Further-more, the radial shape displayed by the tower

Get image

Canny edgedetector

Hough linetransform

Recognitionof tower, hub

and blades

UpdateKalman filter

Estimate yawand distance

Maneuverrelative to

wind turbine

Useprediction

fromKalman filter

Search forwind turbine

Failed detectionSuccessful detection

Prolonged failed detect

Fig. 3: Activity diagram describing the flow of themachine-vision-based autonomous navigation.

and the blades provides solid foundation for manyapproaches. In addition, a similar shape is notexpected to appear elsewhere in the environment(except for other wind turbines which might bepresent).

3) Size: Since GNSS is more practical to usewhen far away, the UAV is expected to be closeto the wind turbine when the algorithm is used. Atthis range the wind turbine, when in view, shouldcover a large portion of the image frame andstretch out of the frame when at close distances.Consequently, as long as the UAV is not movingtoo fast and is facing the wind turbine, it should beclearly visible in the image. The size of the windturbine as it appears in the image can also serveas an estimate of the distance from the UAV.

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4) Movement: It is assumed that the wind tur-bine is stopped for maintenance so that the bladesdo not rotate. However, when the UAV moves,the images show a movement of the wind turbinerelative to the background.

5) Point-of-view: It will generally be assumedthat the UAV is positioned in front or behind ofthe wind turbine such that the rotor blades ortower usually do not overlap. When directly inthe front or back the angle between the blades are120 degrees. A change of perspective skews theimage altering both angles and proportions, thusproviding cues for estimating the yaw orientationof the wind turbine.

6) Environment: Under clear weather condi-tions a blue sky serves as excellent contrast to thewind turbine. In such conditions, shadows mightcause challenges. Clouds and gloomy weatherleads to less contrast, which might also interferewith detection. Depending on the height and thepitch angle of the camera, the horizon, other windturbines or straight features may appear at varyingheight in the image. If they appears too close tothe hub, they are expected to interfere with bladedetection.

B. Hough line transform

The Hough transform, [20], is used to detectstraight line features such as the tower and bladesof the wind turbine. The progressive probabilisticHough line transform [21] in OpenCV runs effi-ciently. These properties make the Hough trans-form attractive, and it was therefore chosen as thebasis for the recognition program.

Considering the detected lines, there are someissues. Sometimes a line which should be detectedas a single line is broken into smaller segments.Another issue may appear if there is a significantamount of noise in the image. Small changes canaffect the resulting edge map, resulting in frameto frame variability of the Hough transform. Thishas a notable effect on the start and end points ofthe detected lines. Those matters aside, the Houghtransform was found to give accurate description ofthe contours of the wind turbine. The direction andlocation of the lines makes it possible to identifythe blades and tower along with their orientation.

C. Pre-processing and Canny edge-detector

The Hough transform operates on a binary rep-resentation of the image, and it is necessary withpre-processing that should also make the algorithmrobust to background, lighting conditions, UAVmotions not compensated for by image stabiliza-tion (in camera, gimbal or software), and othersources for inaccuracies.

We use the standard Canny edge detection,where the first part consists of computing direc-tional derivatives using the Sobel operator, [20],based on gray-scale images. It utilizes two 3 × 3kernels for vertical and horizontal differentiation.Then the edges are thinned by keeping only thestrongest gradients along the edge when trackingthe edges. The final step converts the edge map to abinary image and removes weak edges that are notlikely to be of significance. This is done throughhysteresis thresholding where the thresholds aretuned experimentally.

D. Wind turbine recognition

1) Locating the tower: Among the parts of thewind turbine, the tower is arguably the simplest tocorrectly identify. Similarly to the blades it tendsto exhibit long and distinct edges, but whereas theblades may settle in any orientation, the tower isfixed in its vertical stance. Therefore one couldbegin by searching for vertical lines, with a fewdegrees tolerance. An issue arises due to the preva-lent roll of the UAV. One solution is to use the rollangle estimated from the IMU to adjust the searchangle. Another option is to mount the camera on agimbal stabilizer to physically prevent rotation ofthe image frame.

