ALS-AIDED NAVIGATION OF HELICOPTERS OR UAVs OVER URBAN TERRAIN

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1 ALS-AIDED NAVIGATION OF HELICOPTERS OR UAVs OVER URBAN TERRAIN M. Hebel a, U. Stilla b a Frauhofer Istitute of Optroics, System Techologies ad Image Exploitatio IOSB, Ettlige, Germay - marcus.hebel@iosb.frauhofer.de b Photogrammetry ad Remote Sesig, Techische Uiversität Müche, Müche, Germay - stilla@tum.de KEY WORDS: Airbore laser scaig, LiDAR, GPS/INS, o-lie processig, terrai-based avigatio, urba data ABSTRACT: Airbore laser scaig (ALS) of urba regios is commoly used as a basis for subsequet city modelig. I this process, data acquisitio relies highly o the quality of GPS/INS positioig techiques. Typically, the use of differetial GPS ad high-precisio GPS/INS postprocessig methods are essetial to achieve the required accuracy that leads to a cosistet database. Cotrary to that approach, we aim at usig a existig georefereced city model to correct errors of the assumed sesor positio, which is measured uder o-differetial GPS ad/or INS drift coditios. Our approach accouts for guidace of helicopters or UAVs over kow urba terrai eve at ight ad durig frequet loss of GPS sigals. We discuss several possible sources of errors i airbore laser scaer systems ad their ifluece o the measured data. A workflow of real-time capable methods for the segmetatio of plaar surfaces withi ALS data is described. Matchig plaar objects, idetified i both the o-lie segmetatio results ad the existig city model, are used to correct absolute errors of the sesor positio. 1.1 Problem descriptio 1. INTRODUCTION Airbore laser scaig usually combies a LiDAR device (light detectio ad ragig) with high-precisio avigatioal sesors (INS ad differetial GPS) mouted o a aircraft. Rage values are derived from measurig the time-of-flight of sigle laser pulses, ad scaig is performed by oe or more deflectio mirrors i combiatio with the forward movig sesor platform. The avigatioal sesors are used to obtai the 3D poit associated with each rage measuremet, resultig i a georefereced poit cloud of the terrai. Laser scaig delivers direct 3D measuremets idepedetly from atural lightig coditios, ad it offers high accuracy ad poit desity. A well-established applicatio of laser poit clouds acquired at urba areas is the geeratio of 3D city models. However, the overall precisio of the derived city model highly depeds o the accuracy of the data iput, which is directly depedet o the exactitude of the avigatioal iformatio. Great efforts are usually required durig data acquisitio ad postprocessig i order to achieve high fittig accuracy of multiple ALS datasets (e.g. eighborig strips). While ALS data acquisitio is commoly doe to supply other fields of studies with the ecessary data, few examples ca be foud where laser scaers are used directly for pilot assistace. Oe of these examples is the HELLAS obstacle warig system for helicopters (Schulz et al., 2002), which is desiged to detect wires ad other obstacles for icreased safety durig helicopter missios. Despite icreasig performace of LiDAR systems, most remote sesig tasks that require o-lie data processig are still accomplished by the use of covetioal CCD or ifrared cameras. Typical examples are airbore moitorig ad observatio devices that are used for automatic object recogitio, situatio aalysis or real-time chage detectio. Utilizatio of these sesors ca support law eforcemet, disaster maagemet, ad medical or other emergecy services. At the same time, it is ofte desirable to assist pilots with obstacle avoidace ad aircraft guidace i case of poor visibility coditios, durig ladig operatios, or i the evet of GPS dropouts. Three-dimesioal iformatio as provided by the LiDAR sesor techology ca ease these tasks, but the existece of differetial GPS groud statios ad the feasibility of comprehesive data aalysis are ot to be cosidered for these real-time operatios. 1.2 Overview The approach of usig ALS iformatio to provide o-lie avigatio support for aircraft guidace over urba terrai is opposite to the process of city model geeratio. I cotrast to the demad for high-precisio positioig techiques, it is assumed that a proper georefereced city model is already available. ALS measuremets ad matchig couterparts i the city model ca be take ito cosideratio if additioal avigatioal iformatio is eeded, for example i cases of degraded GPS positioig accuracy. This paper presets a workflow of methods for the segmetatio of plaar surfaces i ALS data that ca be accomplished i lie with the data acquisitio process. Sice most of curretly used airbore laser scaers, like the RIEGL LMS-Q560, measure rage values i a patter of parallel sca lies, the aalysis of geometric features is performed directly o this sca lie data. Straight lie segmets are coected across cosecutive sca lies to result i plaar surfaces. I order to exploit the o-lie segmetatio results for aircraft avigatio, a previously geerated set of plaar surfaces characterizig the urba terrai is eeded for compariso (e.g. facades, rooftops). I our experimets, eve this iformatio origiated from ALS data, which were recorded uder optimal DGPS coditios, but it might as well be derived from ay other existig 3D city model. Withi our approach, we assume that INS avigatio is cotiuously available ad that we have a iitial guess of the sesor positio ad orietatio (±50 m, ±5 ). If we have to

