Plane positioning algorithm based on image generation

Abstract

The invention belongs to the technical field of Internet of Things, discloses a plane positioning algorithm based on image generation, and aims to improve the precision of plane positioning. The algorithm is based on an image generation technology, and the plane coordinates of the positioned object are calculated by using the distance value between the anchor point and the positioned object. The algorithm is suitable for a scene with three or more anchor points. The algorithm does not depend on a specific anchor point position or a specific sensor type. The method has the characteristics of high universality, low arrangement cost and high positioning precision.

Description

Technical Field

[0001] The invention belongs to the technical field of Internet of Things, and in particular relates to a plane positioning algorithm based on image generation. Background Art

[0002] With the development of the times, the popularity of distance measurement sensors is increasing. This has led to more and more products equipped with hardware such as Bluetooth, Star Flash, and ultra-wideband that can be used for distance measurement, providing users with more usage scenarios based on positioning services. For example, in the automotive field, functions such as non-sensing entry and non-sensing start can be achieved by locating smart keys; for another example, indoor navigation can be achieved by locating smartphones;

[0003] However, no matter how sensor technology develops, errors always exist objectively. Manufacturers also need to balance the cost and performance of sensors. Therefore, in order to provide users with a better experience, an algorithm that can overcome measurement errors and obtain higher positioning accuracy is currently needed. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a plane positioning algorithm based on image generation. The present invention aims to solve the problems of high deployment cost and low positioning accuracy of existing sensors and positioning methods.

[0005] The present invention provides a plane positioning algorithm based on image generation, comprising the following steps:

[0006] S1. Determine the scale and pixel size of the generated image according to the required coordinate granularity and positioning range;

[0007] S2. Determine the error range of the ranging result according to the characteristics of the anchor point, the application scenario and the actual measurement effect;

[0008] S3. Set the ranging anchor point and obtain the ranging value between each ranging anchor point and the located object;

[0009] Among them, the coordinates of each ranging anchor point on the plane are known;

[0010] S4. Based on the distance measurement value obtained in step S3, using image generation technology, on an empty canvas, using a transparent color, with the coordinates of each anchor point as the center of the circle, with the distance measurement value of the corresponding anchor point minus the maximum positive error as the inner circle radius, and with the distance measurement value of the corresponding anchor point minus the maximum negative error as the outer circle radius, draw a circle respectively, to obtain an intersection graph corresponding to the set of data;

[0011] S5. Identify the area with the highest degree of overlap in the intersection graph, that is, obtain the regional positioning map of the located object at the time of the round of data collection;

[0012] S6. All the regional positioning maps calculated at different times within a period of time are assigned different weights and then superimposed to obtain the position probability map of the measured object within the period of time;

[0013] The darker the color of the pixel in the position probability map, the greater the probability that the located object is located at that position;

[0014] S7. extracting the plane point set corresponding to the pixel point with the highest probability in the position probability map;

[0015] S8. Calculate the centroid coordinates of the extracted plane point set, which are the precise coordinates of this positioning;

[0016] S9. Repeat S3-S8.

[0017] Furthermore, in step S3, the number of ranging anchor points is at least three.

[0018] Furthermore, in step S3, the ranging anchor point and the measured object should be approximately located in the same plane.

[0019] Beneficial effects:

[0020] The present invention is based on image generation technology and uses the distance between the anchor point and the located object to calculate the plane coordinates of the located object. The algorithm is applicable to scenes with three or more anchor points. The algorithm of the present invention does not rely on specific anchor point positions or specific sensor types, and has the characteristics of high versatility, low deployment cost, and high positioning accuracy.

[0021] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A flow chart of a plane positioning algorithm based on image generation according to the present invention;

[0023] Figure 2 The diagram is a schematic diagram of the anchor point arrangement and the located object in the case of three anchor points, where the blue ones are anchor points and the green ones are located objects;

[0024] Figure 3 It is the intersection graph generated by a set of anchor point data in the case of three anchor points;

[0025] Figure 4 Reason Figure 3 The extracted region location map;

[0026] Figure 5Location probability maps generated for multiple regional location maps;

[0027] Figure 6 For Figure 5 Schematic diagram of the selected plane point set, marked by red pixels;

[0028] Figure 7 for Figure 6 Schematic diagram of the centroid of the plane point set shown, marked by red pixels. DETAILED DESCRIPTION

[0029] In order to make the technical solutions, advantages and purposes of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the protection scope of this application.

