A fusion positioning method and system in a complex scene in a factory

Abstract

The application discloses a kind of complex scene under factory area fusion positioning method and system, belong to mobile positioning technical field, applied to mobile device, comprising: judge the number of received position data corresponding ultra-wideband UWB positioning base station whether greater than or equal to first preset value;When the number of UWB positioning base station is greater than or equal to first preset value, the first confidence of position data is calculated according to the signal-to-noise ratio of position data and the signal strength of position data;When the first confidence is greater than the number of UWB positioning base station corresponding to the position data of second preset value is greater than or equal to 3, select the first confidence of the first 3 position data;The observation coordinates of the particle group of the particle filter of mobile device and the second confidence corresponding to observation coordinates are calculated;Wherein, particle group includes N particles, and the second confidence is the average of the first confidence of the first 3 position data.

Description

Technical Field

[0001] This invention belongs to the field of mobile positioning technology, and in particular relates to a fusion positioning method and system for complex scenarios within a factory area. Background Technology

[0002] With the development of intelligent manufacturing technology, the demand for indoor positioning is increasing, and the requirements for positioning accuracy are also getting higher. In some implementations, high-precision indoor positioning can be achieved through ultra-wideband (UWB) positioning technology. For example, the location information of mobile devices can be obtained through UWB positioning technology.

[0003] For mobile devices equipped with UWB positioning capabilities, the mobile device can receive location data signals sent by multiple UWB positioning base stations and calculate the location information of the mobile device based on the received location data.

[0004] However, due to the complexity of indoor environments, there may be obstructions such as walls and materials between UWB positioning base stations and mobile devices, which can affect the transmission and reception of location data signals, resulting in inaccurate UWB positioning. Summary of the Invention

[0005] This application provides a fusion positioning method and system for complex scenarios within a factory area. Based on the signal strength and signal-to-noise ratio of the location data signal sent by the UWB positioning base station, this application can calculate the observation coordinates of the particle swarm of the particle filter and the real-time confidence of the observation coordinates. In other words, the confidence of the particle swarm observation coordinates is related to the degree of obstruction of the location data signal, which can improve the accuracy and stability of mobile device positioning.

[0006] The first objective of this invention is to provide a fusion positioning method for complex scenarios within a factory area, comprising:

[0007] Determine whether the number of UWB positioning base stations corresponding to the received location data is greater than or equal to a first preset value;

[0008] When the number of UWB positioning base stations is greater than or equal to the first preset value, the first confidence level of the location data is calculated based on the signal-to-noise ratio and signal strength of the location data.

[0009] When the number of UWB positioning base stations corresponding to location data with the first confidence level greater than the second preset value is greater than or equal to 3, the top 3 location data with the highest first confidence level are selected; (Explanation of this step: First, the first confidence level is thresholded using the second preset value (also known as the confidence threshold), discarding the first confidence level below the threshold and retaining the first confidence level above the threshold. Then, the three highest first confidence levels are selected from the retained first confidence levels. Finally, the location data with these three first confidence levels are selected;)

[0010] Calculate the observation coordinates of the particle swarm of the particle filter of the mobile device and the second confidence level corresponding to the observation coordinates; wherein the particle swarm includes N particles, and the second confidence level is the average of the first confidence levels of the first 3 position data.

[0011] Preferably, it further includes:

[0012] When the number of UWB positioning base stations is less than the first preset value, or when the number of UWB positioning base stations corresponding to the location data with the first confidence level greater than the second preset value is less than 3, the observation coordinates of the particle swarm and the second confidence level are set to the first value.

[0013] Preferably, it further includes:

[0014] When the observed coordinates of the particle swarm and the second confidence level are not set to the first value, the observed coordinates of the N particles are generated based on the second confidence level.

[0015] Calculate the third confidence level of the N particles based on the Euclidean distance;

[0016] Based on the third confidence level, the particle swarm is resampled to obtain a new particle swarm.

[0017] Based on the new particle swarm, the center coordinates of the particle swarm are calculated to obtain the position coordinates of the mobile device.

