Positioning method of large-space intelligent glasses, head-mounted display equipment and storage medium

Abstract

The invention relates to a positioning method of large-space intelligent glasses, head display equipment and a storage medium, belongs to the technical field of meta-cosmic space calculation, and is suitable for a meta-cosmic positioning function that the head display equipment of players in a multi-user interaction large space can directly interact without learning or reasoning of a central server. A plurality of different texture pictures are arranged on the bottom surface or the ceiling of a large space, any camera on the intelligent glasses is used for collecting images, the texture pictures are recognized from the images collected by the camera T, picture information corresponding to the texture pictures is obtained, the world position of the camera T can be obtained through geometric calculation, and the texture pictures are displayed on the intelligent glasses. The method can be quickly realized on local head-mounted display equipment, so that the learning process of a server or the delay of image reasoning and position information returning and errors caused by a positioning sensor are avoided, and the problem of errors caused by long distance of a large-space site or shielded texture pictures is also avoided.

Description

Technical Field

[0001] The present invention relates to the field of metaverse space computing technology, and specifically to a positioning method for large-space smart glasses, a head-mounted display device, and a storage medium. Background Art

[0002] Extended Reality (XR) smart glasses (head-mounted display devices) are divided into two types of glasses: AR augmented reality and VR virtual reality. AR glasses use Optical See Through or optical lenses to achieve perspective and see the surrounding environment (hereinafter referred to as optical perspective OST). VR glasses are purely virtual glasses that cannot see the outside environment. In recent years, MR mixed reality glasses have begun to appear, that is, VR uses a camera to view the surrounding environment. This is achieved by using Video See Through or Visual Pass Through or video perspective (hereinafter referred to as video perspective VST). VST glasses can calculate the positioning of glasses through the camera used by VST itself or other cameras on the glasses. OST glasses originally did not have cameras, but now some advanced OST glasses have added camera configurations to calculate the surrounding space application. Many OST or AR glasses are also called MR mixed reality glasses because they have cameras to calculate the environment space.

[0003] In recent years, large-space applications have emerged in the metaverse industry. Large-space applications refer to applications in which multiple people can walk in the same space through smart glasses, watch virtual renderings or real scenes, and interact with real or virtual objects, virtual characters, and other real players. Large-space technology refers to a mixed virtual and real environment that uses spatial positioning methods to allow rendering and interaction of multiple people and objects. Because many players are using smart glasses at the same time in the same space (also known as "world position" WorldCoordinates), the local position of each player's glasses (also known as "personal position" Personal Coordinates) and its relative position and direction to the world position are the core of large-space technology. Incorrect calculation of the position of large-space glasses will lead to the following problems: (1) the virtual scene rendered in the glasses will drift or the position of objects will deviate too much; (2) the position offset of players will cause multiple people to collide or fail to contact correctly; (3) the interaction between players and virtual objects will be incorrect due to incorrect position calculation; (4) the direction or position of the shooting or receiving will be incorrect due to inaccurate position when catching or shooting; (5) the height of each player is incorrect when looking at each other, resulting in height illusion, and other application errors caused by incorrect position of each player's glasses.

[0004] The main way to calculate the position of glasses on the market is using point cloud vision and SIFT calculation methods. The general logic of point cloud and SIFT is to use unique textures, marks or graphics on the walls, ceilings and bottom surfaces (hereinafter referred to as "texture images") to perform visual neural network or machine training. The texture graphics in all spaces cannot be repeated, otherwise the trained neural network or machine learning will give incorrect or repeated world positions during the reasoning process. Since point cloud and SIFT need to be trained, it is inevitable to transmit the texture graphics captured by the glasses to the server in real time for reasoning, and then send the reasoning results back to the head display device. Therefore, there is a problem of delayed position information, which is a fatal experience in large space applications, especially fast-speed competitive applications. This same model will also cause a waste of time in the process of making applications, such as long training time and the need for error correction.

[0005] In addition, when used in large spaces, multiple people use the same space at the same time. If the texture image is identified through the image captured by the front camera of the glasses, it is possible that other players will block the line of sight and fail to correctly capture the texture image of the opposite wall. The longer the distance, the greater the calculated position error. If the specific position of the glasses cannot be correctly inferred, the glasses must rely on the assistance of the device's own gyroscope, Bluetooth positioning, TOF positioning, laser positioning or other SLAM positioning technologies. These auxiliary technologies will have their own errors and drift problems, and the device needs to add redundant positioning sensors, which will undoubtedly increase the cost, computing power, weight and energy consumption of the device or system.

[0006] Currently, most large-space applications use a method where multiple head-mounted display devices share the same server. Each head-mounted display device uploads the texture image seen by the personal device to the server, and the world position relationship of all head-mounted display devices is calculated and sent down in the server, so that each head-mounted display device knows the position of other head-mounted display devices in the same world space to achieve interaction. This requires that large spaces must deploy servers and infer the delay of the position sent back to each device. Summary of the invention

[0007] The purpose of the present invention is to provide a positioning method, head-mounted display device and storage medium for large-space smart glasses, which are suitable for large-space metaverse application scenarios with multi-person interaction, and are particularly suitable for scenarios in which the head-mounted display devices of each player in the large space can directly interact without training and reasoning through a central server. By acquiring image information of texture images in the large space, the world position of the player's own glasses can be quickly calculated, and at the same time, the player's own world position and game status data can be broadcast to other players in the same space, thereby supporting the demand for rapid response to the position information of each player in large-space applications.