[0030] like Figure 1 As shown, the present invention provides a plane positioning algorithm based on image generation, comprising the following steps:

[0031] S1. Determine the scale and pixel size of the generated image according to the required coordinate granularity and positioning range;

[0032] S2. Determine the error range of the ranging result according to the characteristics of the anchor point, the application scenario and the actual measurement effect;

[0033] S3. Set the ranging anchor point and obtain the ranging value between each ranging anchor point and the located object;

[0034] The number of ranging anchor points is at least three, and the ranging anchor points and the measured object should be approximately located in the same plane, and the coordinates of each ranging anchor point on the plane are known;

[0035] S4. Based on the distance measurement value obtained in step S3, using image generation technology, on an empty canvas, using a transparent color, with the coordinates of each anchor point as the center of the circle, with the distance measurement value of the corresponding anchor point minus the maximum positive error as the inner circle radius, and with the distance measurement value of the corresponding anchor point minus the maximum negative error as the outer circle radius, draw a circle respectively, to obtain an intersection graph corresponding to the set of data;

[0036] S5. Identify the area with the highest degree of overlap in the intersection graph, that is, obtain the regional positioning map of the located object at the time of the round of data collection;

[0037] S6. All the regional positioning maps calculated at different times within a period of time are assigned different weights and then superimposed to obtain the position probability map of the measured object within the period of time;

[0038] The darker the color of the pixel in the position probability map, the greater the probability that the located object is located at that position;

[0039] S7. extracting the plane point set corresponding to the pixel point with the highest probability in the position probability map;

[0040] S8. Calculate the centroid coordinates of the extracted plane point set, which are the precise coordinates of this positioning;

[0041] S9. Repeat S3-S8.

[0042] Example 1

[0043] An implementation method of the algorithm of the present invention is described below by taking a scenario with three-star flash anchor point, a positioning range of 10m*10m, and a positioning granularity of 1cm as an example. Figure 2 The diagram shows the layout of anchor points and the located objects, where the blue ones are anchor points and the green ones are located objects. The algorithm execution is as follows:

[0044] S1. Take the scale 1 pixel = 1 cm and the canvas size 1000 pixel * 1000 pixel.

[0045] S2. According to the characteristics of star flashes, the error of the ranging data is taken as -40cm to +40cm.

[0046] S3. Obtain the ranging value of each ranging anchor point with a period of 10 ms.

[0047] S4. Use OpenGL to draw the intersection graph corresponding to this group of data. The intersection graph of this embodiment is as follows Figure 3 shown.

[0048] S5. Using OpenGL from the intersection graph ( Figure 3 ) to extract the regional positioning map, the regional positioning map of this embodiment is as follows Figure 4 shown.

[0049] S6. Using OpenGL, all 50 regional positioning maps generated in the last 500ms are merged to obtain the latest position probability map. The position probability map of this embodiment is as follows: Figure 5 shown.

[0050] S7. Obtain the plane point set corresponding to the pixel with the highest probability in the probability map, Figure 6 Shows from Figure 5 The plane point set selected in is marked by red pixels.

[0051] S8. Calculate the centroid of the plane point set to obtain the positioning result. Figure 7 Shown Figure 6 The centroid of the midplane point set is marked by the red pixel.

[0052] S9. Repeat S3-S8.

[0053] It is hereby declared that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should be included in the scope of the claims of the present invention.

Claims

1. A plane positioning algorithm based on image generation, characterized in that: The following steps are involved: S1. Determine the scale and pixel size of the generated image according to the required coordinate granularity and positioning range; S2. Determine the error range of the ranging result according to the characteristics of the anchor point, the application scenario and the actual measurement effect; S3. Set the ranging anchor point and obtain the ranging value between each ranging anchor point and the located object; Among them, the coordinates of each ranging anchor point on the plane are known; S4. Based on the distance measurement value obtained in step S3, using image generation technology, on an empty canvas, using a transparent color, with the coordinates of each anchor point as the center of the circle, with the distance measurement value of the corresponding anchor point minus the maximum positive error as the inner circle radius, and with the distance measurement value of the corresponding anchor point minus the maximum negative error as the outer circle radius, draw a circle respectively, to obtain an intersection graph corresponding to the set of data; S5. Identify the area with the highest degree of overlap in the intersection graph, that is, obtain the regional positioning map of the located object at the time of the round of data collection; S6. All the regional positioning maps calculated at different times within a period of time are assigned different weights and then superimposed to obtain the position probability map of the measured object within the period of time; The darker the color of the pixel in the position probability map, the greater the probability that the located object is located at that position; S7. extracting the plane point set corresponding to the pixel point with the highest probability in the position probability map; S8. Calculate the centroid coordinates of the extracted plane point set, which are the precise coordinates of this positioning; S9. Repeat S3-S8.

2. The plane positioning algorithm based on image generation according to claim 1, characterized in that: In step S3, the number of ranging anchor points is at least three.

3. The plane positioning algorithm based on image generation according to claim 2, characterized in that: In step S3, the ranging anchor point and the measured object should be approximately located in the same plane.

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