Reducing geospatial uncertainty using reference measurements

Abstract

Systems and methods for minimizing uncertainty in geospatial location measurements. First and second position data of the first reference marker are received, each position data including an uncertain radius with respect to the preliminary coordinates. A first translation vector including a displacement from the second reference mark to the first reference mark is received. The second uncertainty radius is translated by a first translation vector, and the first uncertainty radius is reduced to an intersection region of the translated second uncertainty radius and the first uncertainty radius based on the intersection region.

Description

Technical Field

[0001] The present disclosure relates to systems for determining geospatial positions and, more particularly, to systems and related processes for iteratively reducing uncertainty in geospatial uncertainty using virtual reference measurements. Background Art

[0002] The specific geospatial location (e.g., longitude and latitude) received and returned by consumer devices is inherently inaccurate, typically with a resolution of a few meters of the device's exact (e.g., true) geospatial location in the case of a global positioning system (GPS). Although physical location can be fairly accurately described on a map in terms of geospatial coordinates (e.g., longitude, latitude, altitude), the exact location of the device or user relative to the world and the map is uncertain due to the granularity of measurement (e.g., meters). Typically, a location is usually given a preliminary coordinate due to the uncertainty. Therefore, querying a device for the exact location of the device or something in the device's field of view at a resolution appropriate for the device is inherently inaccurate. Summary of the invention

[0003] In view of the foregoing, the present disclosure provides systems and associated methods for minimizing uncertainty in location measurements (e.g., GPS, ultra-wideband, GLONASS, Wi-Fi) by reference and trusting reference measurements (e.g., measurements performed locally, using distance and direction applications or algorithms, such as simultaneous learning and mapping algorithms). Once the precise displacement between at least two reference markers is known, the uncertainty of the geospatial coordinates can be iteratively reduced by eliminating areas where candidate locations for the true location of the markers cannot be, which is achieved by converting any of the uncertainty radii to another using the measured displacement between the two reference markers, as will be described and explained in more detail below.

[0004] In a first method, a method for minimizing uncertainty in geospatial position measurement is provided. The method includes receiving first and second position data of a first reference marker, the first and second position data including a second uncertainty radius about a second preliminary coordinate. For example, a user scans a reference marker such as a QR code on a user device (two reference markers in this example, but 1+N is possible), or taps an NFC tag, and receives a GPS position with a radius of certainty for each of the reference markers. A first translation vector including a displacement from the second reference marker to the first reference marker may be received, calculated, or measured. For example, using an augmented reality measurement tool that utilizes built-in SLAM functionality and an accelerometer of the user device, the displacement (i.e., distance and direction) from the second reference marker to the first reference marker may be measured or calculated, and then received for further use in the method. Knowing the translation vector including the displacement between the reference markers, the minimum uncertainty radius, i.e., the second uncertainty radius, is translated by the first translation vector. In some examples, the translation is performed from any point within the second uncertainty radius. The intersection between the first uncertainty radius and the overlapping uncertainty radius of the translated second uncertainty radius replaces the first uncertainty radius, so the first uncertainty radius is reduced to the intersection area, and the uncertainty in the geospatial measurement has been reduced. The intersection can be referred to as a reduced radius, etc., and the term "reduced radius" is used in this context as a label for any convex shape or substantially convex shape.

[0005] In some examples, the method further includes iteratively repeating the translation and reduction of the uncertainty radius. For example, the newly reduced radius (which may actually be an irregular shape or volume) may now be translated (translated, or "converted") to the second preliminary coordinate, and so on, until the deterministic radius of both the first and second position data is minimized. In some examples, the method further includes stopping the iterative process when the first radius reaches a threshold. In general (i.e., more than two measurements), if sufficient uncertainty radii have been obtained, the threshold may be before all possible translations and intersections are calculated for N intersections from N+1 measurements. Therefore, in some examples, the threshold may be based on the ideal uncertainty radius size, after the total number of iterations (e.g., 1 iteration per N measurements, or a total of 10 iterations), after the timer expires, and / or when the resolution of the uncertainty (e.g., the uncertainty radius) is of the same order of magnitude as the user device. For example, when the radius of uncertainty is less than 10 mm, the iterative process may stop after 20 iterations, 5 seconds, or at the size of the user device. It should be understood that these examples are given to illustrate the configuration of the threshold of the iterative process. Note that for large sample sizes, e.g. 100 measurements, it is possible that after 99 intersections of the uncertainty radius, only an empty set remains, since some measurements are contradictory, specifically the uncertainty radius is only an indication, and for one of the measurements is wrong, which invalidates the intersection. Thus, such erroneous samples can be identified by repeating different combinations of intersections less than N and stopping the process when an appropriate uncertainty radius (i.e., resolution) has been reached.

[0006] In some examples, the method further includes photographing the first reference marker and the second reference marker by a camera on the user device. The method may also include storing the first position data and the second position data in a temporary local coordinate space stored on the user device. In order to measure the displacement between the two or more reference markers (i.e., a three-dimensional vector including both distance and direction), the position of the reference marker is converted to a local coordinate system (e.g., a virtual coordinate space) often referred to as a new spatial coordinate system in this article. Therefore, the displacement between the two reference markers and the translation vector between the two reference markers are equivalent terms. In some examples, the method includes executing a simultaneous learning and mapping (SLAM) algorithm on the user device to obtain the displacement (e.g., absolute distance and direction) between the first reference marker and the second marker in the local coordinate space.

[0007] In some examples, the method further includes receiving third position data of a third reference marker, the third position data including a third uncertainty radius about the third preliminary coordinate. The method may further include receiving, measuring or calculating a second translation vector including a displacement from the first reference marker to the third reference marker. The method may further include translating the reduced first uncertainty radius by the second translation vector; and based on the intersection area of ​​the translated reduced first uncertainty radius and the third uncertainty radius, reducing the third uncertainty radius to the intersection area.

[0008] In some examples, the method also includes photographing the first reference marker and the third reference marker by a camera on the user device; storing the first position data and the third position data in a temporary local coordinate system stored on the user device; and performing a simultaneous learning and mapping SLAM algorithm on the user device to obtain an absolute distance between the first marker and the third marker in the local coordinate system, and then obtaining a displacement from the first marker to the third marker in the case of a known orientation (e.g., a virtual coordinate space stored locally on the user device, which has a known orientation relative to the global coordinate system). For example, a scanning device in communication with the first user device can be used to scan reference markers or additional reference markers; such a device can be mounted on a car, tricycle, boat, snowmobile, and underwater device, as well as on a backpack carried on foot.

