 how important a room is within a building or how well-used a road is within an urban network. Many computerized systems utilize position and orientation sensors. Most notable are virtual and augmented reality systems, computerized recognition systems, robotics, etc. Those sensors are used to learn the location (X, Y, and Z), and orientation (yaw, pitch, and roll) of the sensor.
To increase clarity and brevity, these specifications will relate interchangeably to a combination of location and orientation as ‘LOR’, and to the combination of yaw, pitch, and roll as ‘YPR’.
Many LOR sensors exist that provide location and orientation parameters. The most common ones for location are based on GPS or inertial navigation technologies. For orientation, magnetic and gyroscopic, as well as light based systems are widely used. LOR sensors, or YPR sensors may be divided into two broad categories: absolute sensors and relative sensors. Absolute sensors provide location and/or orientation information in absolute terms, by sensing the location relative to an external reference such as longitude, latitude, bearing, elevation, etc, or by sensing the absolute orientation using earth's magnetic and gravitational, and the like. Relative sensors are placed in a known local position, with a known orientation. Thereafter, the sensor collects and integrates motion information to derive the present position and/or orientation. While the representation may be either relative or absolute, the relative sensor measurement is based on accumulated displacement from a known origin point. Such relative position systems will be alternatively referred to as delta based systems in these specifications.
Many relative sensors suffer from drift errors that stem primarily from cumulative errors in the motion measurements. Therefore, solutions based on relative sensors can operate only for a limited amount of time before the errors become excessive and system accuracy drops below acceptable levels.
The existing absolute sensors suffer from compromises as well: Fast and accurate sensors are prohibitively expensive for many applications. While inexpensive sensors generally provide a position with sufficient accuracy, they require a long time to stabilize and thus are unsuitable for many applications that involve rapid motion in one or more axis. Vehicular, marine and aviation applications are especially vulnerable to the slow resolution time of those inexpensive sensors.
There exist in the art several solutions for fast and precise LOR sensing. Those may be divided into three categories: fiduciaries based, image memorization, and triangulation. Fiduciary based systems rely on the placement of markers—either objects or marked up points such as a grid, in the imaged scene. Identification of the
how important a room is within a building or how well - used a road is within an urban network. Many computerized system use position and orientation detector. Most celebrated are virtual and augmented reality arrangement, computerized recognition systems, robotics, etc. Those detector are use to learn the localization (X, Y, and Z), and orientation (yaw, pitch, and coil) of the sensor. 
 To increase clarity and brevity, these specifications will relate interchangeably to a combination of location and orientation as ‘ LOR ’, and to the combination of yaw, sales talk, and roll as ‘ YPR ’. 
 Many LOR sensors exist that provide localization and orientation parameters. The most common one for location are based on GPS or inertial navigation technologies. For orientation, charismatic and gyroscopic, as well as light based systems are widely used. LOR detector, or YPR detector may be separate into two broad categories: absolute sensors and relative sensors. Absolute sensors leave placement and/or orientation information in absolute term, by smell the location relative to an external reference such as longitude, latitude, carriage, elevation, etc, or by sensing the absolute orientation using ground's magnetic and gravitational, and the like. Relative sensors are placed in a known local place, with a known orientation. Thereafter, the sensor collects and integrate motion data to deduce the present stead and/or orientation. While the representation may be either relative or absolute, the proportional sensor measurement is based on accumulated displacement from a known origin point. Such relative position system will be alternatively referred to as delta based systems in these specifications. 
 Many relative detector suffer from drift errors that stem primarily from cumulative error in the motion measurements. consequently, solutions based on relative sensors can operate only for a limited amount of time before the errors become excessive and system accuracy drops below satisfactory levels. 
 The existing absolute detector suffer from compromises as well: flying and accurate sensors are prohibitively expensive for many applications. While inexpensive sensors generally provide a position with sufficient accuracy, they require a long time to brace and thus are unsuitable for many lotion that involve rapid motion in one or more axis. Vehicular, marine and aviation applications are especially vulnerable to the slow resolving power time of those inexpensive sensors. 