[0018] Preferably, resampling the particle swarm further includes:

[0019] Based on the third confidence level, the N particles are sorted.

[0020] Filter out particles with a confidence level less than a third preset value from the N particles to obtain a filtered particle swarm.

[0021] Randomly copy particles from the filtered particle swarm back to the filtered particle swarm;

[0022] When the number of particles in the filtered particle swarm is equal to the number of particles in the particle swarm, the new particle swarm is obtained.

[0023] Preferably, before determining whether the number of UWB positioning base stations corresponding to the received location data is greater than or equal to a first preset value, the method further includes:

[0024] Establish a kinematic model;

[0025] Initialize the particle swarm;

[0026] The location data and IMU data measured by the inertial measurement unit (IMU) are acquired; wherein the IMU data includes the acceleration and angular velocity of the mobile device.

[0027] The location data and the IMU data are synchronized in time and frequency.

[0028] The IMU data is then filtered.

[0029] Based on the kinematic model and the IMU data, the position coordinates of the N particles are predicted.

[0030] The second objective of this invention is to provide a fusion positioning system for complex scenarios within a factory area, used to implement the aforementioned fusion positioning method for complex scenarios within a factory area. The fusion positioning system includes: a UWB tag, a UWB base station, an IMU, and a processing module.

[0031] The UWB tag is used to receive location data sent by the UWB positioning base station;

[0032] The IMU is used to measure the acceleration and angular velocity of the mobile device;

[0033] The processing module is used to process the location data, the acceleration, and the angular velocity to obtain the position coordinates of the mobile device.

[0034] A third objective of this invention is to provide a mobile device comprising: a processor and a memory;

[0035] The memory stores computer-executed instructions;

[0036] The processor executes the computer execution instructions stored in the memory, causing the mobile device to perform the aforementioned fusion positioning method in complex scenarios within the factory area.

[0037] A fourth objective of this invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned fusion positioning method for complex scenarios within a factory area.

[0038] The advantages and positive effects of this invention are:

[0039] This invention calculates the observation coordinates and real-time confidence of the particle swarm of a particle filter based on the signal strength and signal-to-noise ratio of the location data signal received from the UWB positioning base station. In other words, the confidence of the particle swarm observation coordinates is related to the degree of obstruction of the location data signal, which can improve the accuracy and stability of mobile device positioning. Attached Figure Description

[0040] Figure 1 This is a schematic diagram illustrating an application scenario provided in the embodiments of this application;

[0041] Figure 2 A flowchart of an indoor positioning method provided in an embodiment of this application;

[0042] Figure 3 This is a flowchart of the UWB post-processing provided in the embodiments of this application;

[0043] Figure 4 This is a flowchart of particle swarm resampling provided in an embodiment of this application;

[0044] Figure 5 A comparison diagram of positioning results provided for embodiments of this application. Detailed Implementation

[0045] To facilitate a clear description of the technical solutions in the embodiments of this application, some technical terms and technologies involved in the embodiments of this application will be briefly introduced below.

[0046] 1. Particle Filter

[0047] Particle filters can be applied to indoor positioning. They can estimate the position and pose of an object based on UWB and / or IMU104 measurement data, and represent the position of the object using a set of randomly sampled particles.

[0048] For example, Figure 1 This is a schematic diagram illustrating an application scenario provided in an embodiment of this application.

[0049] Figure 1 The scenario shown may include mobile device 101, UWB positioning base station 102-A, UWB positioning base station 102-B, and UWB positioning base station 102-C. It is understood that... Figure 1 The three UWB positioning base stations shown are for illustrative purposes only. In actual applications, the number of UWB positioning base stations is usually greater than or equal to three. This application does not specifically limit the number of UWB positioning base stations.

[0050] The mobile device 101 may be equipped with a UWB tag 103, which can be used to receive location data sent by UWB positioning base stations 102-A, 102-B, and / or 102-C. However, since there are obstructions 100 between the mobile device 101 and the UWB positioning base stations 102-A, 102-B, and 102-C, the location data signal may be attenuated due to the obstruction when the UWB positioning base stations send the location data signal to the mobile device 101. For example, the signal strength of the location data signal may decrease, and the signal-to-interference plus noise ratio (SNR) may decrease.