[0009] In some examples, the user device is a second user device different from the first user device. In some examples, the first and second marks are the same mark recorded by the same user device at two different time points. In this way, the translation vector can be 0, but the intersection area between the two uncertainty radii measured at the first time T1 and the second time T2 can intersect to reduce the uncertainty of the mark position.

[0010] In another method, a media device is provided, including a control module, a transceiver module and a network module, configured to: receive first position data of a first reference marker, the first position data including a first uncertainty radius about a first preliminary coordinate; receive second position data of a second reference marker, the second position data including a second uncertainty radius about the second preliminary coordinate; receive a first translation vector, the first translation vector including a displacement from the second reference marker to the first reference marker; translate the second uncertainty radius by the first translation vector; and based on an intersection area of ​​the translated second uncertainty radius and the first uncertainty radius, reduce the first uncertainty radius to the intersection area.

[0011] In another method, a non-temporary computer-readable medium is provided, having instructions recorded thereon, which, when executed, perform a method for minimizing the uncertainty of position measurement, the method comprising: receiving first position data of a first reference marker, the first position data comprising a first uncertainty radius about a first preliminary coordinate; receiving second position data of a second reference marker, the second position data comprising a second uncertainty radius about the second preliminary coordinate; receiving a first translation vector, the first translation vector comprising a displacement from the second reference marker to the first reference marker; translating the second uncertainty radius by the first translation vector; and reducing the first uncertainty radius to an intersection area of ​​the translated second uncertainty radius and the first uncertainty radius based on the intersection area of ​​the translated second uncertainty radius and the first uncertainty radius.

[0012] In another method, a system for minimizing the uncertainty of position measurement is provided, the system comprising: a device for receiving first position data of a first reference marker, the first position data including a first uncertainty radius about a first preliminary coordinate; a device for receiving second position data of a second reference marker, the second position data including a second uncertainty radius about the second preliminary coordinate; a device for receiving a first translation vector between the first reference marker and the second reference marker; a device for translating the second uncertainty radius by the first translation vector; and a device for reducing the first uncertainty radius to an intersection area based on an intersection area of ​​the translated second uncertainty radius and the first uncertainty radius. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The above and other objects and advantages of the present disclosure will be apparent upon consideration of the following detailed description in conjunction with the accompanying drawings, in which:

[0014] Figure 1 is an illustrative diagram of a mapping application utilizing GPS technology to locate a user according to some embodiments of the present disclosure;

[0015] Figure 2 is an illustrative diagram of reference markers in a locality according to some embodiments of the present disclosure;

[0016] Figure 3 is an illustrative diagram of geospatial uncertainty of reference markers according to some embodiments of the present disclosure;

[0017] Figure 4A and 4B is an exemplary illustration of how position and translation vectors between reference points in a new spatial environment can be generated according to some embodiments of the present disclosure;

[0018] Figure 5 and 6is an illustrative diagram showing the translation of an error radius according to some embodiments of the present disclosure;

[0019] Figures 7 to 12 is an exemplary diagram of determining an error radius, a translation error radius, and an iterative process to achieve minimized geospatial uncertainty according to some embodiments of the present disclosure;

[0020] Fig.13 is an illustrative flow chart of a process for minimizing geospatial uncertainty of a location according to some embodiments of the present disclosure;

[0021] Fig.14 is an illustrative topology of devices (or computing configurations) programmed and configured for navigating media content according to some examples of the present disclosure; and

[0022] Fig.15 An exemplary heads-up display device according to some examples of the present disclosure is illustrated. DETAILED DESCRIPTION

[0023] As briefly described above, geospatial uncertainty in current GPS technology can be minimized by receiving first and second position data for a first reference marker, each of the first and second position data including an uncertainty radius about a preliminary coordinate. A first translation vector including a displacement from the second reference marker to the first reference marker is received. The second uncertainty radius is translated by the first translation vector, and based on the intersection of the translated second uncertainty radius and the first uncertainty radius, the first uncertainty radius is reduced to an intersection area between the first and second uncertainty radii. This modality can be implemented on, for example, a GPS device.

[0024] GPS technology has become widely available and is now commonly used by user devices such as smart phones. For example, a GPS receiver in a user device calculates its four-dimensional position in space and time based on data received from multiple GPS satellites. Each satellite carries an accurate record of its position and time and sends this data to the receiver. Satellites carry very stable atomic clocks that are synchronized with each other and with ground clocks. Any drift in the time kept from the ground is corrected every day. In the same way, the satellite position is known with great accuracy. The GPS receiver located in the user device also has a clock, but they are less stable and less accurate, resulting in uncertainty in the measurement. Since the speed of radio waves is constant and independent of the satellite speed, the time delay between the satellite transmitting the signal and the receiver receiving the signal is proportional to the distance from the satellite to the receiver. At least, four satellites must be within the field of view of the receiver so that it calculates four unknown quantities (three position coordinates and the deviation of its own clock from the satellite time). However, the uncertainty and resolution of GPS are limited by the capabilities of the user device. Therefore, it is desirable to have a method to improve the uncertainty of GPS position in other ways. GPS technology is not the only common geospatial location data that is becoming or has become widely available; Ultra-Wideband (UWB), home-based mesh Wi-Fi, etc. may also be the basis for location measurements that require improved uncertainty.

[0025] Figure 1 is an illustrative diagram of a mapping application utilizing GPS technology to locate a user 110 in accordance with some embodiments of the present disclosure. The user 110 is located at an "X" on a map of the mapping application on a smart phone device (e.g., a user device). The user 110 has initiated a "locate me" or similar feature of the mapping application to find their location on the mapping application's map. The mapping application has returned preliminary geospatial coordinates 120 with an uncertainty radius 130. In some examples, the mapping application also returns a field of view 140 of the user 110, or more specifically, a field of view 140 of the user's device.

[0026] Geospatial data is information describing objects, events or other features with locations on or near the surface of the earth. Geospatial data typically combines location information (usually coordinates on the earth) and attribute information (characteristics of the objects, events or phenomena involved) with time information (time or life time range of the location and attributes). The location provided can be static (e.g., the location of a piece of equipment, a seismic event, a child living in poverty) or dynamic (e.g., the spread of moving vehicles or pedestrians, infectious diseases) in the short term. Geospatial data typically involves a large collection of spatial data collected from many different sources in a varying format, and can include information such as census data, satellite imagery, weather data, cell phone data, drawn images and social media data. Geospatial data is most useful when it can be discovered, shared, analyzed and used in conjunction with traditional business data. Geospatial data and / or geographic data and information are defined in the ISO / TC 211 series of standards as data and information that are implicitly or explicitly associated with a location (geographic location or geographic location) relative to the earth. It is also called geospatial data and information, georeferenced data and information, and geographic data and geographic information. About 90% of data sourced from governments has a location component.