Because the UAV expectedly flies at an altitudeat level with the hub, the base of the tower will bebelow the bounds of the image and consequentlythe lines of the tower continues beyond the loweredge of the image frame. Based on this knowledgeanother restriction is imposed; the lines represent-ing the tower must possess an ending point nearthe lower bound of the image.

2) Locating the blades: If at least one linewas identified as part of the tower, the algorithmproceeds to search for the lines of the blades.By identifying the highest point among all thetower lines, a first estimate is provided for the hublocation. Thus by searching for lines ending in a

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circular area around this point one can expect tofind lines belonging to the blades.

An issue with this approach occurs when thehorizon appears at a vertical position in the imageclose to the hub, which can potentially lead toit being incorrectly identified as a blade. Onecannot simply ignore horizontal lines, because (asis apparent in Figure 4) a blade may also behorizontally aligned.

The lines which were determined to belongto blades are sorted by angle and grouped withother lines of adjacent angles. Together each groupform a single blade object, where their averageangle determines the estimated angle of the blade.Similarly, the tower lines form a tower object.

The property of there being a 120 degree anglebetween the blades is utilized by implementing avoting system. A blade which receives a certainamount of votes is assumed to be a false de-tection and is removed. The voting is conductedby comparing each detected blade to each other.If the angle between two blades is not closeenough to 120 degrees by a certain tolerance, bothblades obtain a vote. The tolerance was set to 20degrees to accommodate for measurement errorand skewed angles due to potential yaw angledifference between the UAV and wind turbine.Setting the vote limit to 3, such that three votes ormore signifies a false blade, proved to work well.Thus, if for instance, the three actual blades plus afourth false blade is detected, then the false bladereceives three votes in total from the actual bladesand is removed. Meanwhile, the true blades receiveonly one vote from the false blade. Figure 4 showshow the false blade lines are removed when votingis successful.

3) Locating the hub center: The first step isto calculate intersections between the recognizedblade lines. Since all the blades beam out from thehub, the hub location can be found by calculatingtheir common intersection point. However, it canbe observed in Figure 4 that the detected linesfor the blades are sources of disagreement. Forinstance, by looking at the upper right blade oneobserves that the curvature on the lower edgecauses a steeper angle on the corresponding linecompared to the lines from the upper edge. Sincelines are detected at both edges of the bladesthere will be a spatial disagreement as well. Atthis point it is unclear which lines provide the

Fig. 4: The false detections have been removedby the voting procedure. Only the green linescorresponding to the actual blades detected.

appropriate result. This was handled by calculatingthe intersection points between every line pair,except between lines belonging to the same blade,see Figure 5.

Fig. 5: The green circles show the points wherethe blade lines intersect each other.

The second step is to obtain the average in-tersection point. From the previous step theremay appear some stray intersection points causingdiscrepant results. For instance, if the horizon isincorrectly detected as a blade it can intersect withthe other blades at a distance far from the hub.To prevent such scenarios, restrictions are imposedon the points of intersection. Points which havehorizontal position far from the line extended bythe tower are ignored. Since the actual verticalposition of the hub is more uncertain, a less strictrestriction is imposed on vertical distance based onthe temporary estimated hub center (highest pointamong the tower lines). After the points caught bythe restrictions have been removed, an average iscalculated from the remaining points which resultsin the estimated hub center position.

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E. Feature TrackingFeature tracking refers to tracking how a feature

moves or evolves over time in a sequence ofimages. This requires state information about itsposition being updated between frames. Our algo-rithm tracks the position of the hub center in theimage frame, so that the UAV can be maneuveredin the fashion described in section II.