[0051] In practical applications, due to the complex indoor environment (such as factories), there may be multiple obstructions in UWB positioning base stations and mobile devices, such as walls, materials, and other production equipment. Therefore, the location of mobile devices located by UWB positioning technology may be inaccurate.

[0052] In some implementations, the accuracy of mobile device positioning can be improved when there are obstructions indoors by combining UWB positioning technology and inertial measurement unit (IMU) measurement. This involves fusing the position data obtained by UWB positioning technology with the acceleration and angular velocity measured by IMU.

[0053] However, the degree of obstruction of location data signals depends on factors such as the material of the obstruction, the distance between the UWB positioning base station and the obstruction, and the angle. In other words, the degree of obstruction varies between location data signals transmitted by different UWB positioning base stations; location data signals with a high degree of obstruction have low confidence, while those with a low degree of obstruction have high confidence. Therefore, when fusing location data, acceleration, and angular velocity to calculate the location of a mobile device, if the mobile device's location largely depends on location data with low confidence, the positioning of the mobile device may still be inaccurate.

[0054] In view of this, embodiments of this application provide an indoor positioning method. This method can calculate the observation coordinates of the particle swarm of the particle filter and the real-time confidence of the observation coordinates based on the signal strength and signal-to-noise ratio of the location data signal sent by the UWB positioning base station. In other words, the confidence of the particle swarm observation coordinates is related to the degree of obstruction of the location data signal. This can improve the accuracy and stability of mobile device positioning.

[0055] Below, in conjunction with Figures 2 to 4 The indoor positioning method provided in the embodiments of this application will be described.

[0056] S201. Initialize the positioning system of the mobile device.

[0057] In this embodiment of the application, the initialization of the positioning system of the mobile device may include establishing a kinematic model and initializing the particle swarm of the particle filter.

[0058] Among them, the kinematic model is used to describe the motion information of the mobile device, which may include the position and pose of the mobile device.

[0059] The particle swarm of the particle filter can include N particles, and the initial value of the particle swarm can be determined based on the position data obtained from the first measurement by the UWB positioning base station and the acceleration and angular velocity obtained from the first measurement by the IMU.

[0060] S202. The mobile device acquires data sent by the UWB positioning base station and data measured by the IMU.

[0061] The UWB tag installed on the mobile device can be used to receive location data signals transmitted by M UWB positioning base stations. For ease of description, the location data signals received by the mobile device from the M UWB positioning base stations will be referred to as UWB data. In addition, the UWB tag can also be used to receive base station IDs, signal strengths, and signal-to-noise ratios transmitted by the M UWB positioning base stations.

[0062] For example, at time j, the mobile device receives a signal from the a-th USB base station with base station ID number a and signal strength q. a,j The signal-to-noise ratio is SNR. a,j .

[0063] The mobile device may also be equipped with an IMU, which can be used to measure the acceleration and angular velocity of the mobile device. It is understood that the IMU can be a six-axis IMU or a nine-axis IMU. This application embodiment does not limit the specific type of IMU. In addition, for ease of description, the acceleration and angular velocity measured by the IMU received by the mobile device will be referred to as IMU data.

[0064] S203. Perform time and frequency synchronization on UWB data and IMU data.

[0065] In this embodiment of the application, time synchronization can refer to synchronizing UWB data and IMU data to the same time as the mobile device processor. For example, if the mobile device processor time is 23:10:59:30, the UWB data time is 23:10:59:25, and the IMU data time is 23:10:59:27, then the time of UWB data and the time of IMU data are both synchronized to 23:10:59:30.

[0066] In this embodiment, frequency synchronization can refer to adjusting the frequency of UWB data and the frequency of IMU data to a preset frequency. For example, if the preset frequency is 50Hz, the frequency of UWB data is 25Hz, and the frequency of IMU data is 100Hz, then both the time of UWB data and the frequency of IMU data are adjusted to 50Hz. In a possible implementation, frequency synchronization can be achieved through data frame interpolation.