[0027] like Figure 1 As shown, at 110, preliminary coordinates 120 are not centered on the actual user's location. This is due to the lack of precision in user devices that utilize geospatial positioning technology. GPS coordinates received and returned by consumer devices are inherently inaccurate, typically only able to place the device within a few meters of a precise geospatial location. Although the physical location can be accurately described in terms of geospatial coordinates on a map (e.g., longitude, latitude, altitude), the location of the user device relative to the world and the map remains uncertain. The present disclosure includes details about querying the exact location of a device or a reference marker scanned by a device and reducing the uncertainty of returned queries. As well as Figure 1 As shown, the device returns a preliminary coordinate and an uncertainty radius, and finds the exact position somewhere within the circle represented by the product of the uncertainty radius and the preliminary coordinate.

[0028] Figure 2 is an illustrative diagram of reference marks in regions according to some embodiments of the present disclosure. Figure 2 Two reference markers are shown, reference marker A and reference marker B. Each reference marker displays unique coded information that is obtainable (e.g., machine detectable or machine readable). For example, the reference marker can be considered a machine readable label or the like. For example, the machine readable label can be a two-dimensional code, such as a QR code. In some examples, the reference marker can be a near field communication NFC tag that a user device can query to receive reference marker data.

[0029] The reference marker also triggers the user device to determine the current geospatial location of the user device (e.g., the current location and orientation of the user device). For example, placing two markers (A and B, such as Figure 2 In a scenario where a user device is positioned on a screen (as shown in FIG. 1 ), these markers, when read by a user device, will have geospatial coordinates with varying degrees of uncertainty. The user device is configured to obtain known physical dimensions and appearance of a reference object. As in “known physical dimensions and appearance”, the term “known” should be understood as the physical dimensions and appearance of the reference markers that are predetermined. In other words, the reference object has a pre-known physical dimension and appearance so that it can be compared with the apparent dimension and appearance of the imaged reference object.

[0030] Figure 3 is an illustrative diagram of a user's geospatial uncertainty when scanning a reference marker according to some embodiments of the present disclosure. Figure 3 The geospatial locations and uncertainties of markers A and B are shown in FIG. Each marker has a geospatial location represented as a tuple of preliminary coordinates (pn) and uncertainty radius (rn), where n in this example is either A or B. After scanning the markers, all that is known is that each marker is somewhere in the region created by using the uncertainty radius and preliminary coordinates. For example, marker A is within region oA created by preliminary coordinates pA and uncertainty radius rA.

[0031] Figure 4A and 4B is an exemplary illustration of how position and translation vectors between reference points in a new spatial environment can be generated according to some embodiments of the present disclosure. Figure 4A and 4B Shown in FIG. 1 are an origin 100 , a reference point A, a reference point B, a reference point C, and a plurality of position vectors.

[0032] In geometry, a position or position vector, also called a place vector or radius vector, is a Euclidean vector representing the position of a point P in space relative to an arbitrary reference origin O 100. Usually denoted x, r, or s, it corresponds to the straight line segment from O to P. In other words, it is the displacement or translation that maps the origin to P:

[0033]

[0034] The term "position vector" is used primarily in the fields of differential geometry, mechanics, and occasionally vector calculus. This is often used in two-dimensional (2D) or three-dimensional (3D) space (e.g., in a video game environment), but can be easily generalized to Euclidean and affine spaces of any dimension. This is relevant to the present disclosure in reducing spatial uncertainty for a user, as current GPS technology only considers a 2D "top-down" approach, and the present disclosure enables, for example, the position of a user or a user's device to be considered in 3D. In three dimensions, any set of three-dimensional coordinates and their corresponding basis vectors can be used to define the position of a point in space - whichever is simplest for the task at hand can be used.

[0035] refer to Figure 4A ,vector is the first position vector from the origin 100, which shows the position of the reference point A. The origin 100 is the common origin between all objects in the environment. Similarly, is a second position vector from the origin 100, which shows the position of the reference point B.

[0036] Typically, people use the familiar Cartesian coordinate system, or sometimes spherical polar coordinates, or cylindrical coordinates:

[0037]

[0038] where t is a parameter due to their rectangular or circular symmetry. These different coordinates and the corresponding basis vectors represent the same position vector r. The choice of coordinate system is mainly determined by the required resolution level. For example, if millimeter accuracy of geospatial certainty is required, a volumetric method with spherical polar coordinates may be the most appropriate. Another factor in the choice of coordinate system may be the computing power of the system making the measurement. In the first case, and for the remaining examples, a Cartesian coordinate system will be used because of its universality. The chosen coordinate system may be local to the user device and used to temporarily store the positions of reference markers between which displacement vectors can be calculated.

[0039] Although Cartesian coordinates are simple for many applications, for some types of motion of objects of interest, it may be necessary or more efficient to work in one of the nonlinear coordinate systems, such as polar or cylindrical coordinates. For example, if an avatar or user is in constant motion around an environment, the motion involves circular interpolation around multiple reference points, and therefore polar coordinates may be more convenient to work with than Cartesian coordinates. Spherical polar coordinates use a combination of linear and angular units to define a position in 2-D or 3-D space. In the case of spherical polar coordinates, a point is specified by a straight-line distance from a reference point (usually the origin 100 or the center of the user's viewpoint 110, 120) and one or two angles relative to a reference direction. These are called radial and angular coordinates (r, θ) or (r, θ, φ) in 2-D and 3-D, respectively.

[0040] A cylindrical coordinate system is a three-dimensional coordinate system that specifies point locations by distance from a selected reference axis such as an axis (not shown) at the origin 100, direction from the axis relative to a selected reference direction (usually the positive X direction), and distance from a selected reference plane perpendicular to the axis. The latter distance is given as a positive or negative number depending on which side of the reference plane faces the point. The power (effectively the origin) of a cylindrical coordinate system is the point at which all three coordinates can be given as zero. This is the intersection between the reference plane and the axis.

[0041] Recall that with Cartesian coordinates, any point in space can be defined by only one set of coordinates. The key difference when using polar coordinates is that the polar coordinate system allows a theoretically infinite number of sets of coordinates to describe any point. Therefore, in summary, spherical polar coordinates may be the preferred choice for minimizing geospatial uncertainty by minimizing or reaching a threshold volume of uncertainty. However, the simplicity of Cartesian coordinates can be used on hardware with processing limitations, such as mobile devices, etc., and cylindrical coordinates are used in conjunction with objects that have some rotational symmetry about the longitudinal axis (e.g., mapping of a stadium with a reference marker at the center of the stadium).