Noise and failure to detect the hub is dealtwith using the Kalman filter with a simple motionmodel where the velocity is modeled as a Markovprocess, [20]. For the case when the hub is notdetected one may estimate its position using aprediction from the model of the filter. This isdone by only running the prediction step in theKalman filter, while skipping the measurementupdate steps. Consequently, the program will stillhave knowledge of the states of the object, andcan act accordingly. However, if the program isnot able to locate the hub after an extended periodof time, e.g. when the covariance exceeds a giventhreshold, instead of continuing to blindly rely onthe model it should switch to the searching statewhere movement is stopped until the wind turbinehas been identified again by rotating about the yawaxis to search.

IV. EXPERIMENTAL RESULTS

In this section the program will be evaluatedby using video sequences as input. The videoswere recorded at Bessakerfjellet wind farm usinga GoPro camera mounted in a stabilizing gimbalon a hexa-copter. The experiments mimic the ma-neuvering plan described in Section II-A, to getrepresentative footage at the expected range andangles. The images are down-sampled to 480x270pixels.

The UAV is controlled using an ArduPilot au-topilot. It can receive commands from the pilot viathe operator display or remote control unit. It canalso receive commends from an on-board Pand-aBoard SBC where the machine vision, estimation,navigation and high-level control is implementedin real-time.

A. Recognition performanceThe Canny edge detector was found to be a criti-

cal part in order to perform robustly under lightingvariation, and good tuning of its parameters is

(a)

(b)

(c)

(d)

Fig. 6: (a) Shows how the shadow under the hubcauses a split left edge of the tower which in (b)results in two separate Hough lines. (c) Showshow the same shadow blends into the backgroundand hides the edge, which leads to no Hough linedetected in (d).

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important. A change in lighting can affects the in-tensity distribution in the image, and can thus alterthe strength of the edges. The hysteresis thresholdvalues need to be adjusted low enough to acceptthe most prominent edges, but high enough to filterweaker unwanted edges. It should be emphasizedthat the transitions from good to bad is gradual,and we have only studied a limited number ofvideos from a single site under a limited numberof weather and lighting conditions. The Houghline transform was found to be inherently robust.Because the shape of the wind turbine alwaysremains the same, a specific set of parameters willproduce similar results unless the Canny detectoroutputs a significantly different edge map, in whichcase the Canny edge detector needs to be adjusted.

Assuming the minimum requirements for suc-cessful detection are met, it will now be discussedhow a detection may fail. This can occur wheneither the tower is not detected or if the sufficientamount of valid blades is not detected. Towerdetection fails if a too small part of the tower isvisible. If the tower edges are split into smallerlines, it can lead to failed detections of hub po-sitions. Such an issue arose with the appearanceof a shadow cast by the hub onto the tower. Forexample, Figure 6 shows how this shadow disruptsthe edge detector and consequently also the Houghtransform. Worse than segmenting the lines, it canhide the edges completely in the shadow area ifthe background is of matching color, such that thehighest point among the tower lines (which formthe center for the blade search area) is lower than itshould be. The effect is stronger when the distanceto the wind turbine is shorter, because then theshadow appears larger in the image.

Another scenario is when too many blades are(incorrectly) detected. Then there is much uncer-tainty as to which detections are true blades, andthe voting procedure will discard all detections.

B. False positives

A successful detection does not necessarily im-ply a correct detection, due to the occurrence offalse positives, being detected tower lines or bladelines which do not actually correspond to edges ofthe tower or blades.

On the other hand, false blade detections oc-curred more frequently. Every detected line which

has an end point inside the blade search radiusyields a true or false positive. This was mostcommonly caused by the horizon and the visibleparts of the hexa-copter itself, see Figure 7, andto some degree the environment when the bladesearch radius was big. However, the environmentproduced very few edges in general. Many of thefalse edges are removed by the voting procedure(section III-D.2) or are dominated by the usuallymore numerous true blade edges.

Fig. 7: The interference of the hexacopter is anexample of where the edge of another entity isfalsely detected as a blade edge, illustrated by theincorrect green line.