[0067] S204. Filter the IMU data.

[0068] It is understandable that, since IMU data is instantaneous, it may contain noise signals. Therefore, mobile devices can use filters to filter the IMU data and remove noise signals. The filter can be a Butterworth low-pass filter, and in possible implementations, other filters may also be used; this application does not specifically limit this.

[0069] S205, Predict the position coordinates of each particle in the particle swarm.

[0070] Specifically, the mobile device can predict the position coordinates of each particle in the particle swarm based on the aforementioned kinematic model and IMU data. The position coordinates of the i-th particle at time j can be (xp... i,j ,yp i,j ,zp i,j ).

[0071] S206, UWB Data Post-processing

[0072] Methods for post-processing UWB data can include, for example... Figure 3 As shown, the specific steps are as follows.

[0073] S301. The mobile device determines whether the number of UWB positioning base stations corresponding to the UWB data that can be received is greater than or equal to a first preset value.

[0074] The first preset value can be a value greater than or equal to 3; the mobile device receiving UWB data can mean that the mobile device can parse the location coordinates of the mobile device based on the UWB data.

[0075] S302. When the number of UWB positioning base stations corresponding to the receivable UWB data is greater than or equal to the first preset value, according to the signal-to-noise ratio (SNR)... a,j and signal strength q a,j The confidence level of the UWB data of each base station is calculated as follows.

[0076]

[0077] Among them, Conf aUsed to represent the confidence level of the a-th base station at time t.

[0078] S303. Determine whether the number of base stations with a confidence level greater than the second preset value is greater than or equal to 3.

[0079] The second preset value can be in the range of [0.5, 1].

[0080] S304. When the number of base stations with a confidence level greater than the second preset value is greater than or equal to 3, select the UWB data corresponding to the three UWB positioning base stations with the highest confidence levels.

[0081] S305. Calculate the observation coordinates and confidence level of the particle swarm.

[0082] The embodiments of this application can calculate the observation coordinates (xg) of the particle swarm at time j based on the triangulation algorithm. j ,yg j ,zg j ), the confidence level of the observed coordinates Conf′ j This can include the average confidence level of the three base stations.

[0083] S306. When the number of UWB positioning base stations corresponding to the UWB data that can be received is less than a first preset value, or when the number of base stations with a confidence level greater than a second preset value is less than 3, the mobile device sets both the observation coordinates of the particle swarm and the confidence level of the observation coordinates to the first value. The first value can be N / A, or it can be 0 or null, etc. This application embodiment does not specifically limit this.

[0084] Thus, based on the above steps S301 to S306, the observation coordinates of the particle swarm and the confidence level of the observation coordinates can be obtained.

[0085] S207. After completing the UWB data post-processing, determine whether the observed coordinates of the particle swarm are the first value.

[0086] S208. When the data obtained from UWB post-processing is not the first value, generate the observation coordinates of each particle.

[0087] Assuming that the observation error of the particle swarm's observation coordinates follows a normal distribution, when calculating the difference between the observation coordinates of each particle and the particle swarm, the confidence level can be introduced into the observation coordinates corresponding to the particle swarm to obtain the observation coordinates corresponding to each particle.

[0088] xg i,j ′=xg j ×(1+random(-(1-Conf′ j ),(1-Conf′j))

[0089] ygi,j ′=yg j ×(1+random(-(1-Conf′ j ),(1-Conf′ j ))

[0090] zg i,j ′=zg j ×(1+random(-(1-Conf′ j ),(1-Conf′ j ))

[0091] Among them, (xg i,j ′,yg i,j ′,zg i,j ′) is used to represent the observation coordinates of the i-th particle at time j.

[0092] S209. Calculate the confidence level of each particle.

[0093] In this embodiment of the application, the confidence level of each particle can be calculated based on the Euclidean distance. The Euclidean distance can refer to the Euclidean distance between the observation coordinates of each particle and the observation coordinates of the particle swarm. The calculation method is as follows.