[0042] Return to reference Figure 4A ,vector and is a translation vector describing the translation from reference point A to reference point B or point B to point A, respectively. In Euclidean geometry, a translation is a geometric transformation that moves every point of a figure, shape, or space by the same distance in a given direction. A translation can also be interpreted as adding a constant vector to each point, or as moving the origin of a coordinate system. Thus, a translation in a new spatial environment can be applied to the origin position 100, or to the position of reference points A, B, or C.

[0043] In classical physics, translational motion is a movement that changes the position of a body (or objects), rather than a rotation. For example, translation is an operation that changes the position of all points (x, y, z) of an object according to the following formula

[0044] (x,y,z)→(x+Δx,y+Δy,z+Δz)

[0045] Where (Δx, Δy, Δz) is a displacement that is the same for every point of the object. The translation vector (Δx, Δy, Δz) common to all points of the object describes a specific type of displacement of the object, often called a linear displacement to distinguish it from displacements involving rotations (called angular displacements). In some scenarios, the translation vector alone will be sufficient to determine how to correct the position information, however, in some examples, particularly for reference markers in real-world environments, the translation vector may be accompanied by a rotation or rotation vector, which will be described in more detail below.

[0046] Therefore, after determining the position vectors of multiple reference points relative to, for example, the origin, we can also determine the translation vector from each reference point to another reference point. In this way, we can define something like Figure 4B Shapes like the ones shown. Figure 4B In , a triangle was chosen, however, a translation vector between two points (eg, defining a line) or indeed any other shape would be sufficient to implement the disclosure herein. Figure 4B The shape of will be used throughout the following examples and will be referred to as triangle ABC or ΔABC. It should be noted that ΔABC has a fixed shape and does not distort when translated, as mathematically described in this example as 3 translation vectors between points A, B and C;

[0047] In addition, the corresponding position of the reference marker is also determined in the virtual space (referred to as the new spatial position) using temporary local coordinates, the local coordinate system having a known orientation relative to the global coordinate system. Therefore, the geospatial and new spatial positions of the reference marker are known. The reference marker is also known at the time of scanning (i.e., reading / interacting with / imaging), which is the position and orientation of the user device relative to the reference marker. Obtaining the known physical size and appearance of the reference marker may include accessing a virtual space layer that includes the known physical size and appearance of the reference object.

[0048] Measurements between markers can be performed using an augmented reality measurement application (e.g., "AR Ruler" by Auki Labs (RTM)) that utilizes, for example, the device's built-in SLAM functionality and accelerometer to find the precise distance between the markers. Using the known orientation, the displacement between the markers can then be obtained or calculated. Thus, each marker is given 3D coordinates in a new spatial coordinate system that is temporarily created and stored on the device. The new spatial coordinates of each marker are precise, while the geospatial coordinates remain uncertain. Thus, the displacement between two or more markers, e.g. Figure 3 A and B can be expressed as

[0049] The local coordinate system may be a coordinate system in a virtual space, i.e., a new space system, which may be part of a partially virtual world, such as an augmented reality world of known orientation. Alternatively, the virtual space may be part of a completely virtual world, such as a virtual reality world. The virtual space may have a coordinate system different from the preliminary coordinates of the first and second position data. For example, the preliminary coordinates as a geographic space may be configured with the origin at the center of the earth, as in the case of GPS. The virtual space including the local coordinate system has a known orientation relative to the global coordinate system.

[0050] Figure 5 and 6 is an illustrative diagram showing the translation of the error radius according to some embodiments of the present disclosure. Figure 5 , select the minimum error radius, in this case, the error radius area 520oB, so as to translate towards the reference mark A Or more precisely, translation However, and is the same distance but in opposite directions, so for simplicity we will continue to use For completeness, Figure 6 shows the error radius 520 translation To the error radius 510. In some examples, the error region oB is determined from any coordinate 625 allowed within the region oB. of translation.

[0051] Mathematically, the intersection of two or more sets is the set of elements that are common to each of the sets. An element is in the intersection if it belongs to all sets. The intersection symbol is "∩", associated with the word "and", because A∩B is the set of elements that are in both A and B. To find the intersection of two (or more) sets, only those elements that are in both (or all) sets are listed. In a Venn diagram, the intersection of two sets A and B can be shown at the shaded area in the middle of two interlocking circles, in a similar way, the intersection between oA and oB is also the middle of two interlocking areas. In mathematical notation, the intersection of A and B is written as A∩B = {x: x∈A and x∈B}.

[0052] exist Figure 6 In , for example, we find that by moving the configuration to the leftmost possible coordinate allowed by uncertainty oB, we can infer that marker A cannot be further to the left, because assuming coordinates further to the left while maintaining the new spatial consistency would put marker B outside the acceptable geospatial range provided by GPS. In particular, we show that we can move the coordinates of the vector A to the left, and then move them to the left. To translate oB, and the geographic spatial coordinates of marker A must be between oA and the translated oB, In the intersection area, + is used to indicate the translation of oB

[0053] exist Figure 2 , 3 , 5 and 6, only two markers and one translation vector are shown. However, it should be understood that multiple markers may be used and the term translation vector is sometimes used herein as a label to describe not only the translation of the second error radius in the x, y and / or z directions, but also the accompanying rotation and / or additional rotation vector. In particular, when a user scans two or more reference markers, the translation vector may include a series of translations and rotations between the two reference markers, similar to the path taken by the user's device in the local coordinate system. However, in some examples, the shortest path translation between two or more reference markers may be calculated regardless of the path taken by the user's device in the local coordinate system.

[0054] In another use case, if a user communicates with multiple beacons in a certain location, where the exact location of each beacon is known to the user device, the relative location of the user device relative to the beacon can be determined, reducing the uncertainty radius, and then determining the true location of the user device. In other words, if the user device communicates with multiple beacons, it will be able to determine the direction, distance, and uncertainty radius of each beacon, and the uncertainty radius can be minimized by the method of this article. Knowing the true location of the beacon and the relative location of the user device, the true location of the user device can be determined. The beacon can be, for example, a UWB beacon, a Wi-Fi mesh beacon, etc. in the user's home, office space, train station, bus station, stadium, etc.

[0055] In addition, the imaging of the reference markers may depend on the corresponding field of view of the user device. In some examples, after observing the reference markers in the field of view of the user device, the current position will be requested (e.g., via GPS, etc.) and the area will be mapped using the SLAM algorithm until the next reference marker. The next reference marker may be located behind the user relative to the user's initial field of view. Therefore, when applying a translation vector, a rotation vector may be required. In addition, in another example, the user may be able to record one or more reference markers by rotating his field of view without moving the user device. Therefore, in some scenarios, the translation vector may also include a rotation element or a rotation vector. In some examples, the translation vector and the rotation vector are applied separately, or applied in parallel to obtain a combined translation vector.