[0008] The present invention provides a positioning method for large-space smart glasses. A plurality of different texture images are arranged on the bottom surface or ceiling of the large space. An image is collected by any camera on the smart glasses. The texture image is identified from the image collected by the camera T, and image information corresponding to the texture image is obtained. The image information can be directly obtained by analyzing the texture image, or the ID of the texture image can be obtained. The image information is matched in a comparison table locally stored in the smart glasses according to the ID. The image information includes the world position (XYZ) and graphic size (m, n) of the image; three corner anchor points (A, m, n) are arranged on the texture image and form an angle nAm, wherein the corner anchor point A is the vertex of the angle nAm, the world position of the corner anchor point A is defined as the world position (XYZ) of the image, the line An from A to the n corner anchor point is in the positive direction of the Y axis, the line Am from A to the m corner anchor point is in the positive direction of the X axis, the length of the line nA in the real world is n, and the length of the line Am in the real world is m; if the angle nAm is not 90 0 , then the image information also includes the angle nAm, which is used to convert the X-axis and Y-axis; According to the image information, we can get: the world position A(X A ,Y A ,Z A ), the world position m(X m ,Y m, Z m ) and the world position n(X n ,Y n ,Z n ), the X m =X A +m, Y m =Y A , X n =X A , Y n = Y A +n,Z m =Z n =Z A ; Get the pixel position P corresponding to the above corner anchor point mAn from the collected image A (X PA ,Y PA ), P m (X Pm ,Y Pm ) and P n (X Pn ,Y Pn ); The nine-axis geomagnetic chip of the smart glasses is used to obtain the pixel position P of the vertical ground or ceiling in the image captured by the camera.O (X Po ,Y Po ); Let the normal of camera T perpendicular to the bottom or ceiling and the line H connecting camera T to corner anchor point A be A The angle formed is , the line H between the normal of the camera T perpendicular to the bottom or ceiling and the camera T to the corner anchor point m m The angle formed is , the line H between the normal of the camera T perpendicular to the bottom or ceiling and the camera T to the corner anchor point n n The angle formed is ; The world position of camera T can be obtained through geometric calculation (X T ,Y T ,Z T ).

[0009] The world position (X) of the camera T can be obtained by geometric calculation. T ,Y T ,Z T ), assuming that the texture image in the captured image is not deformed or has been corrected, the field of view FOV of the camera and the total number of pixels on the X axis of the screen X D , get the number of pixels per degree PPD=X D / FOV; the specific calculation steps are as follows: Step 1. Let the line Am from corner anchor point A to corner anchor point m be the X axis of the world space, and according to the pixel position P A and P m Calculate the slope S of the line Am X , then through point P X Point P A The X-axis is expressed by the straight line formula: Y=S X X+B X (6); The pixel position P A or P m Substituting into formula (6) we get B X ; Since the straight line P O P X Perpendicular to the X axis, pixel position P X The straight line P O P X Intersection with the X axis, perpendicular to the line P A P m The straight line P O P X The slope is -S X , expressed as: Y=-S XX+B XV (7); The pixel position P O (X Po ,Y Po ) into formula (7) to obtain B XV , so we can find the straight line P O P X The pixel position of the intersection with the X axis is P X (X PX ,Y PX ); Let An, the line connecting corner anchor point A to corner anchor point n, be the Y axis of the world space. A and P n Calculate the slope S of the line An Y , then through point P Y Click P A The straight line is expressed by the formula: Y=S Y X+B Y (8); The pixel position P A or P n Substituting into formula (8) we get B Y； Since the straight line P O P Y Perpendicular to the Y axis, pixel position P Y The straight line P O P Y Intersection with the Y axis, perpendicular to the line P A P n The straight line P O P Y The slope is -S Y , expressed as: Y=-S Y X+B YV (9); The pixel position P O (X Po ,Y Po ) into formula (9) to obtain B YV , so we can find the straight line P O P Y The pixel position of the intersection with the Y axis is P Y (X PY ,Y PY ); Step 2. Use the Pythagorean theorem to calculate the pixel distance P X P A and P X P m ,but =P X P A / PPD =P X P m / PPD(3); Calculated by the following formula: (1); or (2); Use the Pythagorean theorem to calculate the pixel distance P Y P A and P Y P m ,but =P Y P A / PPD =P Y P n / PPD(3); Calculated by the following formula: (1); or (2); Step 3. Add the two Z obtained above T After averaging, we get the world position of camera T (X T ,Y T ,Z T ).

[0010] The world position (X) of the camera T can be obtained by geometric calculation. T ,Y T ,Z T ), assuming that the texture image in the captured image has been deformed due to the camera angle problem, calculate the angle ( , , ): The three corner anchor points are all square positioning images. The angle of the upper half image of the positioning pattern of each corner anchor point is measured, and the largest angle is selected as , then the angle formed by the normal of camera T perpendicular to the ground or ceiling and the line connecting camera T to the corner anchor point is: (4); The angle ( , , ); First, use the Pythagorean theorem to get the following six formulas: ; Then the above six formulas are combined to calculate the world position T(X T ,Y T ,Z T ) function formula: (5).

[0011] The world positions of multiple cameras T are averaged or converted according to the known positional relationship of multiple cameras to obtain the world position of the head-mounted display device / smart glasses; the player's head-mounted display device / smart glasses broadcasts its own world position together with the current game status data to other players in the large space in real time.

[0012] The bottom surface of the large space refers to the ground, steps, walls or tabletop of the large space.

[0013] The three corner anchor points of the texture image may be positioning patterns without information or independent small texture images. The image information corresponding to the small texture image includes the world position of the small texture image itself and the size of the small texture image.

[0014] The image information of the texture image also includes a space region name for distinguishing a virtual space in a large space.

[0015] The image information of the texture image further includes: distance information p or q to adjacent texture images, that is, the distance p to the adjacent texture image in the positive direction of the X axis and the distance q to the adjacent texture image in the positive direction of the Y axis.

[0016] A head-mounted display device, comprising a camera, a display, a memory and a processor; The camera is connected to the processor and is used to scan the real environment where the user is located; The display is connected to the processor and is used to display the output content of the processor; The memory is connected to the processor and is used to store the computer program; The processor is used to execute the computer program to implement the above-mentioned positioning method of large-space smart glasses.

[0017] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the computer program implements the above-mentioned large-space smart glasses positioning method.

