A VISION-BASED UNMANNED AERIAL VEHICLE NAVIGATION METHOD

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1 st Intenational Confeence on Innovative Reseach and Maitime Applications of Space Technology IRMAST 5 A VISIO-BASED UMAED AERIAL VEHICLE AVIGATIO METHOD Paweł Budziakowski, Maek Pzyboski, Jakub Szulwic Faculty of Civil and Envionmental Engineeing, Gdansk Univesity of Technology, autowicza St. /, 8-33 Gdansk, Poland, pawelbudziakowski@gmail.com, maek.pzyboski@pg.gda.pl, szulwic@pg.gda.pl Atu Janowski Faculty of Geodesy, Geospatial and Civil Engineeing, Univesity of Wamia and Mazuy in Olsztyn, M. Oczapowskiego St., -79 Olsztyn, Poland, atu.janowski@geodezja.pl Summay The satellite navigation systems ae the main position souces fo unmanned aeial vehicles (UAVs). This fact limits the aea of UAVs opeation to the places whee adio signals is visible fo a satellite navigation system eceive, mounted on the vehicle - outdoo navigation. Closed spaced ae unavailable fo vehicles which navigation is based on global satellite navigation systems (GSS). Miniatue UAV (MiniUAV) is able to opeate successfully within a indoo envionment, howeve is has to be suppoted by teleopeation system opeated by a man. MiniUAV is not able to plot the tajectoy and detemine thei position elatively to the indoo envionment. The eseach pesented in the pape ae solving a poblem of indoo navigation position tacking. Developed and tested method is based on image data acquied by a single non-metic digital camea placed on miniuav. The pape pesents the method of solving a own UAV position, based only on image data, as well as pocess of UAV navigation suppoted by developed method and the main advantages and limitations. The method is based on photogammety and a moden compute vision algoithms..intoduction.. Poblem statement The satellite navigation systems ae the main position souces fo unmanned aeial vehicles (UAVs). This fact limits the aea of UAVs opeation to the places whee adio signals is visible fo a satellite navigation system eceive, mounted on the vehicle []. Satellite navigation detemines a UAV opeation envionment, so UAV is able to opeate only within an outdoo envionment. Closed spaces ae unavailable fo vehicles which navigation is based on global satellite navigation systems (GSS). Miniatue UAV (MiniUAV) is able to opeate successfully within an indoo envionment, howeve is has to be suppoted by a man, who is opeating it emotely. evetheless, MiniUAV is not able to plot the tajectoy and detemine thei position elatively to the indoo envionment. The fact, that MiniUAV is not able to plot own tajectoy without access to IS (inetial navigation system) o GSS, being inside a closed envionment, inspiited this eseach. It has been noticed, that evey MiniUAV is equipped with a small single non-metic camea. This type of senso is a good souce of data, which can be used fo a navigation puposes, without any additional installation on boad MiniUAV equied []. Usually, low esolution video data fom vehicle is tansmitted to an opeato, in ode to suppot a navigation decision pocess. High esolution video is usually gatheed fo cucial envionmental data collecting, enhancing and updating moden Geogaphical Infomation System (GIS) database, eithe fo geogaphical o counteteoism puposes [3].

2 . Poposed method.. avigation pocess phases The MiniUAV navigation pocess Fig., suppoted by developed method, has been divided on 4 phases. Fist, -phase is conducted in a satellite navigation available envionment. The tajectoy [4,5,6] is plotted using a main navigation system. A VISUAL-phase is conducted within a envionment, whee satellite signal is unavailable. The vehicle is able to poceed though that envionment and plot own tajectoy using pesented method based on a vision data acquied by a on-boad single non-metic camea. A COMB-phase is conducted within a envionment, whee main navigation system signal is being peiodically lost o its accuacy is dastically diminished. In this phase, a vision data ae suppoting a main navigation system poviding a tajectoy, which is esistant to a position changes caused by a satellite signal lost. A SYCH-phase is used fo a efeence system tansfomation. Figue. Motion estimation.3 Synchonization phase The tajectoy descibed by matix shown in equation () is efeed to a fist image taken, at time t= in camea coodinates. The diection of camea coodinates axes ae aligned with coesponding axes of st image. This fist image is called CCf Camea Coodinate fame, and duing synchonization phase CCf is efeed to the wold coodinates system, then it is called WCf Wold Coodinated fame. ICf Image Coodinate fame epesents a coodinate system located at the image level (Fig. ) []. Figue. MiniUAV navigation pocess... Motion estimation The VISUAL-phase is based on own motion estimation algoithms [7,8] A elation between two neighbouing images is epesented by a P CAM(t,t+) matix Fig.. This matix descibes a otation R and tanslation T between images taken in time t and t+, this matix can be witten: P RT t t t 3 3 CAM ( t, t) ( t, t) () A full tajectoy is epesented by a cumulative matix multiplication, equation () [9]: 3 t P [ RT ] () t i Figue 3. Synchonization phase The elationship between two coodinate systems WCf and CCf ae found by using pais of measuements of the coodinates of a numbe of points in systems. This is descibed by equation (3). P matix descibes a tajectoy efeed to a wold efeence system. P new S R P T (3) A detailed knowledge about wold efeence system is not equied, because S scale, R otation matix, and T tanslation vecto ae found, duing synchonization phase (Fig.3), with efeence to known own tajectoy detemined by a Mini UAV main navigation system i.e.