2 avigate through GPS dropouts, the positioig accuracy will degrade because of INS drift effects, but we ca assume that the measured ALS data are still roughly aliged to the stored iformatio. I additio to their positio i 3D space, features like size ad ormal directio are assiged to all segmeted plaar patches, thus it is comparatively easy to fid correspodig objects i the database. We use this iformatio to achieve precise aligmet betwee the measured ALS data ad the existig city model, which fially eables us to correct the presumed sesor positio. 1.3 Related work I recet years, airbore laser scaig systems have bee explored by various scietists from differet poits of view. The complexity of ALS data acquisitio leads to a umber of potetial error sources. Schek (2001) ad Fili (2003) address this problem ad categorize differet iflueces that should be cosidered. I additio to varyig exactess of the avigatioal iformatio sources, several systematic effects ca lead to reduced poit positioig accuracy. Exemplary limitig factors are scaig precisio ad rage resolutio of the specific laser scaig device. Other egative effects ca be itroduced by iaccurate sychroizatio of the system compoets. Cosiderable discrepacies are caused by moutig errors or disregarded lever arms (displacemets betwee laser scaer, INS, ad GPS atea). Skaloud ad Lichti (2006) approached this problem with a rigorous method to estimate the system calibratio parameters such that 3D poits represetig a plae are coditioed to show best possible plaarity. I order to use ALS withi the scope of aircraft avigatio, we suppose that the sesor system has bee calibrated beforehad. Some procedures described i this paper are cocered with the segmetatio of poit clouds ito plaar surfaces. May differet methods regardig this topic ca be foud i literature. Some authors are iterested i detectig eve more kids of objects like spheres, cyliders, or coes. Rabbai et al. (2007) describe two methods for registratio of poit clouds, i which they fit models to the data by aalyzig least squares quality measures. Vosselma et al. (2004) use a 3D Hough trasform to recogize structures i poit clouds. Fili & Pfeifer (2006) propose a segmetatio method that is based o cluster aalysis i a feature space. The RANSAC algorithm (Fischler & Bolles, 1981) has several advatages to utilize i the shape extractio problem (Schabel et al., 2006). We apply a RANSAC-based robust estimatio techique to fit straight lie segmets to the sca lie data. I a similar maer, a modified versio of this method is used to idetify locally plaar patches i the model data. The amout of outliers lets us distiguish betwee buildigs ad irregularly shaped objects like trees. Fudametal ideas o fast segmetatio of rage data ito plaar regios based o sca lie aalysis have bee published by Jiag ad Buke (1994). Their algorithm divides each row of a rage image ito straight lie segmets which are combied i a regio growig process. Despite the fact that we are cosiderig cotiuously recorded sca lies istead of rage images, we basically follow this approach durig the o-lie data aalysis. Several existig cocepts of terrai-based avigatio for aerial vehicles ca be foud, e.g. image based avigatio (IBN), terrai-followig radar (TFR), or terrai cotour matchig (TERCOM). Other tha these methods, laser scaig is a comparatively ew techique. Toth et al. (2008) propose the use of LiDAR for terrai avigatio, as it provides distict 3D measuremets that ca easily be used for exact compariso to previously recorded data. I their cocept, the iterative-closestpoit algorithm (Besl & McKay, 1992) is chose for surface matchig. Istead of a ICP approach, we idetify matchig plaar objects with regard to several geometric features (i.e. positio, pricipal compoets, ormal directio). Similar methods have demostrated high performace for markerless TLS registratio (Breer et al., 2008). The problem of determiig the trasformatio parameters is trasferred to a system of liear equatios that ca be solved immediately. 