[0018] The present invention uses simple visual calculation to find and identify the image information of texture images (such as mature QR code logos) for images collected by cameras in any direction on smart glasses. The image information includes the world position (XYZ) and image size (m, n) of the image itself, and obtains the position of the player's own glasses based on the identified image information. At the same time, it broadcasts its own world position and game status data to other players in the same space, thereby obtaining the position and status information of the head display devices of other players in the same space. The above calculation process can be quickly implemented on the local head display device, avoiding the delay of image training, image reasoning and returning position information to the server and the errors caused by positioning sensor assistance, and also avoiding the error problems caused by long distance or blocked texture images. The present invention not only reduces the server but also greatly reduces the investment cost of large spaces. It only needs the computing power of smart glasses to realize multi-person interactive scenes. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The texture image of Example 1 in the present invention; Figure 2 The texture image of Example 2 of the present invention; Figure 3 A schematic diagram of the distance information between adjacent two-dimensional code images recorded in the present invention; Figure 4 The present invention is based on the single-dimensional space two-dimensional code to calculate the camera T position (X T ,Z T )’s trigonometric calculation diagram; Figure 5 The image of the present invention showing the undeformed QR code; Figure 6 The angle formed by the normal line of the center of the camera and the vertical projection line of the QR code in the present invention Schematic diagram of the changes; Figure 7 Schematic diagram of the change of the positioning pattern of the corner anchor point A of the QR code viewed from different angles through the camera in the present invention; Figure 8 The QR code seen at four pitch angles; Fig. 9 The image of the present invention showing the deformed QR code; Fig.10 This is a schematic diagram of the relationship between the top view camera T and the texture image of the present invention; Fig.11This is a schematic diagram of the relationship between the camera T and the corner anchor point A viewed from the side of the present invention; Fig.12 This is a functional block diagram of a head-mounted display device in the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0021] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0022] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or scheme described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way. The principle of the present invention is described as follows: (1) Texture images are set on the ground, steps, tables and / or ceilings in a large space. The texture images are all different. The camera in the smart glasses is used to collect images and texture image recognition is performed through simple visual calculations: Since players in large space applications need to wear smart glasses to walk in the large space, the ground of the space has the characteristics of being flat and safe to walk without causing falls. Existing smart glasses usually have cameras in multiple directions on the glasses frame. In order to solve the problem that the texture image is set on the wall, resulting in a long distance or being blocked by other players, and the need to lower or raise the head, resulting in a poor experience, the present invention proposes printing or pasting or projecting the texture image on the ground, steps, desktops and / or ceilings, and using the camera in any direction in the smart glasses to collect images. Through simple visual calculations, the texture image in the image can be found, and the image information contained in the texture image can be identified, or the image information can be obtained in the comparison table stored locally in the smart glasses through the texture image ID. The image information includes the world position (XYZ) and graphic size (m, n) of the image itself.

[0023] The image information of the texture image may also include a space area name, which is used to distinguish virtual spaces in a large space. Players can interact with virtual locations in different virtual spaces at the same physical location. When players are in different virtual spaces, the same texture image can be used to interact with different virtual scenes. A large space may also be composed of many subspaces, and subspaces may have overlapping and interactive areas. Overlapping virtual areas can better allow players to seamlessly transfer and use space, avoiding the problem of gaps caused by the non-intersection between subspaces.

[0024] The image information of the texture image also includes: distance information p or q to adjacent texture images. Figure 3 As shown, let the distance between two adjacent texture images be p or q, that is, the distance p from the adjacent texture image in the positive direction of the X axis and the distance q from the adjacent texture image in the positive direction of the Y axis. When the smart glasses recognize three texture images at the same time, they obtain the world positions (XYZ) and image sizes (m, n) of the three texture images, as well as the distances p or q between the two adjacent texture images, respectively. This forms a small matrix containing the world positions of the three texture images, image sizes (m, n), and the distance p between the two texture images on the X axis and the distance q between the two texture images on the Y axis. Similarly, the entire large space or its secondary space can have texture images with a fixed distance to calculate the glasses position multiple times in multiple dimensions. If the positions calculated by the two data are deviated, the average value of the two is a more accurate glasses position. Therefore, combined with the calculation of the spacing between adjacent texture images, the calculation deviation can be corrected to obtain a more accurate glasses position.

[0025] The present invention can also paste, print or project the texture image vertically on a wall or the ground. The vertical texture image can be made by changing the An direction to the Z axis and setting the Am direction to the X axis or the Y axis. If the Am direction is the XY two-dimensional direction, the texture image should add the Am axis direction for converting the X axis and the Y axis.

[0026] (2) In the field of positioning, obtaining location information through texture images is an existing technology. Several examples of texture images are given below: A. The texture image in Example 1 is a QR code image, such as Figure 1 As shown, three of the four corners of the two-dimensional code (A, m, n) are provided with a positioning pattern (Position Detection Pattern) without information. The present invention calls the positioning pattern a corner anchor point. Assuming that the two-dimensional code is square, the adjacent corner anchor points YA or AX are connected in pairs, and the three corner anchor points form a 90-degree angle nAm, wherein the corner anchor point A is the vertex of the angle nAm, and the world position of the corner anchor point A is defined as the world position (XYZ) corresponding to the texture image. The line from A to the n corner anchor point is the positive direction of the Y axis, and the line from A to the m corner anchor point is the positive direction of the X axis. That is to say, the information surface of the two-dimensional code is the positive surface of the X axis and the Y axis. The length of the line nA in the real world is n, and the length of the line Am in the real world is m. If the two-dimensional code is a square, then m=n; B. The texture image in Example 2 is a QR code image, such as Figure 2 As shown, the three corner anchor points of the QR code are also independent QR code images. The image information parsed from the large and small QR codes and the position information (XYZ) of the three small QR codes are different. The position information of the A corner anchor point of the large QR code is the same as the position information of the small QR code that appears at the A corner anchor point. The image sizes (m, n) of the large and small QR codes are different, where the (m, n) of the large QR code is larger than the (m, n) of the small QR code. The position information of the small QR code of the n corner anchor point of the large QR code is equal to the Y position information of the A corner anchor point. A Add the position of n distance, the position information of the small QR code of the m corner anchor point of the large QR code is equal to the X of the A corner anchor point A Add m distance. If the player is too close to the texture image, and the captured image is not a complete large QR code and cannot parse the large QR code information, you can find any corner anchor point in the image, directly parse the small QR code corresponding to the corner anchor point, and obtain the image information corresponding to the small QR code; when the captured image can identify a complete large QR code, directly obtain the image information corresponding to the large QR code. In this way, whether it is a long-distance or close-distance texture image, the image information can be parsed through the large and small QR codes, ensuring that the calculated glasses position is more accurate or faster.