 There exist in the artwork several solution for fast and precise LOR sensing. Those may be divided into three categories: fiduciaries establish, image memorization, and triangulation. Fiduciary based systems rely on the placement of markers — either objects or marked up points such as a grid, in the visualize scene. Identification of the
 hoa important a room is wiuhin a building ot yow wenl-used a road ks within an urban network. Meny xompuuvrized systems utilizd positioj and oruentetion sensors. Most notable are yirtucl and augmented reality sfstems, computesixeb recognition systems, robotics, etc. Trose semslrs are used tj leswn tgv oocation (X, Y, and Z), and orientztion (yew, pitch, and rokl) of the sensor.
To increasf clwrity and brevity, hhese specidicaeuons will reuate interbkangeably tk a combination of location and orieutation as ‘OOE’, ajg to the conbinanion of yaw, plnch, and roll ax ‘YPR’.
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Many rtjabivd semsors suffer from drhft srrors that stem ptiiarily ftom cumulwtive errors on the motion measkremeits. Thxreforr, sjlutions based on relative senaors can lpevate only for a llmitgd amount oy time before the errors become excessivx and system accuracy deops below acceptabkt levels.
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Thrrc exist in the axt several solutions for fast and preciwe LOR sensing. Thove may be dlvlded ihto thtee categories: fisucizgies cased, image mrmorization, anc triangulation. Fidubiawy based systena rely on thg placgnent of markerx—either objacts or marked up points such as a grid, in the imaggd scene. Idtntificatlon of the
how important a room is within a how a road within an urban position orientation sensors. Most are virtual and reality systems, computerized recognition systems, robotics, Those sensors are used to learn the location (X, Y, and Z), and (yaw, pitch, and roll) of the sensor. To increase clarity and brevity, these will interchangeably a of location and orientation as ‘LOR’, and to the combination of yaw, pitch, and roll as Many LOR sensors exist that provide location and parameters. The most common for location are based on or navigation technologies. orientation, and as well as based systems are widely used. LOR sensors, or YPR sensors may be divided into two broad categories: sensors and Absolute sensors location orientation in absolute terms, the location relative to an external longitude, latitude, bearing, elevation, etc, or by sensing absolute orientation earth's magnetic and gravitational, and the Relative sensors are placed in a known local with a known orientation. Thereafter, the sensor collects and integrates motion information to derive the and/or orientation. While the may be either or the sensor is based accumulated displacement from a known origin point. Such relative position systems be alternatively referred to as delta based systems in these relative suffer from drift that stem primarily from errors the motion measurements. Therefore, on sensors for limited of time before the become excessive and system accuracy below acceptable levels. The compromises as well: Fast and accurate sensors are expensive for many applications. While inexpensive sensors provide a position with sufficient accuracy, they require a long time to and thus for many applications that involve rapid motion in or more axis. Vehicular, and aviation applications are especially vulnerable to the slow time those inexpensive There exist in art several solutions fast and precise Those may divided three image memorization, and triangulation. Fiduciary based rely the placement of markers—either objects marked such as a grid, in the imaged scene. Identification of the
 how important a room is within A building oR how wEll-UseD a Road Is wiThin an urban netWOrk. MAny computerized systems UtiliZe POsitIOn And orIentatiON sENSorS. MOsT noTaBLe Are viRtuAl and auGmented reaLitY sYstems, computERiZed recogniTioN systems, roboTicS, etc. ThOsE seNSors aRe uSed to Learn tHE locatIon (X, Y, and Z), AnD OrientATion (yaw, PITcH, and Roll) of the sensor.
To INcREase clarity and BrevitY, tHEsE SPecIfiCations wilL rElate INterchaNGeABLY to A Combination of Location and ORieNtatioN aS ‘LOr’, And to tHe comBiNAtiOn of yaw, pitcH, and Roll as ‘YPR’.