[0094]

[0095] Among them, P i,j Used to represent the Euclidean distance between the observed coordinates of the i-th particle and the observed coordinates of the particle swarm at time t, it can be understood that P i,j The smaller the value, the higher the confidence level of the i-th particle.

[0096] According to the Euclidean distance P i,j The confidence level of each particle is calculated using the following formula.

[0097]

[0098] Among them, R i,j It can be used to represent the confidence level of the i-th particle at time j. It should be noted that the sum of the confidence levels of all particles in the swarm is equal to 1.

[0099] S210. Based on the confidence level of each particle, resample the particle swarm.

[0100] Particle swarm resampling methods can be as follows: Figure 4 As shown, the specific steps are as follows.

[0101] S401. Sort the particles in the particle swarm based on the confidence level of each particle.

[0102] In this embodiment of the application, the sorting method can be from smallest to largest or from largest to smallest, and this embodiment of the application does not specifically limit it.

[0103] S402, Filter particles with a confidence level less than the third preset value.

[0104] The third preset value can be any number between [0,1), such as 0.7.

[0105] S403, Randomly copy particles from the filtered particle swarm.

[0106] Mobile devices can randomly copy particles from the filtered particle swarm to another filtered particle swarm, generating a new particle swarm.

[0107] S404. Determine whether the number of particles in the new particle swarm is equal to the number of particles in the original particle swarm.

[0108] Particle swarm resampling is complete when the number of particles in the new swarm is equal to the number of particles in the original swarm.

[0109] When the number of particles in the new particle swarm is not equal to the number of particles in the original particle swarm, the mobile device can execute step S403 until the number of particles in the new particle swarm is equal to the number of particles in the original particle swarm.

[0110] In this way, the resampling of the particle swarm can be completed based on steps S401 to S404.

[0111] S211. Calculate the coordinates of the particle swarm center, i.e., the coordinates of the mobile device.

[0112] After particle swarm resampling is completed, or when the third predicted value of the data obtained from UWB post-processing is obtained, the mobile device can calculate the center coordinates of the particle swarm based on the position coordinates of each particle and the confidence level of each particle. The calculation method is as follows.

[0113]

[0114]

[0115]

[0116] in, It can be used to represent the coordinates of mobile devices.

[0117] Understandably, after obtaining the coordinates of the mobile device through steps S201 to S211, the mobile device can continue to execute steps S202 to S211 to obtain the coordinates of the mobile device at the next moment.

[0118] Thus, based on the above indoor positioning method, in complex indoor environments where the data signals sent by UWB positioning base stations are obstructed to varying degrees, the confidence level of particle swarm observation coordinates can be adjusted in real time based on the signal strength and signal-to-noise ratio of the received location data signals sent by UWB positioning base stations, so as to filter out particle swarm observation coordinates with higher confidence levels, thereby improving the accuracy and stability of mobile device positioning.

[0119] The localization result of this method is as follows Figure 5 As shown, the black line represents the actual trajectory of the mobile device, the solid black dots represent the UWB observation coordinates, and the hollow black dots represent the mobile device coordinates obtained by this method. It can be seen that compared to UWB observation coordinates, the mobile device coordinates obtained by this method are closer to the actual trajectory of the mobile device, resulting in more accurate positioning.

[0120] A fusion positioning system for complex scenarios within a factory area is provided to implement the aforementioned fusion positioning method for complex scenarios within a factory area. The fusion positioning system includes: a UWB tag, a UWB base station, an IMU, and a processing module.

[0121] The UWB tag is used to receive location data sent by the UWB positioning base station;

[0122] The IMU is used to measure the acceleration and angular velocity of the mobile device;

[0123] The processing module is used to process the location data, the acceleration, and the angular velocity to obtain the position coordinates of the mobile device.

[0124] A mobile device includes: a processor and memory;

[0125] The memory stores computer-executed instructions;

[0126] The processor executes the computer execution instructions stored in the memory, causing the mobile device to perform the aforementioned fusion positioning method in complex scenarios within the factory area.

[0127] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned fusion positioning method for complex scenarios within a factory area.