[0027] C. There are multiple patterns on the texture image, and the patterns can be bound to information content in advance, so the image information recorded by the patterns can be known through the arrangement order of the patterns.

[0028] (3) Smart glasses generally have multiple cameras for calculating spatial distance. In addition to obtaining the position of a single camera of the glasses, by calculating the distance and angle between the single camera of the glasses and a texture image, combined with the distance between the two cameras, the position of the center point of the two cameras of the glasses (XYZ) can be further calculated.

[0029] Since multiple cameras calculate their own positions independently, even if different cameras see different QR codes, the calculated world positions of the glasses should be the same in theory. In practice, due to various errors, the calculation results are usually different. Therefore, it is more accurate to take the average of the positions of multiple cameras, so a more accurate average position can be obtained by averaging the positions calculated by multiple cameras. If a pair of glasses has 2 cameras and each camera collects 2 QR codes, there are 4 average values. If 3 QR codes are seen, there are 6 average values. The more QR codes are seen, the more accurate the average world position of the glasses calculated by multiple cameras. Since the world position of each camera is different, when averaging the world positions calculated by multiple cameras, they should be converted to the common position of multiple cameras on the smart glasses (such as the middle of the smart glasses) and then the average calculation results should be calculated.

[0030] (4) The world position of the smart glasses in the large space is calculated based on the image information of the texture image: A. Calculate the world position T (X) of the smart glasses based on the one-dimensional space QR code T ,Z T ) method: Assume that the QR code in a single-dimensional space has only an X-axis but no Y-axis. The two ends of the QR code are corner anchor points A and m. Let the line connecting the two corner anchor points Am be the X-axis. The position of corner anchor point A (X A , Z = 0) and the length of the line Am is m, then ,like Figure 4 As shown, the vertical projection point of camera T on the X axis is X T , the line TA connecting the camera T and the corner anchor point A forms an angle with the vertical projection of the camera T on the X axis , the line Tm between camera T and corner anchor point m forms an angle with the vertical projection of camera T on the X axis , the two-dimensional world position of the smart glasses is derived through triangulation calculation (X T ,Z T ) is calculated as: (1) or (2) The angle and angle Obtained by: According to the field of view FOV (Field Of View) in the smart glasses camera specifications and the total number of pixels on the X axis of the screen X D , get the number of pixels per degree PPD (Pixels Per Degree), that is, PPD=X D / FOV; Assuming FOV=100º, X D =2000 pixels, then 2000 pixels divided by 100 degrees is 20PPD or 20 pixels per degree. A , the pixel position of the corner anchor point m is P m ,but:

[0031] (3); If the camera is perpendicular to the ground, let P0 be the pixel perpendicular to the ground; if the camera is perpendicular to the ceiling, let P0 be the pixel perpendicular to the ceiling. The P0 value can be obtained through the sensor of the smart glasses (such as a nine-axis geomagnetic chip); If the single-dimensional QR code has only a Y axis but no X axis, then X in the above formulas (1), (2), and (3) can be replaced by Y.

[0032] B. Calculate the world position T (X) of the smart glasses based on the undeformed QR code T ,Y T ,Z T ) method: like Figure 5 As shown in the figure, assuming that the image captured by the camera is 20x20 and the camera field of view FOV is 20 degrees, then there is only one pixel per degree (1PPD), and the center pixel position is P C , and the camera is not pointing vertically downward but at an angle, the pixel position vertical to the ground is P O The image information of the QR code includes the world position of the corner anchor point A in the large space and the image size (m, n). That is, the distance between the corner anchor point A and the X-axis corner anchor point m is m, and the distance between the corner anchor point A and the Y-axis corner anchor point n is n. Calculate the world position of the glasses (X T ,Y T ,Z T ), that is, to solve the pixel position P vertical to the ground O World position relative to corner anchor point A: Assume that the world position of the corner anchor point A is A(X A ,Y A ), the corresponding pixel position in the acquired image is P A (X PA ,Y PA ); the world position of the corner anchor point m is m(Xm ,Y m ), the corresponding pixel position in the acquired image is P m (X Pm ,Y Pm ); the world position of corner anchor point n is n(X n ,Y n ), the corresponding pixel position in the acquired image is P n (X Pn ,Y Pn ); The pixel position vertical to the ground or ceiling on the collected image obtained by the nine-axis geomagnetic chip of the smart glasses is P O (X Po ,Y Po ), we know from the spectacle camera specification that the center pixel position of the captured image is P C (X Pc ,Y Pc ); Assume that the image information of the QR code includes: the world position W of the corner anchor point A A =(1400,1000), m=n=300mm; then the world position of corner anchor point m is m=(1700,1000) and the world position of corner anchor point n is n=(1400,1300). The m and n in the world position only affect the X-axis or the Y-axis. like Figure 5 As shown, P C =(10,10), P O =(02,08), P A =(14,10), P m =(17,13), P n =(11,13), and m=n=4.24 pixels are obtained by the Pythagorean theorem. The QR code in the actual captured image is deformed due to perspective and is no longer a square but a trapezoid. To simplify the understanding, it is assumed that the camera T is at a very high position, and the QR code seen is basically not deformed.