2. EXPERIMENTAL SETUP Data used for this study were collected durig field campaigs i 2008 ad 2009, usig the equipmet that is briefly described i this sectio. A more thorough descriptio of the sesor system ca be foud i (Schatz, 2008). 2.1 Sesor carrier The sesors described below have bee attached to a helicopter of type Bell UH-1D (Figure 1). Laser scaer ad IMU are mouted o a commo sesor platform at the side of the helicopter, which ca be tilted to allow differet perspectives, i.e. adir or oblique view. I a operatioal system, the pilot must be able to react to upcomig dagers, e.g. durig degraded visibility coditios. Therefore, a obliquely forward-lookig sesor cofiguratio was used i our experimets. The lever arms of the compoets i the system are kow, ad the correct bore-sight agles have bee determied. Calibratio of these parameters is ot topic of this paper, suitable methods ca be foud i (Skaloud & Lichti, 2006). Roll Pitch Headig z IMU x y GPS LiDAR Height Sca agle α Figure 1. Sesor carrier ad sesor cofiguratio. 2.2 Laser Scaer Δl Δp North The RIEGL LMS-Q560 (2006) laser scaer makes use of the time-of-flight distace measuremet priciple with a pulse repetitio rate of 100 khz. Opto-mechaical beam scaig provides sigle sca lies, where each measured distace ca be georefereced accordig to positio ad orietatio of the sesor. Waveform aalysis ca cotribute itesity ad pulsewidth as additioal features, but sice we are maily iterested i fast acquisitio ad o-lie processig of rage measuremets, we eglect full waveform aalysis throughout this paper. Rage d uder sca agle α (Figure 1) is estimated correspodig to the first sigificat echo pulse as it ca be foud by costat fractio discrimiatio. Typically, each sca lie covers a field of view of 60 with 1000 rage measuremets (d,α) that ca be coverted to 2D Cartesia coordiates (Figure 6). Navigatioal data are sychroously assiged to these rage measuremets for direct georeferecig. East

3 y 2.3 Navigatioal sesor system The Applaix POS AV 410 comprises a GPS receiver ad a gyro-based iertial measuremet uit (IMU), which is the core elemet of the iertial avigatio system (INS). The GPS data are used for drift compesatio ad absolute georeferecig, whereas the IMU determies acceleratios with high precisio. These data are trasferred to the positio ad orietatio computig system (PCS), where they are fused by a Kalma filter, resultig i positio ad orietatio estimates for the sesor platform. I additio to the real-time avigatio solutio, specialized software ca be used for accurate postprocessig of the recorded avigatioal data. Applaix POSPac MMS icorporates the use of multiple DGPS referece statios ad the import of precise GPS ephemeris iformatio. We cosider this corrected avigatio solutio while geeratig a optimal database of the urba terrai. 3. USED METHODS AND DATA PROCESSING I this chapter, we distiguish two differet operatig modes of ALS data acquisitio ad processig. First, we assume that we have optimal settigs for creatio of a adequate database: the relevat urba area ca be scaed several times from multiple aspects with a calibrated sesor, ad data ca be processed ad optimized off-lie. Durig this stage, we ca resort to ow differetial GPS base statios or use accordig iformatio, e.g. provided by the Satellite Positioig Service of the Germa State Survey (SAPOS). Uder these coditios, the absolute measuremet accuracy of a ALS system is typically i the order of oe decimeter (Rieger, 2008). 3.1 Automatic geeratio of a adequate database The iteded utilizatio of LiDAR sesors for aircraft guidace does ot require a highly detailed GIS. We limit the creatio of a database to the extractio of plaar patches i multi-aspect ALS poit clouds of the relevat urba area. As metioed before, these data should be collected uder optimal coditios (Figure 2a). The combied complete 3D poit cloud cotais iformatio cocerig all facades ad rooftops of buildigs. A workflow of off-lie processig methods is used to filter poits ad extract most of the plaar objects. The respective flowchart is illustrated i Figure Multi-aspect ALS data sets INS/DGPS postprocessig Poit cloud k-d tree data structure Removal of groud poits 45 RANSAC-based plae extractio (a) Figure 2. Horizotal cross-sectio of a buildig i overlappig poit clouds: (a) after INS/DGPS postprocessig, (b) usig the real-time avigatio solutio. I the secod mode of ALS operatio, the data is used for olie avigatio updates durig helicopter missios. At this time, we expect o-differetial GPS coditios, GPS dropouts, ad loss of data poits due to smoke, fog, or other egative iflueces. Figure 2 shows the accuracy that ca be obtaied i the differet operatig modes. ALS data i this example were acquired at a skew agle of 45 degree (forward-lookig). The helicopter approached the same urba area from six differet directios, ad the resultig 3D poits were