[0128] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented, in whole or in part, as a computer program product, the computer program product includes one or more computer instructions. When the computer program instructions are loaded or executed on a computer, the processes or functions described in the embodiments of the present invention are generated, in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)). The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention shall fall within the scope of the technical solution of the present invention.

Claims

1. A fusion positioning method for complex scenarios within a factory area, characterized in that, include: Determine whether the number of UWB positioning base stations corresponding to the received location data is greater than or equal to a first preset value; When the number of UWB positioning base stations is greater than or equal to the first preset value, the first confidence level of the location data is calculated based on the signal-to-noise ratio and signal strength of the location data. When the number of UWB positioning base stations corresponding to the location data with the first confidence level greater than the second preset value is greater than or equal to 3, the top 3 location data with the highest first confidence level are selected. Calculate the observation coordinates of the particle swarm of the particle filter of the mobile device and the second confidence level corresponding to the observation coordinates; wherein, the particle swarm includes N particles, and the second confidence level is the average of the first confidence levels of the first 3 position data; The observation error of the particle swarm's observation coordinates follows a normal distribution. When calculating the difference between the observation coordinates of each particle and the particle swarm, the confidence level is introduced into the observation coordinates corresponding to the particle swarm to obtain the observation coordinates corresponding to each particle.

2. The fusion positioning method for complex scenarios within a factory area according to claim 1, characterized in that, Also includes: When the number of UWB positioning base stations is less than the first preset value, or when the number of UWB positioning base stations corresponding to the location data with the first confidence level greater than the second preset value is less than 3, the observation coordinates of the particle swarm and the second confidence level are set to the first value.

3. The fusion positioning method for complex scenarios within a factory area according to claim 1 or 2, characterized in that, Also includes: When the observed coordinates of the particle swarm and the second confidence level are not set to the first value, the observed coordinates of the N particles are generated based on the second confidence level. Calculate the third confidence level of the N particles based on the Euclidean distance; Based on the third confidence level, the particle swarm is resampled to obtain a new particle swarm. Based on the new particle swarm, the center coordinates of the particle swarm are calculated to obtain the position coordinates of the mobile device.

4. The fusion positioning method for complex scenarios within a factory area according to claim 3, characterized in that, The particle swarm resampling also includes: Based on the third confidence level, the N particles are sorted. Filter out particles with a confidence level less than a third preset value from the N particles to obtain a filtered particle swarm. Randomly copy particles from the filtered particle swarm back to the filtered particle swarm; When the number of particles in the filtered particle swarm is equal to the number of particles in the particle swarm, the new particle swarm is obtained.

5. The fusion positioning method for complex scenarios within a factory area according to any one of claims 1-4, characterized in that, Before determining whether the number of UWB positioning base stations corresponding to the received location data is greater than or equal to a first preset value, the method further includes: Establish a kinematic model; Initialize the particle swarm; The location data and IMU data measured by the inertial measurement unit (IMU) are acquired; wherein the IMU data includes the acceleration and angular velocity of the mobile device. The location data and the IMU data are synchronized in time and frequency. The IMU data is then filtered. Based on the kinematic model and the IMU data, the position coordinates of the N particles are predicted.

6. A fusion positioning system for complex scenarios within a factory area, characterized in that, The fusion positioning system is used to implement the fusion positioning method in complex scenarios within a factory area as described in any one of claims 1-5, the fusion positioning system comprising: a UWB tag, a UWB base station, an IMU, and a processing module; The UWB tag is used to receive location data sent by the UWB positioning base station; The IMU is used to measure the acceleration and angular velocity of the mobile device; The processing module is used to process the location data, the acceleration, and the angular velocity to obtain the position coordinates of the mobile device.

7. A mobile device, characterized in that, include: Processor and memory; The memory stores computer-executed instructions; The processor executes the computer execution instructions stored in the memory, causing the mobile device to perform the fusion positioning method in complex scenarios within the factory area as described in any one of claims 1-5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the fusion positioning method for complex scenarios within the factory area as described in any one of claims 1-5.

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