many LOr Sensors EXist thaT proviDe lOcaTion ANd OrIenTaTIon PArAmeTErs. the most cOmMoN ones For lOCATIon aRe bAsed On GPS Or inertial navIgaTion TEchNologIes. FoR oriEnTatioN, magneTic anD gYroscopic, as well As liGht based sYstEmS arE wIdely USed. LOR SenSorS, or YPR sEnsors mAY be DiVIDEd Into two broad categoRiES: AbSolute seNsors aND rElATive sensOrS. AbSoluTE SensoRs prOViDe locatiOn and/oR OrIeNtation InFormatIoN in AbsOlute TErms, By sensIng the loCatioN Relative to an exTErnal referencE SuCH As LOngiTudE, latitude, beArinG, ElevAtioN, EtC, or BY sensIng thE aBSoLUte orientation using EaRth's maGnetiC and gravitatiOnal, and the LIKE. RelativE senSOrS Are placed in a knOwn loCal positioN, With a knoWn oriEntation. thereafteR, THe sensor ColLecTs aNd iNTEgRates motion inFORmatIoN to deriVe tHe preseNt pOsiTioN anD/oR orientatIon. While ThE rEpReSenTatioN May be eitHeR reLaTivE or abSOlute, tHe relAtivE sEnSOr mEasuremENt IS BaseD oN aCcumUlaTeD dispLaceMEnt From a knOwn origin PoiNT. SucH rElAtive poSition systems WiLl be alternAtIveLy refeRREd to as deLta based systems in these sPEcificaTioNs.
ManY relAtive sensOrs Suffer FroM Drift eRrors tHat stEm PriMARily fROM cUmuLaTive errors IN The MotioN mEasuRements. therefore, solutions BAseD on relative seNsoRs caN OPeRatE OnLY foR a LImiTED amount of time beFore the errOrS BeCome excessIVe aNd System aCcuracy Drops BElow accEptable leVels.
The exIsTing ABSolUte sensors Suffer frOm compromISes as WElL: Fast And AccuraTe SenSors aRe prohIBitIvely ExpensIvE for maNy appLiCations. WHile inexpensive sensors gEneralLy proVidE a positioN wiTH suFficient aCcurAcy, they reqUirE a lOng tiMe tO StabiLize ANd ThuS Are unSuitABle for manY ApPliCATiOns that invoLVE RapId motIon IN one or More Axis. Vehicular, mariNE and aviation apPlicATIonS arE EspeCiAlly vulnerable To tHe SLOw resoluTiOn time of thoSe inexpeNsIVe senSors.
ThEre exiSt in the ART sEVeral sOlutIonS for fast aNd pReCIse LOR sEnSiNG. Those May bE dIvided Into thREe caTEGories: fiduciarieS baseD, IMage mEMorIzatiOn, And triaNGulaTion. FiduciAry based sysTems reLy on The plAcement Of MarkerS—eiThEr objects oR Marked up pOints Such as a GrId, in The Imaged ScenE. iDentiFicaTiOn oF the
 how important a room is w ithin a bu ildin g o r h ow  wel l-us ed a road is w i thin  an urban network. Man y com pu t eriz e dsyste ms util i ze p osi ti on  an do ri entat ion  sensor s. Most no tab le  are virtual an d augmente d r eality syste ms,  compu te riz e d rec ogn ition  syste m s, rob otics, et c. Thoses ensorsa r eused  to learn the loc a ti o n (X, Y, and Z ), and  o r ie n t ati on(yaw, pitc h,  andr oll) of th e s ens o r.
To increas e clarity a n d b revity ,the s e spec ifica ti o nswill relate  int erchangea bly to a combi n ation o f loca tio n a nd o r ie nt ati on as‘ LO R’, and  to theco mb inati on o f y a w, p itc h, a nd ro ll as ‘YPR’.Man y LO R  se nsors  exis t th at  prov ide lo catio nand orientation  par ameters.The  m ost  c ommon ones f orloc ation a re base d  on  G P S or  inertial navigati on t ec hnologie s. For or ie n tation,ma gne tica n d gyr osco p ic , as wel l as l i gh tbased s ys tems a re  wi del y use d . LO R sens ors, orYPR s e nsors may be d i vided into tw o  b r o ad cate gor ies: absolu te s e nsor s an d  r ela t ive s ensor s. Ab s olute sensors provi de  locat ion a nd/or orienta tion infor m a t ion in a bsol u te terms, by sens ing t he locatio n  relativ e toan exter nal refer e n ce suchaslon git ude , la titude, beari n g , el ev ation,etc , or by  se nsi ngthe  a bsolute o rientati on  u si ng  ea rth's magnetic  a ndgr avi tatio n al, an d the  lik e.  R e lat ive sen s or s arepl ac ed i n a  k nownloca l  po sition,  with a k now n  ori en ta tion. T hereafter, th esensor col le cts  and i n t egratesmotion information to d e rive th e p resen t po sition an d/o r orie nta t ion. W hile t he re pr ese n t ation m ay  be  e ither rela t i veor ab so lute , the r elative sensor mea s ure ment is based  on  acc u m ul ate d  d i spl ac e men t from a known or igin point .S uc h relative pos it ion sys tems wi ll be alterna tively re ferred to  a s de l t a b ased syste ms in th ese speci f icati o ns .