[0056] For completeness, mathematically, the axis-angle representation of a rotation parameterizes a rotation in three-dimensional Euclidean space by two quantities: a unit vector e representing the direction of the axis of rotation, and an angle θ describing the magnitude of the rotation about the axis. In this example, the unit vector e, i.e., the axis of rotation, will be parallel to the conventional z-direction (e.g., upward), which is typically substantially perpendicular to the field of view of the user or the user's device. However, other rotation axes may be selected, for example, the rotation axis may be parallel to the translation vector. In this way, the rotation vector may be directly applied to the translation vector to produce a single vector.

[0057] Because the magnitude of e is constrained, only two numbers are needed instead of three to define the orientation of a unit vector e rooted at the origin. For example, the elevation and azimuth of e are sufficient to locate it in any particular Cartesian coordinate system. The angle and axis determine the transformation that rotates a three-dimensional vector (e.g., the position vectors of the first and second users). The axis-angle representation is equivalent to the more concise rotation vector, also called an Euler vector. In this case, both the rotation axis and angle are represented by a vector with the same direction as the rotation axis, whose length is the rotation angle θ,

[0058]

[0059] Many rotation vectors correspond to the same rotation. In particular, for any integer M, a rotation vector of length θ+2πM encodes the same rotation as a rotation vector of length θ. Therefore, there are at least countable infinite rotation vectors corresponding to any rotation. In addition, all rotations of 2πM are the same as no rotation at all, so for a given integer M, all rotation vectors of length 2πM in all directions constitute a two-parameter uncountable infinite number of rotation vectors that encode the same rotation as the zero vector. These relationships are taken into account when inverting the exponential map, that is, when looking for a rotation vector corresponding to a given rotation matrix when applying the present disclosure using matrices rather than vectors.

[0060] Figures 7 to 12 is an exemplary diagram of determining an error radius, a translation error radius, and an iterative process to achieve minimized geospatial uncertainty according to some embodiments of the present disclosure. Figures 7 to 12 With an error radius, the error radius is shown as a rectangle for simplicity. Figure 7 , the three squares (sA, sB, sC) represent the three error radii about the reference marks A, B and C. In addition, the new spatial relationship and the displacement between points A, B and C have been measured and Figures 7 to 12 In particular, ΔABC contains three translation vectors They describe the displacement between each of the reference markers A, B and C respectively.

[0061] about Figure 8 , in the first case, the error square sB is the smallest error square and is therefore chosen to be translated first. The leftmost corner coordinate of sB is chosen as the translation starting point because this is closest to sA. By translating the error square sB It can be inferred that the marker A must exist in the intersection area (or "intersection") of sA and [sB+AB]. In other words, the marker A must exist in sA∩[sB+AB]. Therefore, the error square sA can be reduced to the size of sA∩[sB+AB], and sA is now updated to the intersection sA∩[sB+AB].

[0062] about Fig. 9 , the next iteration begins. In this example, sB is translated It can be inferred that the marker C must exist in the intersection of sC and [sB+BC]. In other words, the marker C must exist in sC∩[sB+BC]. Therefore, the error square sC can be reduced to the size of sC∩[sB+BC], and sC is now updated to the intersection sA∩[sB+BC]. Figure 8 and 9The described iterative steps can be performed sequentially or in parallel. In particular, the system can determine that since the error square sB is the smallest of the three error squares, the error square sB can be translated in parallel to all other points, and the intersection area of ​​sB and the other error squares can be recorded.

[0063] about Fig.10 , the intersection of sC and sB has now become the minimum error square. Therefore, the next step in the iterative process will be to translate the now reduced error square sC by and / or In this way, the original error square sB is also reduced; this means that the error uncertainties for all three reference markers have now been reduced. Marker A is within sA∩[sB+AB], marker C is within sC∩[sB+BC], marker C is within [sC∩[sB+BC]]∩sB, and so on. By continuously iterating in this way (moving the minimum derived uncertainty error by the new spatial reference displacement of the reference markers), the uncertainty of the geospatial position measurement is greatly reduced, and by shifting and reducing the size of the intersection area, the uncertainty area can be made equal to or smaller than the area of ​​the minimum uncertainty area generated in the first case. For example, with respect to Fig.11 , the process is performed again, this time the updated error square sC (i.e. [sC∩[sB+BC]) is translated by AC, and the error square sA, which was once reduced, is further reduced.

[0064] Fig.12 It is demonstrated that a subsequent measurement of a reference marker (e.g., reference marker B) by the user device can introduce further geospatial uncertainty, enabling the iterative process to be performed again because the new error square sB* has an intersection area with the updated error square around reference marker B that further reduces the uncertainty, and so on. In this way, the iterative process can continue until the process reaches a threshold. In some examples, the threshold can be based on an ideal uncertainty radius size, after a total number of iterations, after a timer expires, and / or when the resolution of the uncertainty (e.g., the uncertainty radius) is in the same order of magnitude as the user device. For example, the iterative process can stop when the uncertainty radius of each reference marker is less than a certain size, such as less than 10 mm; after multiple iterations, such as 100 iterations; after a period of iteration, such as 5 seconds; or when the size of one or more errors (the size of the user device). It should be understood that these examples are given to illustrate the configuration of the threshold of the iterative process.

[0065] Fig.13is an illustrative flow chart of a process for minimizing geospatial uncertainty of a location according to some embodiments of the present disclosure. It should be noted that process 1300 or any of its steps may be performed in the following manner: Fig.14 and 15 In addition, one or more steps of process 1300 may be incorporated into or combined with one or more steps of any other process or embodiment described herein.

[0066] At step 1302, the system receives first position data of a first reference marker, the first position data including a first uncertainty radius about a first preliminary coordinate. At step 1304, the system receives second position data of a second reference marker, the second position data including a second uncertainty radius about a second preliminary coordinate. The receiving may be performed by a transceiver, such as a reference Fig.15 transceiver described.

[0067] At step 1306, the system receives a first translation vector between a first reference marker and a second reference marker. In some examples, the first translation vector includes a displacement from the second reference marker to the first reference marker, or the first translation vector may include a displacement from the first reference marker to the second reference marker. In some examples, the translation vector may be calculated, that is, the translation vector may have been received and step 1306 is replaced by a step in which the system calculates the first translation vector from the second reference marker to the first reference marker, or vice versa. For example, relative to an origin (e.g., origin 400 of FIG. 4 ), the position and distance of the reference marker are determined. In practice, this is achieved by determining the coordinates of the reference marker relative to the origin (e.g., the center of the earth at (x1, y1, z1)), so the position vector is in as well as are orthogonal unit vectors; the same operation can be performed for each reference marker. Afterwards, the translation vector between each reference marker is calculated. In practice, this is done by determining the coordinates of the reference markers relative to the origin, for example, if reference marker A is at (x1, y1, z1) and reference marker B is at (x2, y2, z2). Therefore, the translation vector from reference marker B to reference marker A is in, as well as are orthogonal unit vectors; the same operation can be performed to determine the translation vector between each reference marker, mutatis mutandis.