[0033] Step 1: Find the pixel position P X You can use formula (1) to calculate the X axis of the world space T Location: It is known that the line Am from corner anchor point A to corner anchor point m is the positive direction of the world space X axis, P A =(14,10), P m =(17,13), its slope S X =3 / 3=1, then point P X The straight line formula of point A is: Y=S X X+B X , according to P A and Pm Two points are calculated to get B X =-4, then the world space X axis on the image is expressed by the formula: P =X P -4, Straight Line P O P X Perpendicular to the world space X axis, the intersection position is the pixel position P X , find P X (X PX ,Y PX ): X PX =(Y PO +S X X PO -B) / 2S X Y PX =S X X PX +B X X PX =(8+2+4) / 2=7, Y PX =S X X PX +B X =7-4=3, P X =(7,3), and Figure 5 The upper position is consistent.

[0034] Step 2: According to formula (1), we need to first calculate the distance P between two pixels on the X axis of the world space. X P A and P X P m , calculated using the Pythagorean theorem: P X (7,3)P A (14,10) distance = 9.90 pixels, P X (7,3)P m (17,13) distance = 14.14 pixels, then: =P X (7,3)P A (14,10) distance / PPD=9.90 degrees; =P X (7,3)P m (17,13) distance / PPD=14.14 degrees; Formula (1): X T =(X m Tan -X A Tan ) / ( Tan -Tan )= 723 mm; Formula (2): Z T =(X A -X m ) / (Tan -Tan )= 3876 mm ; Step 3: Find the pixel position P in the image Y You can use formula (3) to calculate the Y axis of the world space T Location: It is known that the line An from corner anchor point A to corner anchor point n is the Y axis of the world space, P A =(14,10), P n =(11,13), its slope S Y =-1, then according to formula (4), through point P Y Click P A The straight line is: Y=S Y X+B Y , according to P A and P n Two points are calculated to get B Y =24, the Y axis of the world space on the image is expressed by the formula: P = 24 -X P ; Line P O P Y Perpendicular to the world space Y axis, the intersection position is the pixel position P Y , find P Y (X PY ,Y PY ): X PY =(Y PO +S Y X PO -B Y ) / 2S Y , X PY =9;Y PY =S Y X PY +B Y =15, P Y =(9,15), and Figure 5 The upper position is consistent.

[0035] Step 4: According to formula (3), we need to first calculate the distance P between two pixels on the Y axis of the world space. Y P A and P Y P n , calculated using the Pythagorean theorem: PY (9,15)P A (14,10) distance = 7.07 pixels, P Y (9,15)P n (11,13) distance = 2.83 pixels, then: =P Y (9,15)P A (14,10) distance / PPD=7.07 degrees; =P Y (9,15)P n (11,13) distance / PPD=2.83 degrees; Formula (1): Y T =(Y m Tan -Y A Tan ) / ( Tan -Tan )= 1201 mm; Formula (2): Z T =(Y A -Y n ) / (Tan -Tan )= 4021 mm .

[0036] Since the Z calculated above T There is an error. You can calculate the average Z height of the second and fourth steps, and then the final average Z T =3949 mm; Step 5: Calculate the world position T(X T ,Y T ,Z T )=(723,1201,3949).

[0037] C. Give another method to get the angle formed by the normal line of the camera perpendicular to the ground or ceiling and the line connecting the camera to the corner anchor point A: like Figure 6 As shown, assuming that the angle formed by the normal line of the camera perpendicular to the ground or ceiling and the line connecting the camera to the corner anchor point A is , when looking down from the top of the QR code through the camera, the angle is 0°. If the camera gradually moves to the side and you look at the QR code from the side, the QR code you see through the camera is flat. is 90°, so we know the angle It is directly related to the direction of the camera looking at the corner anchor point A. Therefore, the angle formed by the normal line of the camera perpendicular to the ground or ceiling and the line connecting the camera to the corner anchor point A can be calculated according to the shape of the QR code seen through the camera. .

[0038] Figure 7 The following is a schematic diagram showing the changes in the positioning pattern corresponding to the corner anchor point A of the QR code when viewed from different angles through the camera. Assuming that the positioning pattern is a square, when the camera of the head-mounted display device looks at any positioning pattern of the three corner anchor points mAn of the QR code, the upper pattern will be farther away than the lower pattern. = 0°), otherwise at least one of the four corners of the square of the positioning pattern will have an angle greater than 90° and at least one of the four corners will have an angle less than 90°, except Figure 7 If one side in the middle is vertical, then both angles are 90°. From other pitch angles, there are 2 angles greater than 90° and 2 angles less than 90°. We can intercept the upper half of the positioning figure, measure the angles, and select the largest angle as , then Minus 90 0 It is the angle formed by the normal of the camera perpendicular to the ground or ceiling and the line connecting the camera to the corner anchor point: (4).

[0039] It can be seen that the three corner anchor points mAn of the QR code can be obtained by formula (4) by the angle formed by the normal line of the camera perpendicular to the ground or ceiling and the line connecting the camera to the corner anchor point: , , ). Figure 8 The two-dimensional code seen at four pitch angles. Each two-dimensional code has three corner anchor points mAn. The maximum angle of the upper half of each corner anchor point is As shown above, minus 90° is the angle formed by the normal line of the camera perpendicular to the ground or ceiling and the line connecting the camera to the corner anchor point: , , ). This method does not need to use the pixels and FOV of the image captured by the camera to convert the angle, which can be faster and more direct.

[0040] like Fig. 9 As shown, it is assumed that the pixel position P of the vertical ground or ceiling in the image captured by the camera is obtained through the nine-axis geomagnetic chip of the smart glasses. O , the angle formed by the normal of the camera perpendicular to the ground or ceiling and the line connecting the camera to the three corner anchor points mAn ( , , ) is quickly obtained using this method; like Fig.10 The following is a diagram showing the relationship between the camera T and the texture image when viewed from above (the height Z axis is not visible). T ,Y T ,Z T ) sees the texture image, the camera T(X T ,Y T ,Z T ) and the corner anchor point A(X A ,Y A ,Z A )、m(X m ,Y m ,Z m ) and n(X n ,Y n ,Z n ) is connected to H A , H m and H n , using the Pythagorean theorem to form the following three formulas: ; like Fig.11 The following is a schematic diagram showing the relationship between the camera T and the corner anchor point A from the side (height Z axis and H A as the horizontal axis), combined with Figure 7 And the angle formed by the normal of camera T perpendicular to the ground or ceiling and the line connecting camera T to the three corner anchor points mAn ( , , ), we get the following three formulas: ; The above six formulas are combined to calculate the world position T(X T ,Y T ,Z T ) function formula: (5); The above six formulas can be combined to obtain many different X T Y T Z T Formula (5).