3 , fo matching points. A optimal scale, otation and tanslation paametes of equation (3) is found by minimalizing a following function: min S R P T P (4) t whee, P descibes a known tajectoy obtained by a main navigation system. Equation (4) has been solved using quatenion algeba descibed in []..3. Algoithms Motion estimation, done duing VISUAL-phase is the pocess of estimating the spatial geometical elation between images. This elation is estimated using image featue matches. In ode to succeed this, the intinsic paametes of the camea need to be known. Due to the fact that this method is basing on images obtained by a non-metic camea, intinsic paametes (5), ae to be estimated duing a calibation pocess. M i f x ox o f y y (5) All elements of intinsic matix M i like focal length f x and f y in effective hoizontal and vetical pixel size units, pincipal point coodinates o x o y, skew coefficient γ have been estimated using Zhang method [], as well as lens distotion coefficients (adial and tangential distotions). Having compensated the images fo distotions, next step is to estimate the spatial elation between a pai of images. To do that, the nomalized 8-point algoithm [7,8,3,4] has been used. RASAC [5,6] has been used fo obust outlie ejection. A SIFT [7] detecto has been used fo selecting image featues as well as SIFT desciptos ae used fo the matching pocess. 3. Results 3.. System setup An expeiment has been conducted using a nonmetic camea type GoPo Heo 3 Black Edition. The camea has been mounted on RTK Leica pole, 3 cm unde antenna. Method has been implemented in Matlab softwae, using a FIT3D Toolbox fo Matlab [] and Camea calibation toolbox fo Matlab [8]. A fou test tajectoies (Fig. 4) planed wee measued by RTK, and simultaneously taking pictues by a camea. Both, fist and second tajectoies wee measued by RTK in manual mode, camea in single shot mode (photos wee taken in the same time with position taking). Measues offeed a maximal standad deviation of position (RTK in manual mode): σ =,9 m, σ E =, m, σ H =, m. Tajectoies thid and fouth measued by RTK in automatic mode (position obtained evey, s), camea in timelapse mode (photos took evey,5 s), offeed a maximal standad deviation of position: σ =,98 m, σ E =,3 m, σ H =,84 m. Standad deviation is shown on Figues 7 and 9 as a ed dotted plot. Figue 4. A planed and measues tajectoies 3.. Calibation A calibation pocess was done using Camea calibation toolbox fo Matlab [8]. A thee diffeent size calibation matix (plana checkeboad) wee used fo the best intinsic matix and distotion paametes estimation, Fist matix with x7 mm squaes, second 5x 5mm squaes and thid x3 mm squaes. All images wee ectified using obtained distotion coefficients. As a esults and calibation pocess showed (Fig. 5), intinsic paametes of nonmetic camea (GoPo) obtained by a Matlab tool ae sufficient fo a navigation puposes. Due to the fact, that GoPo offes a fish eye lens type, which has a significant adial distotion, some difficulties wee obseved, as fo distotions coefficients calculation. Figue 5. Complete distotion model of GoPo camea