combied ito a sigle poit cloud. Both illustratios depict the aggregated data withi the horizotal cross-sectio of a buildig i the overlap area. Best accuracy as show i Figure 2a results from a global optimizatio of the avigatioal data with the Applaix postprocessig software POSPac. I this example, the data of six DGPS groud statios were take ito accout. Compared with this accuracy, discrepacies of several meters ca occur if the real-time avigatio solutio is used (Figure 2b). This situatio will eve get worse i case of GPS dropouts, depedig o the quality of the IMU. (b) Database: Plaar patches Features: positio, size, ormal directio, pricipal compoets x Figure 3. Flowchart of the model creatio. Mergig of several multi-aspect ALS data sets results i a irregularly distributed 3D poit cloud. We itroduce a k-d tree data structure to hadle automatic processig of these data. The search for earest eighbors ca be doe very efficietly by usig the tree properties to quickly elimiate large portios of the search space. The subsequet segmetatio method is iteded to keep oly those poits that are most promisig to represet parts of buildigs. At first, we remove all groud poits by applyig a regio growig techique i combiatio with a local aalysis of height values. We search for sectios of the poit cloud i which the histogram of height values clearly shows a multimodal distributio. There, laser poits at groud level appear as the lowest distict peak. Such positios are the used as seed poits for the regio growig procedure, which collects all poits fallig below a certai slope. The ecessary search operatios are accomplished by meas of the k-d tree data structure. I geeral, this method may misclassify some poits (e.g. ier courtyards), but this is egligible for our applicatio. A overview of advaced methods for bare-earth extractio ca be foud i (Sithole & Vosselma, 2004).

4 Sca y [m] Regio growig The mai step of the model creatio is the extractio of plaar features from the remaiig 3D poits. Remarkably, the applied segmetatio method is almost idetical to the algorithm used for detectio of straight lie segmets i the 2D sca lie data, except for the terms lie/plae ad the differet data structure. Similarly, geometric features of the extracted shapes have to be computed i both the model ad the o-lie results. These topics are described i sectios 3.2 ad 3.4, respectively. Figure 4 gives a impressio of the derived model data. The uderlyig poit cloud is composed of four partial scas of the terrai (differet aspects). Figure 4. Partial view of the database with groud level (blue), vegetatio (gree) ad plaar patches (red). 3.2 Sca lie aalysis of airbore LiDAR data Durig o-lie processig, the aalysis of geometric features is performed directly o the sca lie data. Most parts of typical buildigs will appear as local straight lie segmets i the 2D Cartesia represetatio, eve if the airbore laser scaer is used i oblique cofiguratio (Figure 1). The RANSAC techique is used to locally fit straight lie segmets to the sca lie data. As metioed i sectio 3.1, the algorithm described below ca be modified to accomplish segmetatio of plaar surfaces i a 3D poit cloud. This is simply doe by replacig the sca lie idex with the k-d tree data structure, ad by turig attetio to 3D plaes istead of 2D lies. A overview of the proposed method is show i Figure 5. Withi each ew sca lie, the 2D poits (with rage ad sca agle coverted to Cartesia coordiates) are first evaluated by a local pricipal compoet aalysis. This step lets us distiguish betwee regularly or irregularly distributed poit clusters. Withi each followig iteratio step, we radomly select sca lie positios of regularly distributed data poits ad try to fit a straight lie segmet to the eighborig data. The RANSAC techique provides a robust estimatio of the lie segmet s parameters. If the fitted straight lie is of poor quality, the data associated with the curret positio is assessed as clutter. Otherwise, we try to optimize the lie fittig by lookig for all data poits that support the previously obtaied lie, which is doe i steps (10), (11), ad (12). These steps actually represet a lie growig algorithm. The local fittig of a straight lie segmet is repeated with the supportig poits to get a more accurate result. The ed poits of each lie segmet ca be foud as the perpedicular feet of the two outermost iliers. Figure 6 shows detected straight lies for a exemplary sca lie, depicted with a suitable color-codig accordig to the ormal directio. (1) Perform a local pricipal compoet aalysis for the 2D data poits i the ew sca lie A. The respective smallest eigevalue idicates the local straightess of the data. (2) Choose a uprocessed positio i amog the available data i the array A where data poits