[0041] Embodiment 1 Embodiment 1 of the present invention provides a positioning method for large-space smart glasses, which is suitable for a metaverse application scenario in which head-mounted display devices of players in a large space with multiple people can directly interact without training and reasoning of a central server. A number of different texture images are arranged on the ground, steps, desktops, walls and / or ceilings of the large space. By collecting images through any camera on the smart glasses, the texture image can be identified from the image collected by the camera T, and the image information corresponding to the texture image can be obtained. The world position of the camera T can be calculated by combining the image information. It is assumed that the texture image in the collected image is not deformed or has been distorted and calibrated. Figure 5 As shown, the following steps are included: Step 1. The image information can be directly obtained by parsing the texture image, or the ID of the texture image can be obtained, and the image information can be matched in a comparison table stored locally in the smart glasses according to the ID; The image information includes the world position (XYZ) and graphic size (m, n) of the image; three corner anchor points (A, m, n) are set on the texture image and form an angle nAm, wherein the corner anchor point A is the vertex of the angle nAm, and the world position of the corner anchor point A is defined as the world position (XYZ) of the image, the line An from A to the n corner anchor point is in the positive direction of the Y axis, the line Am from A to the m corner anchor point is in the positive direction of the X axis, the length of the line nA in the real world is n, and the length of the line Am in the real world is m; if the angle nAm is not 90 0 , then the image information includes the angle nAm, which is used to convert the X-axis and Y-axis; Step 2. Get the world position A(X) of the corner anchor point A according to the image information. A ,Y A ,Z A ), the world position m(X m ,Y m , Z m ), the world position n(X n ,Y n , Z n ), the X m =X A +m, Y m =Y A , X n =X A , Y n = Y A +n,Z m =Z n =Z A ; Get the pixel position P corresponding to the above corner anchor point mAn from the collected image A (X PA ,YPA ), P m (X Pm ,Y Pm ) and P n (X Pn ,Y Pn ); The nine-axis geomagnetic chip of the smart glasses is used to obtain the pixel position P of the vertical ground or ceiling in the image captured by the camera. O (X Po ,Y Po ); According to the field of view FOV in the smart glasses camera specifications and the total number of pixels on the X axis of the screen X D , get the number of pixels per degree PPD=X D / FOV; Step 3. Let the angle between the normal of the camera T perpendicular to the ground or ceiling and the line TA from the camera T to the corner anchor point A be , the angle formed by the normal of camera T perpendicular to the ground or ceiling and the line Tm connecting camera T to the corner anchor point m is , the angle formed by the normal of camera T perpendicular to the ground or ceiling and the line Tn connecting camera T to corner anchor point n is ; Let the line Am from corner anchor point A to corner anchor point m be the X axis of the world space, according to the pixel position P A and P m Calculate the slope S of the line Am X , then through point P X Point P A The X-axis is expressed by the straight line formula: Y=S X X+B X (6); The pixel position P A and P m Substituting into formula (6) we get B X ; Since the straight line P O P X Perpendicular to the X axis, pixel position P X The straight line P O P X Intersection with the X axis, perpendicular to the line P A P m The straight line P O P X The slope is -S X , expressed as: Y=-S X X+B XV (7); The pixel position PO (X Po ,Y Po ) into formula (7) to obtain B XV , so we can find the straight line P O P X The pixel position of the intersection with the X axis is P X (X PX ,Y PX ); Let An, the line connecting corner anchor point A to corner anchor point n, be the Y axis of the world space. A and P n Calculate the slope S of the connecting line An Y , then through point P Y Point P A The straight line is expressed by the formula: Y=S Y X+B Y (8); The pixel position P A or P n Substituting into formula (8) we get B Y； Since the straight line P O P Y Perpendicular to the Y axis, pixel position P Y The straight line P O P Y Intersection with the Y axis, perpendicular to the line P A P n The straight line P O P Y The slope is -S Y , expressed as: Y=-S Y X+B YV (9); The pixel position P O (X Po ,Y Po ) into formula (9) to obtain B YV , so we can find the straight line P O P Y The pixel position of the intersection with the Y axis is P Y (X PY ,Y PY ); Step 4. Use the Pythagorean theorem to calculate the pixel distance P X P A and P X P m ,but =P X P A / PPD =P X P m / PPD(3); Calculated by the following formula: (1); or (2); Use the Pythagorean theorem to calculate the pixel distance P Y P A and P Y P m ,but =P Y P A / PPD =P Y P n / PPD(3); Calculated by the following formula: (1); or (2); The two Z obtained above are T After averaging, we get the world position of camera T (X T ,Y T ,Z T ).

[0042] The process also includes step 5. averaging the world positions of multiple cameras T or converting them according to the known positional relationship of multiple cameras to obtain the world position of the head-mounted display device; the player's head-mounted display device broadcasts its own world position together with the current game status data to other players in the large space in real time.