C. Distance estimationDistance is estimated using eq. (2) with the

width of the tower as known information. Thiswas done by first finding the outermost towerlines, and extending them to a common height andmeasuring the length between them (in pixels). Itwas considered to use the highest point among alltower lines as the common height, but since thispoint can vary significantly between frames thebottom edge of the image was chosen instead. Thiscauses a more stable estimation, but correspondsto a more uncertain actual width of the tower.

The results are shown in figure 8. It is observedthat the resolution of the distance estimations islow, which is due to the low display resolution inthe image. For instance, a single pixel in differencecauses a jump in distance estimation from 35.2m to 39.6 m. The results show estimations withsomewhat larger distance than this in general. Forclip # 1 and towards the end of clip # 2, the actualdistance is roughly unchanged, but the resultsshowed the estimated distance increasing. This is

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because the hexa-copter was flying upwards, andthus a higher part of the tower (which is thinner)is measured. The spikes almost reaching zero fromvideo clip # 1 are caused by detection of the towerof another wind turbine. In video clip # 2 there ismost variation at the beginning towards the 150thframe because the hexa-copter moves forward andupwards.

100 200 300 400 500 600 700 8000

10

20

30

40

50

60Estimated distance, video clip #1

Dis

tance [

m]

Frame number

50 100 150 200 250 300 350 400 45010

12

14

16

18

20Estimated distance, video clip #2

Dis

tance [m

]

Frame number

Fig. 8: Estimated distances for video clip # 1 and2. Actual distances were roughly estimated to liearound 30 and 15 meters for clips # 1 and 2,respectively.

D. Yaw angle estimation

Yaw estimation was done using eq. (4). Distanceestimates from the previous section was used ford. The results are shown in Figure 9. The aver-age value does not lie too far from the expectedvalues, but there is significant amounts of noise.An essential cause for the high sensitivity is thatthe actual distance between the hub center and thetop of the tower is short compared to the distancefrom the hexa-copter and the measured length,δ. Since a low threshold value for the Cannyedge detector was used, the hub center estimationwas less accurate, affecting the measurement of δwhich relies on this value.

100 200 300 400 500 600 700 800−100

−50

0

50

100Estimated yaw angle, video clip #1

Yaw

[deg]

Frame number

50 100 150 200 250 300 350 400 450−100

−50

0

50

100Estimated yaw angle, video clip #2

Yaw

[deg]

Frame number

Fig. 9: Yaw angle estimation for video clip # 1and 2. The actual angles were roughly estimatedto lie around -10 and 30 degrees for clips # 1 and2, respectively.

E. Real-time performance

The computation time was examined on thePandaBoard SBC with an e-CAM-51 USB camera.The results show a computation time per framemostly in the range of 90-140 ms which translatesto around 7-11 Hz. The Hough transform accountsfor the majority of the computations with 60-80 msper frame.

V. CONCLUSIONS

A machine vision algorithm for recognition andtracking of a wind turbine has been presented.Based on knowledge from the tracking data amethod was suggested for approaching the windturbine for inspection. The wind turbine recogni-tion algorithm was based primarily on the Houghline transform, due to its ability to capture themain features of the wind turbine and its low com-putational demand. The hub center position wasestimated by analyzing Hough lines and trackedusing a Kalman filter. Experimental data acquiredby a UAV at a wind park is used to evaluate theaccuracy and robustness of recognition and navi-gation. It is found that under the tested conditions,

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the method gives sufficient accuracy for the taskand can execute in real time on a single boardcomputer in the UAV.

ACKNOWLEDGMENTS

This work was supported by the Research Coun-cil of Norway through the Centers of Excel-lence funding scheme, Project 223254 - Centrefor Autonomous Marine Operations and Systems(AMOS). The authors thank UAV operations man-ager Lars Semb at NTNU, Øystein Skotheim atSINTEF ICT, and TrønderEnergi Kraft AS foraccess to Bessakerfjellet wind park.

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