[0068] In step 1308, the system translates the second uncertainty radius by the first translation vector, as shown in FIG. Figures 5 to 12For example, the first and second position data may contain information such as direction, distance, height, or other information describing the specific position of the imaged (or scanned / measured, etc.) reference marker relative to the user's current position and / or field of view. Using the translation vector, the position information can be corrected so that the direction, distance, height or other information instead describes the true position to be used in the local coordinate system.

[0069] At step 1310, based on the intersection area of ​​the translated second uncertainty radius and the first uncertainty radius, the system reduces the first uncertainty radius to the intersection area.

[0070] Process 1300 may also include optional steps (not shown). For example, a repeat cycle is initiated between steps 1308 and 1310. In addition, process 1300 may also include: Fig.12 The process 1300 may further include a waiting period (not shown) between steps 1304 and 1306 to allow the user sufficient time to scan multiple reference marks. The waiting period may include a manual action button to force the process 1300 to proceed to step 1306.

[0071] Fig.14An illustrative block diagram of a computing configuration 1400 that may include a media device, system, and / or method disclosed herein is shown. The computing configuration 1400 includes a user device 1402. The user device 1402 may be a portable electronic device, such as a smart phone, a tablet computer, a laptop computer, a smart watch, smart glasses, augmented reality (AR) glasses, AR lenses, or any other suitable device. In some embodiments, the user device 1402 may include a control circuit 1404 and an input / output (I / O) path 1406. The control circuit 1404 may include a processing circuit 1408 and a storage device 1410 (e.g., RAM, ROM, hard disk, removable disk, etc.), and the storage device 1410 may include instructions for executing the method (such as process 1300) herein. In addition, the storage device 1410 may temporarily store a local coordinate system for performing measurements between two or more reference markers. For example, the reference marker may be converted into a position in a local coordinate system, and the displacement between the reference markers may be measured using a SLAM algorithm on the user device. I / O paths 1406 may provide device information or other data over a local area network (LAN) or wide area network (WAN), and / or provide other content and data to control circuitry 1404. Control circuitry 1404 may be operable to send and receive commands, requests, signals (digital and analog), and other suitable data using I / O paths 1406. I / O paths 1406 may connect control circuitry 1404 (and, in particular, processing circuitry 1408) to one or more communication paths.

[0072] The user device 1402 may include a heads-up display 1412 and a speaker 1414 to display content visually and audibly. In addition, to interact with the user, the user device 1402 includes a user interface 1416. The user interface 1416 may include a scroll wheel, a physical button, a switch, a touch pad, a direct drive motor, a trigger, or a touch screen. The user interface 1416 is connected to the I / O path 1406 and the control circuit 1404.

[0073] Control circuit 1404 may be based on any suitable processing circuit, such as processing circuit 1408. As referred to herein, processing circuitry should be understood to mean circuitry based on one or more microprocessors, microcontrollers, digital signal processors, programmable logic devices, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc., and may include multi-core processors (e.g., dual-core, quad-core, hexa-core, or any suitable number of cores), cloud-based computing units, or even supercomputers. In some examples, the processing circuitry may be distributed across multiple separate processors or processing units, such as multiple processing units of the same type (e.g., two Intel Core i7 processors) or multiple different processors (e.g., an Intel Core i5 processor and an Intel Core i9 processor).

[0074] The memory may be an electronic storage device provided as a storage device 1410, which is part of the control circuit 1404. The storage device 1410 may store instructions that, when executed by the processing circuit 1408, perform the processes described herein. As referred to herein, the phrase "electronic storage device" or "storage device" should be understood to mean any device for storing electronic data, computer software or firmware, such as a random access memory, a read-only memory, a hard drive, a solid-state device, a quantum storage device, or any other suitable fixed or removable storage device and / or any combination thereof. Non-volatile memory may also be used (e.g., to start boot routines and other instructions). The user device 1402 may be a smart phone, a tablet, an e-reader, a laptop computer, etc., which includes a camera (not shown) to record an image of a reference marker.

[0075] The computing configuration 1400 may also include a communication network 1418 and a server device 1420. The user device 1402 may be coupled to the communication network 1418 to communicate with the server device 1420. The communication network 1418 may be one or more networks, including the Internet, a mobile phone network, a mobile voice or data network (e.g., Bluetooth, Wi-Fi, WiMAX, Zigbee, GSM, UTMS, CDMA, TDMA, 3G, 4G, 4G LTE, 5G, or other wireless transmissions as described by the relevant 802.11 wireless communication protocols), a mesh network, a peer-to-peer network, a cable network, or other types of communication networks or combinations of communication networks. In addition, additional user devices (not shown) may also communicate with the communication network 1418.

[0076] In some examples, server device 1420 may include control circuitry 1422 and input / output (I / O) paths 1424. Control circuitry 1404 may include processing circuitry 1426 and storage devices 1428, which may be similar to those already discussed with respect to user device 1402. Server device 1420 may be a content provider for user device 1402, such as SLAM algorithm data, new spatial data information, geospatial data information, user profile data, and the like.

[0077] It should be understood that the user device 1402 is not limited to the embodiments and methods shown and described herein. In non-limiting examples, the user device 1402 can be a digital storage device, a digital media receiver, a digital media adapter, a streaming device, a personal computer (PC), a laptop computer, a tablet computer, a PC media server, a PC media center, a handheld computer, a personal digital assistant (PDA), a mobile phone, a portable game console, a smart phone, a virtual reality head-mounted device, an augmented reality head-mounted device, a mixed reality head-mounted device, or any other device, client equipment or wireless device capable of performing the method described in the reference claims, and / or a combination thereof.

[0078] Fig.15 An exemplary media device 1500 according to some embodiments of the present disclosure is shown. The media device 1500 includes a transceiver module 1510, a control module 1520, and a network module 1530. The media transmission system can communicate with additional user devices 1535, such as a gateway, a smart phone, a camera system (e.g., a 3D camera), an Internet of Things (IoT) device, or other device, as described in reference Fig.14 In some examples, the additional user device 1535 is a primary device for a user interacting with the reference marker, and the media device 1500 includes components for performing the processing, particularly when the additional user device is limited in processing capabilities.