[0043] Embodiment 2 Embodiment 2 of the present invention provides another positioning method for large-space smart glasses, which is suitable for a metaverse application scenario in which the head display devices of players in a large space with multiple people can interact directly without going through a central server inference. A number of different texture images are arranged on the ground, steps, desktops, walls and / or ceilings of the large space. By collecting images through any camera on the smart glasses, the texture image can be identified from the image collected by the camera T, and the image information corresponding to the texture image can be obtained. The world position of the camera T can be calculated in combination with the image information. The corner anchor point in the image has deformed the texture image due to the angle of the camera shooting, such as Fig. 9 As shown, the steps include: Step 1. The image information can be obtained by parsing the texture image, or the ID of the texture image can be obtained, and the image information can be matched in a comparison table locally stored in the smart glasses according to the ID; The picture information includes the world position of the A corner anchor point of the picture (X A ,Y A ,Z A ) and the world size (m, n) of the X-axis and Y-axis of the graphic; the texture image is provided with three corner anchor points (A, m, n) and forms an angle nAm, the corner anchor points are square positioning patterns, wherein the corner anchor point A is the vertex of the angle nAm, and the world position of the corner anchor point A is defined as the world position (X A ,Y A ,Z A ), the line An from A to the nth corner anchor point is in the positive direction of the Y axis, the line Am from A to the mth corner anchor point is in the positive direction of the X axis, the length of the line nA in the real world is n, and the length of the line Am in the real world is m; Step 2. Get the world position A(X) of the corner anchor point A according to the image information. A ,Y A ,Z A ), the world position m(X m ,Y m ,Z m ) and the world position n(X n ,Y n ,Z n ), the X n =X A +m,Y m =Y A , X n =X A ,Y n =Y A +n,Z m = Z n =Z A ; Step 3. Let the angle between the normal of camera T perpendicular to the ground or ceiling and the line connecting camera T to corner anchor point A be , the angle formed by the normal of camera T perpendicular to the ground or ceiling and the line connecting camera T to the corner anchor point m is , the angle formed by the normal of camera T perpendicular to the ground or ceiling and the line connecting camera T to the corner anchor point n is , respectively calculate the angles ( , , ): Measure the angle of the upper half of the image of the positioning pattern for each corner anchor point ( Figure 7 ), select the largest angle and set it as , then the angle formed by the normal of camera T perpendicular to the ground or ceiling and the line connecting camera T to the corner anchor point is: (4); Step 4. Use formula (4) to obtain the angle ( , , ), the world position of camera T (X T ,Y T ,Z T ): (5); The formula (5) is derived through the following steps: When the camera T(X T ,Y T ,Z T ) sees the texture image, assuming that the camera T(X T ,Y T ,Z T ) and the corner anchor point A(X A ,Y A ,Z A )、m(X m ,Y m ,Z m ) and n(X n ,Y n ,Z n ) is connected to H A , H m and H n , the angle formed by the normal of the camera T perpendicular to the ground or ceiling and the line connecting the camera T to the three corner anchor points mAn ( , , ), and use the Pythagorean theorem to get the following six formulas: ; The above six formulas are combined to calculate the world position T(X T ,Y T ,Z T ) function formula: (5); The above six formulas can be combined to obtain many different X T YT Z T Function formula (5).

[0044] The method also includes step 5. averaging the world positions of multiple cameras T or converting them according to the known positional relationship of multiple cameras to obtain the world position of the head-mounted display device; the player's head-mounted display device broadcasts its own world position together with the current game status data to other players in the large space in real time, and the interaction method includes existing communication interaction methods such as wireless communication mode and Internet mode.

[0045] The three corner anchor points of the texture image may be positioning patterns without information or independent small texture images. The image information corresponding to the small texture image includes the world position of the small texture image itself and the size of the small texture image.

[0046] The image information of the texture image also includes a space region name for distinguishing a virtual space in a large space.

[0047] The image information of the texture image further includes: distance information p or q to adjacent texture images, that is, the distance p to the adjacent texture image in the positive direction of the X axis and the distance q to the adjacent texture image in the positive direction of the Y axis.

[0048] The professionals should further realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented by electronic hardware, computer software or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to the function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0049] Specifically, each step of the method embodiment in the embodiment of the present application can be completed by the hardware integrated logic circuit and / or software instructions in the processor, and the steps of the method disclosed in the embodiment of the present application can be directly embodied as a hardware decoding processor, or a combination of hardware and software modules in the decoding processor. Optionally, the software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps in the above method embodiment in combination with its hardware.

[0050] Embodiment 3 Embodiment 3 of the present invention provides a head mounted display device, such as Fig.12As shown, the head display device 100 includes: a camera 101, a display 102, a memory 103 and a processor 104; The camera 101 is connected to the processor 104 and is used to scan the real environment where the user is located; The display 102 is connected to the processor 104 and is used to display the output content of the processor 104; The memory 103 is connected to the processor 104, and is used to store the computer program and transmit the program to the processor 104. In other words, the processor 104 can call and run the computer program from the memory 103 to implement the method in the first embodiment of the present application.

[0051] In some embodiments of the present application, the processor 104 may include but is not limited to: General-purpose processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc.

[0052] In some embodiments of the present application, the memory 103 includes, but is not limited to, volatile memory and / or non-volatile memory. The non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAMbus RAM (DRRAM).

[0053] In some embodiments of the present application, the computer program may be divided into one or more modules, which are stored in the memory 103 and executed by the processor 104 to complete the method of the first embodiment provided by the present application. The one or more modules may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the head display device 100.

[0054] It should be understood that the various components in the head display device 100 are connected via a bus system, wherein the bus system includes not only a data bus but also a power bus, a control bus and a status signal bus.

[0055] Embodiment 4 Embodiment 4 of the present invention further provides a computer storage medium on which a computer program is stored. When the computer program is executed by a computer, the computer can execute the method in the above-mentioned embodiment 1.