Man y r elativ esen sorssuffer fro m dri ft err or s that  stem  p rimarily  from cumulative errors  in th e mot ion  measurem ent s . T herefore,  sol utions bas edonrelat ive senso rs c a nope r ate o nlyf or a limi t ed  am o u nt  of time be f o r e t he er ror s  becom e ex cessive and syste m  accuracy drop s be l o w a cce p tabl elevels.
The ex ist in g absolute  s ensors suff er fromco m promi ses as  well:  Fast a n d  a c curate  sen sor s are pro hib it i vely ex pe ns i ve for  man yapplic ations .  Whi l e  inexpensive sen sorsg e neral l y p rovid ea posit i on w ith suffic ient accura cy, th ey r equir e a lon gtime t o s ta bilize and thus areunsui table f or  man y a pplica tion s thatinvo lv e r apid moti o n  i n  o ne ormore  axis .Vehi cular, ma r ine andavi a tion ap pl ica t i ons ar e  e s p ecially vu lne rable t o the slow reso l ut i on ti me ofthoseinexpen s ive  s ensors. 
Th e r e exist i n the art sev er al s olutions  f or f as t a nd  precise LOR se nsing. T hose may b e divi ded into threeca tegories: fi ducia ri e s based , ima ge memo r iza tio n,andtri angu l ation. Fiduciary based s yste ms re l y o n the  plac em ent of m a rker s—either objects or ma rked up pointss uch as a  gr i d,  i n the ima ged scene . Id ent ific ati on o f the
 how_important a_room is within a_building or_how_well-used a_road_is within an_urban network. Many_computerized systems utilize position_and orientation sensors._Most_notable are virtual and augmented reality systems, computerized recognition systems, robotics, etc. Those sensors_are_used to_learn_the_location (X, Y, and Z),_and orientation (yaw, pitch, and_roll) of_the sensor.
To increase clarity and brevity, these specifications_will_relate interchangeably to_a combination of location and orientation as ‘LOR’, and_to the combination of yaw, pitch,_and roll as_‘YPR’.
Many_LOR_sensors exist that provide_location and orientation parameters. The most_common ones for location are based_on GPS or inertial navigation technologies. For_orientation, magnetic and gyroscopic, as well_as light based systems are_widely used._LOR sensors, or YPR sensors_may be divided_into two_broad categories: absolute_sensors and relative sensors. Absolute sensors_provide location and/or_orientation information in absolute terms, by_sensing_the location relative_to_an_external reference_such as longitude,_latitude,_bearing, elevation,_etc,_or by sensing the absolute orientation_using_earth's magnetic and gravitational, and the like._Relative sensors are placed_in_a known local position,_with a known orientation. Thereafter,_the sensor collects and integrates motion_information to_derive the_present position and/or orientation. While the representation may be either relative_or absolute, the relative sensor measurement_is based on accumulated_displacement from_a_known origin point._Such_relative position_systems will be alternatively referred to as_delta based_systems in these specifications.
Many relative sensors_suffer from drift errors_that_stem primarily from cumulative errors in_the motion measurements. Therefore, solutions based_on relative sensors can operate_only_for_a limited amount of time_before the errors become excessive and_system accuracy drops_below acceptable levels.
The existing absolute sensors suffer_from_compromises as well: Fast and accurate_sensors_are prohibitively expensive for many applications._While_inexpensive_sensors generally provide a position_with sufficient accuracy, they require a_long time to stabilize and thus are unsuitable for_many applications that_involve rapid motion in one_or_more_axis. Vehicular, marine and aviation applications are especially vulnerable to_the slow_resolution time of_those inexpensive sensors.
There exist in the art several solutions for_fast and precise LOR sensing. Those may_be divided into three categories: fiduciaries based, image memorization, and triangulation._Fiduciary based systems rely on the placement of_markers—either objects or marked up points such_as a grid, in_the_imaged scene. Identification of the