[0079] In some examples, the transceiver module communicates with the second user device 1535 via a communication link 1518. The communication link 1518 between the transceiver module 1510 and the second user device 1535 may include a physical connection facilitated by an input port such as a 3.5 mm jack, an RCA jack, a USB port, an Ethernet port, or any other suitable connection for communicating over a wired connection, or may include a wireless connection via Bluetooth, Wi-Fi, WiMAX, Zigbee, GSM, UTMS, CDMA, TDMA, 3G, 4G, 4G LTE, 5G, or other wireless transmissions as described by relevant 802.11 wireless communication protocols.

[0080] In some examples, the second user device 1535 can receive image data input (e.g., an image of a QR code representing a reference marker as described above) and then transmit the image data to the media device 1500. However, these examples are considered non-limiting, and other combinations of distribution of features herein across two or more devices are considered within the scope of the present disclosure. For example, each of the transceiver module, the network module, and the control module can be a separate Internet of Things (IoT) device that each performs a portion of the method herein. These devices can be collectively referred to as a system. In some examples, the image data can be stored in a device such as a computer. Fig.14On the server of server device 1420.

[0081] The media device 1500 and / or user device 1535 system may collectively be a series of image capture devices operated on a portable backpack, vehicle (e.g., a car or van), bicycle, etc. In addition, the media device 1500 may be a virtual reality, mixed reality, or augmented reality head mounted device, and in such a case, may include an eye contact detection component that may be part of the control module 1520, which may be used to identify the user's gaze point in order to determine whether the user is focused on a particular portion of the environment and / or determine the user's line of sight or field of view. For example, the location where the user's eyes are focused may determine the location where image data is captured and recorded.

[0082] In particular, the transceiver module 1510 and the network module 1530 may be configured to receive first position data of a first reference marker, the first position data including a first uncertainty radius about a first preliminary coordinate, and receive second position data of a second reference marker, the second position data including a second uncertainty radius about a second preliminary coordinate. In addition, the control module 1520 may be configured to calculate a first translation vector between the first reference marker and the second reference marker; translate the second uncertainty radius to the first preliminary coordinate by the first translation vector; and reduce the first uncertainty radius to the intersection area based on the intersection area of ​​the translated second uncertainty radius and the first uncertainty radius.

[0083] The above-described systems and processes are intended to be illustrative rather than restrictive. Those skilled in the art will appreciate that the actions of the processes discussed herein may be omitted, modified, combined, and / or rearranged, and any additional actions may be performed, without departing from the scope of the present invention. More generally, the above disclosure is intended to be exemplary rather than restrictive. Only the appended claims are intended to set boundaries for what is included in the present disclosure. In addition, it should be noted that the features and limitations described in any one embodiment may be applied to any other embodiment herein, and the flowcharts or examples associated with one embodiment may be appropriately combined, performed in a different order, or performed in parallel with any other embodiment. In addition, the systems and methods described herein may be executed in real time. It should also be noted that the above-described systems and / or methods may be applied to other systems and / or methods, or used in accordance with other systems and / or methods. In this specification, the following terms may be understood according to the following explanations:

[0084] All features disclosed in this specification (including any accompanying claims, abstract and drawings) and / or all steps of any method or process so disclosed may be combined in any combination, except combinations in which at least some of such features and / or steps are mutually exclusive.

[0085] Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise. Therefore, unless expressly stated otherwise, each feature disclosed is only an example of a series of general equivalent or similar features.

[0086] The invention is not limited to the details of any foregoing embodiments. The invention extends to any novel feature or any novel combination of features disclosed in this specification (including any accompanying claims, abstract and drawings), or to any novel step or any novel combination of steps of any method or process so disclosed. The claims should not be interpreted as covering only the foregoing embodiments, but also any embodiment falling within the scope of the claims.

[0087] Throughout the description and claims of this specification, the words "comprise" and "contain" and their variations mean "including but not limited to", and they are not intended to (and do not) exclude other parts, additives, components, integral bodies or steps. Throughout the description and claims of this specification, the singular includes the plural, unless the context requires otherwise. In particular, where the indefinite article is used, the specification should be understood as considering both the plural and the singular, unless the context requires otherwise.

[0088] All features disclosed in this specification (including any accompanying claims, abstract and drawings) and / or all steps of any method or process so disclosed may be combined in any combination, except combinations in which at least some of such features and / or steps are mutually exclusive. The invention is not limited to the details of any foregoing embodiments. The invention extends to any novel feature or any novel combination of features disclosed in this specification (including any accompanying claims, abstract and drawings), or to any novel step or any novel combination of steps of any method or process so disclosed.

[0089] The reader's attention is directed to all papers and documents filed concurrently with or previous to the specification related to the present application and which are disclosed to the public with the present specification, the contents of all of which are incorporated herein by reference.

Claims

1. A method for minimizing uncertainty in position measurement, the method comprising: receiving first position data for a first reference marker, the first position data comprising a first uncertainty radius about a first preliminary coordinate; receiving second position data for a second reference marker, the second position data comprising a second uncertainty radius about the second preliminary coordinate; receiving a first translation vector, the first translation vector comprising a displacement from the second reference marker to the first reference marker; Translating the second uncertainty radius by the first translation vector; as well as Based on an intersection area of ​​the translated second uncertainty radius and the first uncertainty radius, the first uncertainty radius is reduced to the intersection area.

2. The method according to claim 1 further comprises iteratively repeating the translation and reduction of the uncertainty radius. The method of claim 2 , further comprising stopping the iterative process when the first radius reaches a threshold value.

4. The method of claim 3, wherein the threshold is based on an ideal uncertainty radius size, a total number of iterations, an expiration of a timer, and / or when the resolution of the uncertainty is on the same order of magnitude as the user equipment.

5. The method according to claim 1, further comprising: photographing the first reference marker and the second reference marker by a camera of the user device; The first position data and the second position data are stored in a temporary local coordinate system stored on the user device.

6. The method according to claim 5 further comprises executing a simultaneous learning and mapping SLAM algorithm on the user device to obtain the displacement between the first marker and the second marker in the local coordinate system.

7. The method according to claim 1, further comprising: receiving third position data for a third reference marker, the third position data comprising a third uncertainty radius about a third preliminary coordinate; receiving a second translation vector, the second translation vector comprising a displacement from the first reference marker to a third reference marker; translating the reduced first uncertainty radius by a second translation vector; as well as Based on an intersection area of ​​the translated reduced first uncertainty radius and the third uncertainty radius, the third uncertainty radius is reduced to the intersection area.

8. The method according to claim 7, further comprising: photographing the first reference marker and the third reference marker on a camera of the user device; storing the first position data and the third position data in a temporary local coordinate system stored on the user device; as well as A simultaneous learning and mapping SLAM algorithm is performed on the second user device to obtain an absolute distance between the first marker and the third marker in the local coordinate system.