[0056] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A positioning method for large-space smart glasses, characterized in that: A plurality of different texture images are arranged on the bottom surface or ceiling of the large space, and an image is collected by any camera on the smart glasses, and the texture image is identified from the image collected by the camera T, and the image information corresponding to the texture image is obtained. The image information can be directly obtained by analyzing the texture image, or the ID of the texture image can be obtained, and the image information is matched in a comparison table locally stored in the smart glasses according to the ID; The image information includes the world position (XYZ) and graphic size (m, n) of the image; three corner anchor points (A, m, n) are arranged on the texture image and form an angle nAm, wherein the corner anchor point A is the vertex of the angle nAm, the world position of the corner anchor point A is defined as the world position (XYZ) of the image, the line An from A to the n corner anchor point is in the positive direction of the Y axis, the line Am from A to the m corner anchor point is in the positive direction of the X axis, the length of the line nA in the real world is n, and the length of the line Am in the real world is m; if the angle nAm is not 90 0 , then the image information also includes the angle nAm, which is used to convert the X-axis and Y-axis; According to the image information, we can get: the world position A(X A ,Y A ,Z A ), the world position m(X m ,Y m, Z m ) and the world position n(X n ,Y n ,Z n ), the X m =X A +m, Y m =Y A , X n =X A , Y n = Y A +n,Z m =Z n =Z A ; Get the pixel position P corresponding to the above corner anchor point mAn from the collected image A (X PA ,Y PA ), P m (X Pm ,Y Pm ) and P n (X Pn ,Y Pn ); The nine-axis geomagnetic chip of the smart glasses is used to obtain the pixel position P of the vertical ground or ceiling in the image captured by the camera. O (X Po ,Y Po ); Let the normal of camera T perpendicular to the bottom or ceiling and the line H connecting camera T to corner anchor point A be A The angle formed is , the line H between the normal of the camera T perpendicular to the bottom or ceiling and the camera T to the corner anchor point m m The angle formed is , the line H between the normal of the camera T perpendicular to the bottom or ceiling and the camera T to the corner anchor point n n The angle formed is ; The world position of camera T can be obtained through geometric calculation (X T ,Y T ,Z T ).

2. A method for positioning large-space smart glasses according to claim 1, characterized in that: The world position (X) of the camera T can be obtained by geometric calculation. T ,Y T ,Z T ), assuming that the texture image in the captured image is not deformed or has been corrected, the field of view FOV of the camera and the total number of pixels on the X axis of the screen X D , get the number of pixels per degree PPD=X D / FOV; the specific calculation steps are as follows: Step 1. Let the line Am from corner anchor point A to corner anchor point m be the X axis of the world space, and according to the pixel position P A and P m Calculate the slope S of the line Am X , then through point P X Point P A The X-axis is expressed by the straight line formula: Y=S X X+B X (6); The pixel position P A or P m Substituting into formula (6) we get B X ; Since the straight line P O P X Perpendicular to the X axis, pixel position P X The straight line P O P X Intersection with the X axis, perpendicular to the line P A P m The straight line P O P X The slope is -S X , expressed as: Y=-S X X+B XV (7); The pixel position P O (X Po ,Y Po ) into formula (7) to obtain B XV , so we can find the straight line P O P X The pixel position of the intersection with the X axis is P X (X PX ,Y PX ); Let An, the line connecting corner anchor point A to corner anchor point n, be the Y axis of the world space. A and P n Calculate the slope S of the connecting line An Y , then through point P Y Point P A The straight line is expressed by the formula: Y=S Y X+B Y (8); The pixel position P A or P n Substituting into formula (8) we get B Y； Since the straight line P O P Y Perpendicular to the Y axis, pixel position P Y The straight line P O P Y Intersection with the Y axis, perpendicular to the line P A P n The straight line P O P Y The slope is -S Y , expressed as: Y=-S Y X+B YV (9); The pixel position P O (X Po ,Y Po ) into formula (9) to obtain B YV , so we can find the straight line P O P Y The pixel position of the intersection with the Y axis is P Y (X PY ,Y PY ); Step 2. Use the Pythagorean theorem to calculate the pixel distance P X P A and P X P m ,but =P X P A / PPD =P X P m / PPD(3); Calculated by the following formula: （1）； or (2); Use the Pythagorean theorem to calculate the pixel distance P Y P A and P Y P m ,but =P Y P A / PPD =P Y P n / PPD(3); Calculated by the following formula: （1）； or (2); Step 3. Add the two Z obtained above T After averaging, we get the world position of camera T (X T ,Y T ,Z T ).

3. The positioning method of large-space smart glasses according to claim 1, characterized in that: The world position (X) of the camera T can be obtained by geometric calculation. T ,Y T ,Z T ), assuming that the texture image in the captured image has been deformed due to the camera angle problem, calculate the angle ( , , ): The three corner anchor points are all square positioning images. The angle of the upper half image of the positioning pattern of each corner anchor point is measured, and the largest angle is selected as , then the angle formed by the normal of camera T perpendicular to the ground or ceiling and the line connecting camera T to the corner anchor point is: （4）； The angle ( , , ); First, use the Pythagorean theorem to get the following six formulas: ； Then the above six formulas are combined to calculate the world position T(X T ,Y T ,Z T ) function formula: （5）。 4. The positioning method of large-space smart glasses according to claim 1, characterized in that: The world positions of multiple cameras T are averaged or converted according to the known positional relationship of multiple cameras to obtain the world position of the head-mounted display device / smart glasses; the player's head-mounted display device / smart glasses broadcasts its own world position together with the current game status data to other players in the large space in real time.

5. A method for positioning large-space smart glasses according to any one of claims 1 to 3, characterized in that: The bottom surface of the large space refers to the ground, steps, walls or tabletop of the large space.

6. The positioning method of large-space smart glasses according to claim 1, characterized in that: The three corner anchor points of the texture image may be positioning patterns without information or independent small texture images. The image information corresponding to the small texture image includes the world position of the small texture image itself and the size of the small texture image.

7. The positioning method of large-space smart glasses according to claim 1, characterized in that: The image information of the texture image also includes a space region name for distinguishing a virtual space in a large space.

8. The method for positioning large-space smart glasses according to claim 1, characterized in that: The image information of the texture image further includes: distance information p or q to adjacent texture images, that is, the distance p to the adjacent texture image in the positive direction of the X axis and the distance q to the adjacent texture image in the positive direction of the Y axis.

9. A head mounted display device, characterized in that: The head display device includes a camera, a display, a memory and a processor; The camera is connected to the processor and is used to scan the real environment where the user is located; The display is connected to the processor and is used to display the output content of the processor; The memory is connected to the processor and is used to store the computer program; The processor is used to execute the computer program to implement the positioning method of large-space smart glasses in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the computer program implements the positioning method of large-space smart glasses according to any one of claims 1 to 8.

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