4 3.3 Algoithm paametes In ode to find most sufficient intinsic matix coefficients, paametes of SIFT and RASAC algoithms, the minimal mean squae eo: of this diffeence (f dif ). Pactically, a navigation duing a VISUAL phase, has to be limited in time. MSE tans ( S t vis S ), (6) whee: S P( t) and P(t) is a diffeence between points of tajectoy ( t, t ) and P P new ( t, t ), was calculated. Fo calibation esults obtained with usage of thid plana checkeboad MSE tans had the smallest value, and this intinsic matix coefficients whee used fo next calculations. Pepaed paametes fo a SIFT and RASAC esulted with smallest MSE tans fo SIFT theshold =.4, RASAC theshold =. and RASAC iteations =, espectively with [] notations. That paametes wee used fo final tajectoies calculation. 3.4 Tajectoies Final esults ae shown on Figues 6 and 8. P positions taken by RTK, P VIS positions obtained by pesented method. Scale S, otation matix R and tanslation vecto T (Eqn. 4) ae found fo ecoded tajectoies fo = 3 % of numbe of all tajectoy points. Exact numbe =7 fo nd tajectoy and =9 fo 4 th tajectoy. As analysis showed, it is stongly ecommended that in ode to solved equation 4 duing a synchonization phase unnamed platfom have to execute minimum one tun. Figue 7. Result functions nd tajectoy Manual measuement offeed a minimal standad deviation, which is athe not usual fo navigation systems mounted on MiniUAV. The situation, that standad deviation is simila to navigation system mounted on MiniUAV has been simulated duing automatic measuement and shown fo 4 th tajectoy. It is clealy visible on figue 9 (5s<t<s) that position obtained by a pesented method is moe stable and not pone on dop of position accuacy. Dop of position accuacy especially caused by multipath signals o natual and atificial intefeence. This stability can be used in COMB phase, in the envionment whee a accuacy can be suffeed. The fact that position is calculated elatively to the envionment acquied fom images, ceates autonomy independence of satellite positioning system. Figue 6. nd tajectoy RTK and VISUAL A functions of absolute sum of X, Y and Z and VISUAL coodinates with standad deviation of position ae shown of figues 7 and 9. Diffeences of function f and f vis show an exponential incement Figue 8. 4 th tajectoy RTK and VISUAL

5 MAEtans Svis S, (8) t MSEtans Svis S, (9) t AEot, () t vis MAEot vis, () Figue 9. Result functions 4 th tajectoy 3.5 Achieved accuacy In ode to asses achieved accuacy and compae ecoveed tajectoy to the an incemental metics has been intoduced, ΔS fo tanslation and Δϕ fo otation. As Fig. -left shows an incemental tanslation fo a tajectoy measued manually with a low standad deviation, both plots ae coesponding to each othe. Whee a eceive suffeed fom vaious intefeence which is indicated as a pick on Fig. -ight, a visual method clealy not eacts on that. A picks of incemental otation ae even moe visible on Fig. -ight. whee: MSEot vis( t), () t ( p( t), p( t )) ( t ). (3) p( t) p( t ) A calculated absolute eo fo nd tajectoy (Fig. ) show that is mean value is.3 m in tanslation and 4.5 deg, fo otation. Mean absolute eos calculated fo 4 th tajectoy ae espectively.6 m and.75 deg. Figue. Tanslation (left) and otation (ight) absolute eo nd tajectoy. Figue. Compaison of incemental tanslations obtained with and visual method nd tajectoy (left), tajectoy 4 th (ight). Figue. Compaison of incemental otation obtained with and visual method nd tajectoy (left), 4 th tajectoy (ight). An eos has been calculated, in accodance with following equations: AEtans S S, (7) vis Figue 3. Tanslation (left) and otation (ight) absolute eo 4 th tajectoy. 3. Conclusions The pape pesented the method of solving an own UAV position, based on image data, as well as pocess of UAV navigation suppoted by developed method. As esults showed, a MiniUAV equipped with small nonmetic camea, using poposed method, is able to continuously plot own tajectoy based only on image data. It is opening a new aeas fo navigating platfoms, the aeas whee taditional satellite navigation systems suffeed fom eithe a loss of signal o stong