were foud to be regularly distributed. (3) Check a sufficietly large iterval aroud this positio i for available data, resultig i a set S of 2D poits. (4) Set the couter k to zero. (5) If S cotais more tha a specific umber of poits, go to (6). Otherwise mark this positio i as discarded ad go to step (15). (6) Icrease the couter k by oe. (7) Perform a RANSAC-based straight lie fittig with the 2D poits i the specified set S. (8) If the umber of iliers is low, mark the curret positio i as discarded ad go to step (15). (9) Obtai the Hessia ormal form L: (x-p) 0 = 0 ad push the curret positio i o a stack (LIFO). (10) Pop the first elemet j off the stack. (11) Check each positio i a iterval aroud j, which has ot already bee looked at, whether the respective poit lies sufficietly ear to the straight lie L. If so, iclude the 2D poit i a ew set S. Additioally push its positio o the stack if idicated by (1). (12) While the stack is ot empty, go to (10). Otherwise cotiue with step (13). (13) If the couter k has reached its predefied maximum (e.g. two cycles), mark all positios of poits i S as processed ad determie the regressio lie to S. Store the perpedicular feet of the two outermost poits to defie the straight lie segmet ad go to step (15). Otherwise cotiue with (14). (14) Go to step (5) with the ew set of poits S:=S. (15) Repeat from (2) util all poits are classified. Figure 5. Procedure for RANSAC-based shape extractio (example: detectio of lies i a set of 2D poits) Sca x [m] Figure 6. Detected straight lie segmets i a typical sca lie. 3.3 Groupig of lie segmets The ed poits of each lie segmet are georefereced to result i correct positioed straight 3D lies. I this sectio, we describe a procedure to coect coplaar lie segmets of cosecutive sca lies. Let P i, P j, ad P k be three of the four ed poits of two lie segmets i differet sca lies. The distace of the fourth ed poit P m to the plae defied by the three others is a measure of coplaarity. We defie a distace d p as the sum of all four possible combiatios: d : p T pi pm pi p j pi pk 4 pi p j pi pk (3.1) The algorithm to fid correspodig lie segmets i a sequece of sca lies ca be summarized as follows:

5 (1) Select the ext lie segmet a i the curret sca lie. (2) Set the label of a to a ew ad icreasig labelig umber. (3) Successively compare lie segmet a to each lie segmet b of several previous sca lies. If Euclidea distaces, disparity of ormal directio, ad the measure of coplaarity d p are foud to be smaller tha predefied thresholds, go to step (4). Otherwise go to step (5). (4) Set the label of a to that of b. (5) Cotiue with (1) util all lie segmets a are processed. The above steps summarize the mai ideas of our method. I fact, we apply a exteded two-pass approach to improve detectio of coected compoets. More details o this topic ca be foud i (Hebel & Stilla, 2008). Figure 7 illustrates the procedure. First, each lie segmet is iitialized with a uique label. Coplaar lie segmets that are foud to lie ear to each other are liked together by labelig them with a commo labelig umber. This process is repeated util all ew lie segmets are labeled. Surfaces are represeted by the emergig clusters of lie segmets with the same label (Figure 8). Figure 7. Illustratio of sca lie groupig. eigevalue. The value of the smallest eigevalue λ 0 of the covariace matrix, divided by the umber of poits, is iflueced by the curvature ad the scatter of C. If it is ear zero, this idicates a plaar surface. The features used to idetify matchig surfaces i the model data ad the results of sca lie aalysis are: cetroid, ormal directio, ad the ormalized eigevalues of the covariace matrix. These features ca eve be used to classify ad remove irregularly shaped surfaces, e.g. the groud level i Figure Registratio of ALS ad model data Eve without cosiderig terrai-based avigatio, we assume that the sesor positio is kow approximately e.g. up to 50 meter. I case of GPS dropouts, the IMU drift will ot distort the positioig exactess dramatically. The relative accuracy provided by the INS measuremets still esures cosistet ALS measuremets over limited periods of time, depedig o the quality of the INS system. I some situatios, the absolute avigatioal accuracy eeds to be improved. Examples are lowaltitude flights of helicopters at ight or preparatio of ladig approaches durig rescue missios at urba areas. If the helicopter is equipped with a LiDAR sesor, ALS data ca be collected for several secods i order to sca the urba area i frot of the helicopter (Figure 8). Surfaces that are istataeously detected i these data ca be compared ad matched to the existig database of the terrai (Figure 4). The features that are used to establish liks have bee described i sectio 3.4. First, the displacemet of the cetroids has to fall below a maximum distace. Secod, the agle betwee the ormal directios should be small (e.g. <10 ). Third, the ormalized eigevalues of the covariace matrix C 0 t C 0 should be similar. Nevertheless, wrog assigmets may occur especially whe cosiderig a large search radius. I order to be robust agaist these perturbatios, the procedure described i this sectio is complemeted by a RANSAC scheme. Figure 9 illustrates a exemplary pair of associated surfaces. The offset i positio ad orietatio idicates the iaccuracy of the avigatioal data. M D c D Figure 8. Result of sca lie groupig for ALS data (5 secods). 