9. The method of claim 8, wherein the user equipment is a second user equipment different from the first user equipment.

10. A non-transitory computer readable medium having recorded thereon instructions that, when executed, perform a method for minimizing uncertainty in position measurements, the method comprising: receiving first position data for a first reference marker, the first position data comprising a first uncertainty radius about a first preliminary coordinate; receiving second position data for a second reference marker, the second position data comprising a second uncertainty radius about the second preliminary coordinate; receiving a first translation vector, the first translation vector comprising a displacement from the second reference marker to the first reference marker; Translating the second uncertainty radius by the first translation vector; as well as Based on an intersection area of ​​the translated second uncertainty radius and the first uncertainty radius, the first uncertainty radius is reduced to the intersection area.

11. The computer readable medium of claim 10, further comprising iteratively repeating the translating and decreasing of the uncertainty radius.

12. The computer-readable medium of claim 11, further comprising stopping the iterative process when the first radius reaches a threshold value.

13. The computer-readable medium of claim 12, wherein the threshold is based on an ideal uncertainty radius size, a total number of iterations, an expiration of a timer, and / or when the resolution of the uncertainty is on the same order of magnitude as the user device.

14. The computer-readable medium of claim 10, further comprising: photographing the first reference marker and the second reference marker by a camera on the user device; as well as The first position data and the second position data are stored in a temporary local coordinate system stored on the user device.

15. The computer-readable medium of claim 14, further comprising executing a simultaneous learning and mapping SLAM algorithm on the user device to obtain a displacement between the first marker and the second marker in the local coordinate system.

16. The computer-readable medium of claim 10, further comprising: receiving third position data for a third reference marker, the third position data comprising a third uncertainty radius about a third preliminary coordinate; receiving a second translation vector, the second translation vector comprising a displacement from the first reference marker to a third reference marker; translating the reduced first uncertainty radius by a second translation vector; as well as Based on an intersection area of ​​the translated reduced first uncertainty radius and the third uncertainty radius, the third uncertainty radius is reduced to the intersection area.

17. The computer-readable medium of claim 16, further comprising: photographing the first reference mark and the third reference mark by a camera of the user device; storing the first position data and the third position data in a temporary local coordinate system stored on the user device; as well as A simultaneous learning and mapping SLAM algorithm is performed on the second user device to obtain a displacement between the first marker and the third marker in the local coordinate system.

18. The computer-readable medium of claim 17, wherein: The user equipment is a second user equipment different from the first user equipment.

19. A media device comprising a control module, a transceiver module and a network module for minimizing uncertainty in position measurement, configured to: receiving first position data for a first reference marker, the first position data comprising a first uncertainty radius about a first preliminary coordinate; receiving second position data for a second reference marker, the second position data comprising a second uncertainty radius about the second preliminary coordinate; receiving a first translation vector, the first translation vector comprising a displacement from the second reference marker to the first reference marker; Translating the second uncertainty radius by the first translation vector; as well as Based on an intersection area of ​​the translated second uncertainty radius and the first uncertainty radius, the first uncertainty radius is reduced to the intersection area.

20. The media device of claim 19, further configured to iteratively repeat the translating and decreasing of the uncertainty radius.

21. The media device of claim 20, further configured to stop the iterative process when the first radius reaches a threshold value.

22. The media device of claim 21, wherein the threshold is based on an ideal uncertainty radius size, a total number of iterations, expiration of a timer, and / or when the resolution of the uncertainty is on the same order of magnitude as the user device.

23. The media device of claim 19, further configured to: photographing the first reference mark and the second reference mark by a camera; The first position data and the second position data are stored in a temporary local coordinate system stored on the user device.

24. The media device of claim 23, further configured to execute a simultaneous learning and mapping SLAM algorithm on the user device to obtain a displacement between the first marker and the second marker in the local coordinate system.

25. The media device of claim 19, further configured to: receiving third position data for a third reference marker, the third position data comprising a third uncertainty radius about a third preliminary coordinate; receiving a second translation vector, the second translation vector comprising a displacement from the first reference marker to a third reference marker; translating the reduced first uncertainty radius by a second translation vector; as well as Based on an intersection area of ​​the translated reduced first uncertainty radius and the third uncertainty radius, the third uncertainty radius is reduced to the intersection area.

26. The media device of claim 25, further configured to: photographing the first reference mark and the third reference mark by a camera on the second user device; storing the first position data and the third position data in a temporary local coordinate system stored on the user device; and A simultaneous learning and mapping SLAM algorithm is performed on the user device to obtain an absolute distance between the first marker and the third marker in the local coordinate system.

27. The media device of claim 26, wherein the user device is a second user device different from the first user device.

28. A system for minimizing uncertainty in position measurements, the system comprising: means for receiving first position data for a first reference marker, the first position data comprising a first uncertainty radius about a first preliminary coordinate; means for receiving second position data for a second reference marker, the second position data comprising a second uncertainty radius about the second preliminary coordinate; means for receiving a first translation vector, the first translation vector comprising a displacement from the second reference marker to the first reference marker; means for translating the second uncertainty radius by the first translation vector; as well as Means for reducing the first uncertainty radius to an intersection area based on an intersection area of ​​the translated second uncertainty radius and the first uncertainty radius.

29. The system of claim 28, further comprising means for iteratively repeating the translation and reduction of the uncertainty radius.

30. The system of claim 29, further comprising means for stopping the iterative process when the first radius reaches a threshold value.

31. The system of claim 30, wherein: The threshold is based on a desired uncertainty radius size and / or number of iterations.

32. The system of claim 28, further comprising: means for photographing the first reference marker and the second reference marker via a camera on the user device; Means for storing the first position data and the second position data in a temporary local coordinate system stored on the user equipment.

33. The system of claim 32, further comprising means for executing a simultaneous learning and mapping SLAM algorithm on the user device to obtain a displacement between the first marker and the second marker in a local coordinate system.

34. The system of claim 28, further comprising: means for receiving third position data for a third reference marker, the third position data comprising a third uncertainty radius about the third preliminary coordinate; means for receiving a second translation vector, the second translation vector comprising a displacement from the first reference marker to a third reference marker; means for translating the reduced first uncertainty radius by a second translation vector; as well as Means for reducing the third uncertainty radius to an intersection area based on the intersection area of ​​the translated reduced first uncertainty radius and the third uncertainty radius.

35. The system of claim 34, further comprising: means for photographing the first reference marker and the third reference marker via a camera on a second user device; means for storing the first position data and the third position data in a temporary local coordinate system stored on the user device; as well as Means for executing a simultaneous learning and mapping SLAM algorithm on the user device to obtain an absolute distance between the first marker and the third marker in a local coordinate system.

36. The system of claim 35, wherein the user device is a second user device different from the first user device.

Images