6 intefeences [9]. It has been also poved, that the method can be used fo own tack stabilization. The use of the descibed method can be extended on the use of data fom geopotals o esouces of satellite images [, ], howeve, a numbe of specific featues and data fomats should be taken into account befoe the data can be sufficient fo this pupose. Refeence [] A. Janowski, A. owak, M. Pzyboski, J. Szulwic: Mobile Indicatos in GIS and Positioning Accuacy in Cities. Rough Sets and Intelligent Systems Paadigms, RSEISP 4. LCS 8537,(4) [] A. Stateczny: Atificial neual netwoks fo compaative navigation. Book Edito(s): Rutkowski, L., Siekmann, J., Tadeusiewicz, R., et al. Atificial Intelligence and Soft Computing - ICAISC 4. LAI, 37 (4) 87-9 [3] J. Dabowski, M. Kulawiak, M. Moszynski, K. Buniecki, L. Kaminski, A. Chybicki, A. Stepnowski: Realtime Web-based GIS fo Analysis, Visualization, and Integation of Maine Envionment Data. Poceedings of Infomation Fusion And Geogaphic Infomation Systems,, Lectue otes in Geoinfomation and Catogaphy (9) 77-88, DOI:.7/ _9 [4] A. Stateczny, W. Kazimieski: A compaison of the taget tacking in maine navigational adas by means of GR filte and numeical filte. 8 IEEE Rada Confeence. IEEE Rada Confeence, Roma,-4 (8) [5] W. Kazimieski, A. Stateczny: Optimization of multiple model neual tacking filte fo maine tagets. 3th Intenational Rada Symposium (IRS). Intenational Rada Symposium Poceedings, Wasaw () [6] W. Kazimieski, G. Zaniewicz, A. Stateczny: Veification of multiple model neual tacking filte with ship's ada. 3th Intenational Rada Symposium (IRS). Intenational Rada Symposium Poceedings, Wasaw () [7] R.I. Hatley,A. Zisseman: Multiple View Geomety in Compute Vision. Cambidge Univesity Pess, ISB: 55458, second ed. (4) [8] R. I. Hatley: In defense of the eight-point algoithm. IEEE Tansactions on Patten Analysis and Machine Intelligence, 9(6) (997) , DOI:.9/ [9] I. Keso, M. Sevovic, S. Segvic: A novel geoefeenced dataset fo steeo visual odomety, Coatian Compute Vision Wokshop (3) [] I. Esteban, J. Dijk, F, Goen: FIT3d Toolbox: multiple view geomety and 3D econstuction fo MATLAB, Intenational Symposium on Secuity 7835 (), 7835J J-9 [] B. K. Hon: Closed-fom solution of absolute oientation using unit quatenions, Jounal of the Optical Society of Ameica A, 4(4) (987) [] Z. Zhang: Flexible camea calibation by viewing a plane fom unknown oientations. Intenational Confeence on Compute Vision (999) [3] A. Janowski, J. Szulwic: Synchonic digital steeophotogaphy and photogammetic analyses in monitoing the flow of liquids in open channels. 9th Intenational Confeence on Envionmental Engineeing. (4) DOI: /.3846/envio.4.79 [4] A. Janowski, K. agodzka-godycka, J. Szulwic, P. Ziolkowski: Modes of Failue Analysis in Reinfoced Concete Beam Using Lase Scanning and Syncho- Photogammety (How to apply optical technologies in the diagnosis of einfoced concete elements?). ACSEE 4, ISB: (4) DOI:.54/ [5] J. Janicka, J. Rapinski: Outlies Detection by Ransac Algoithm in the Tansfomation of D Coodinate Fames. Boletim de Ciencias Geodesicas, (3) (4) 6-65 [6] M.A Fischle, R.C. Bolles: Random sample consensus: a paadigm fo model fitting with applications to image analysis and automated catogaphy. Communications of the ACM 4.6 (98) [7] D.G. Lowe: Distinctive image featues fom scaleinvaiant keypoints. Intenational Jounal of Compute Vision 6. (4) 9- [8] J. Bouguet. Camea calibation toolbox fo matlab. (3) [9] M. Pzyboski, J. Pychla: Reliability of the navigational data. Confeence: Intenational Intelligent Infomation Systems/Intelligent Infomation Pocessing and Web Mining Confeence Advances in Soft Computing (3) [] A. Zwiowicz-Rutkowska: The Use of the Spatial Data Infastuctue in Spatial Planning: Case studies in Poland. 5th Cental Euopean Confeence in Regional Science - CERS, (4),6-5 [] K. Pyka; W. Dzewiecki, K. Benat, A. Wawzaszek, M. Kupinski: Usefulness of wavelet-based featues as global desciptos of VHR satellite images. Confeence on Image and Signal Pocessing fo Remote Sensing XX Location: Amstedam. Image and Signal Pocessing fo Remote Sensing XX. Poceedings of SPIE, 944 (4) 944D

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