3.4 Feature extractio Each cluster of coected straight lie segmets ca be characterized by a set of features which are described i this sectio. For a give cluster of coected lie segmets, let C deote the set of associated 3D data poits. The cetroid of C ca be computed as the sum of all poits divided by their umber, ad C ca be traslated towards the origi: 1 c = ci, C0 : = C - c (3.2) i= 1 The eigevectors of the covariace matrix C 0 t C 0 are the pricipal compoets of C. The ormal directio 0 is give as the ormalized eigevector that correspods to the smallest Figure 9. A pair of correspodig plaes i model M ad curretly acquired ALS data D. I this sectio, we determie a rigid trasformatio (R,t) to correct these discrepacies. Let E M deote the plaar surface of the model M that is associated with the plae E D i the curretly collected ALS data D. The respective Hessia ormal form of these plaes is give by the cetroids ad the ormal directios M, D (Figure 9). Sice both plaes should be idetical after registratio, the cetroid of E D should have zero distace to E M. Moreover, the two ormal vectors should be equivalet if they are ormalized to the same half space. I additio to these coditios, we ca assume that errors of orietatio will ot exceed the rage of ±5. That eables us to liearize the equatios:

6 R + - = 3 2 cd t cm M 0, R D M R = 1 (3.3) The rotatio agles (α 1, α 2, α 3 ) ad the traslatio compoets (t 1, t 2, t 3 ) are the six variables to be determied. Each correspodig pair of plaes E D, E M yields two liear equatios (3.3), therefore at least three pairs have to be idetified i the data to compute the rigid trasformatio (R,t). I geeral, more correspodeces ca be foud at urba areas. The resultig overdetermied system ca be solved approximately by ivertig the ormal equatios. I additio, the area of the plaar patches ca be used as a weightig factor. Fially, the corrected positio of the sesor i the model coordiate system is give as R p GPS +t ad the orietatio is corrected to R R IMU. 4. EXPERIMENTS We tested the proposed methods o the basis of real sesor data which were recorded 300 meters above Abeberg, Germay. Data available from four flights over this urba terrai i 2008 led to the database show i Figure 4. Data collected i 2009 were cosidered to prove the cocept of terrai based avigatio (Figure 8). For this purpose, 8000 radomly chose displacemet vectors i the rage [5 m, 50 m] were added to the exact sesor positio ad it has bee checked if these offsets are corrected automatically. Figure 10 shows the average displacemet betwee calculated ad exact sesor positio agaist the umber of matchig pairs of plaes. With our data, we were able to reduce the average offset i sesor positio to 0.6 m if at least 10 pairs of associated surfaces ca be foud (stadard deviatio: 0.2 m). Whe cosiderig a larger umber of matchig plaes (50), this result was eve improved to 0.4 m. These umbers most likely deped o additioal coditios, e.g. aircraft altitude, aircraft speed, umber ad orietatio of facades ad rooftops. Figure 10. Average displacemet agaist umber of plaes. 5. CONCLUSION AND FUTURE WORK The examples preseted i this paper were obtaied with a experimetal sesor system, for which data aalysis ca oly be doe offlie to show the feasibility of the proposed approach. Nevertheless, we guess that all computatios ca be accomplished i real-time, with a efficiet implemetatio ad appropriate hardware. I our experimets, we were able to alig the model ad the ALS data such that matchig objects show a average distace of 8 cm after the registratio. This absolute exactess is ot ecessarily trasferable to the sesor positio (see Sectio 4). With a larger distace betwee helicopter ad the terrai, impreciseess of the sesor orietatio has a cosiderably higher impact o the overall displacemet. For example, a agular error of 0.1 would lead to a shift of 1 m i a distace of 600 m. The absolute exactess of the estimated sesor positio improves sigificatly whe cosiderig larger areas ad/or shorter rages, e.g. whe approachig the terrai at low altitude. 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