Positioning information processing method, positioning information evaluation method and device

Abstract

The application provides a positioning information processing method and device, electronic equipment and a storage medium, comprising: through a pedestrian trajectory calculation process, performing calculation and processing on the acceleration sensor data and the rotation vector sensor data to determine a pedestrian trajectory calculation result; performing combined positioning processing based on the pedestrian trajectory calculation result and global navigation satellite system data to obtain a first positioning result; through a segmented smoothing process, performing data smoothing processing on the first positioning result to obtain a second positioning result; and based on the random sample consensus algorithm, performing data smoothing processing on the first positioning result, the second positioning result and road information data to obtain a third positioning result, thereby reducing the use cost of the positioning system and achieving more accurate positioning information. The application can be applied to scenarios including but not limited to maps, navigation, automatic driving, Internet of Vehicles, vehicle-road cooperation and the like.

Description

TECHNICAL FIELD

[0001] The present application relates to satellite data processing technology, and in particular to a positioning information processing method and device, a positioning information evaluation method and device, electronic equipment and a storage medium. BACKGROUND

[0002] In the related art, the global navigation satellite system (GNSS) module in a smart phone has greatly improved modern human life. In the development of GNSS navigation and positioning technology, navigation or positioning accuracy has always been a key problem restricting its further application in human production and life and playing a great role. With people's increasing demand for positioning services, people want convenient, inexpensive and accurate positioning services. However, in the related art, the APP generates a travel trajectory mainly depending on the GPS positioning result. However, in some specific scenarios, such as when traveling between a sky bridge, an underground passage, a tunnel, and a high-rise building, the GPS signal is weak or interrupted, making the travel trajectory determined by the GPS positioning result very low in accuracy, poor in user experience, and affecting the accuracy of positioning by the satellite navigation system. SUMMARY

[0003] Therefore, the present application provides a positioning information processing method, which can automatically obtain corresponding positioning results from a sensor data set, and the obtained positioning results can be evaluated for positioning accuracy. The method has small computational complexity, strong real-time performance, is suitable for small devices, can effectively improve user experience, and can achieve more accurate positioning information.

[0004] The technical scheme of the embodiments of the present application is as follows:

[0005] The embodiments of the present application provide a positioning information processing method, comprising:

[0006] performing data classification processing on the sensor data set to obtain acceleration sensor data, rotation vector sensor data, global navigation satellite system data, and road information data;

[0007] performing calculation processing based on the acceleration sensor data and the rotation vector sensor data to determine a pedestrian trajectory calculation result;

[0008] performing combined positioning processing based on the pedestrian trajectory calculation result and the global navigation satellite system data to obtain a first positioning result;

[0009] in response to the first positioning result, triggering a segmented smoothing process;

[0010] The first positioning result is subjected to data smoothing processing to obtain a second positioning result.

[0011] Based on the random sample consensus algorithm, the first positioning result, the second positioning result, and the road information data are subjected to data smoothing processing to obtain a third positioning result.

[0012] A positioning information evaluation method comprises:

[0013] A positioning device receives a positioning request.

[0014] In response to the positioning request, a set of sensor data is received.

[0015] Based on the set of sensor data, a first positioning result, a second positioning result, and a third positioning result are obtained through positioning information processing.

[0016] The first positioning result and the second positioning result are measured through the third positioning result, wherein the positioning information processing can be realized through a preceding method.

[0017] Embodiments of the present application also provide a positioning information processing device, comprising:

[0018] An information transmission module is configured to perform data classification processing on a set of sensor data to obtain acceleration sensor data, rotation vector sensor data, global navigation satellite system data, and road information data.

[0019] An information processing module is configured to perform calculation processing on the acceleration sensor data and the rotation vector sensor data through a pedestrian trajectory calculation process to determine a pedestrian trajectory calculation result.

[0020] The information processing module is configured to perform combined positioning processing based on the pedestrian trajectory calculation result and the global navigation satellite system data to obtain a first positioning result.

[0021] The information processing module is configured to trigger a segmented smoothing process in response to the first positioning result.

[0022] The information processing module is configured to perform data smoothing processing on the first positioning result to obtain a second positioning result.

[0023] The information processing module is configured to perform data smoothing processing based on the random sample consensus algorithm, using the first positioning result, the second positioning result, and the road information data to obtain a third positioning result.

[0024] In the above scheme,

[0025] The information processing module is configured to determine a first correction parameter corresponding to the acceleration sensor data through a pedestrian trajectory calculation process.

[0026] The information processing module is configured to correct the acceleration sensor data based on the first correction parameter to obtain corrected acceleration sensor data.

[0027] The information processing module is configured to determine a second correction parameter corresponding to the rotation vector sensor data through a pedestrian trajectory calculation process.

[0028] The information processing module is configured to correct the rotation vector sensor data based on the second correction parameter to obtain corrected rotation vector sensor data.

[0029] The information processing module is configured to determine a pedestrian trajectory calculation result based on the corrected acceleration sensor data and the corrected rotation vector sensor data.

[0030] In the above scheme,

[0031] The information processing module is configured to perform combined positioning processing, Kalman filtering processing on the pedestrian trajectory calculation result, and obtain a filtered pedestrian trajectory calculation result.

[0032] The information processing module is configured to perform combined positioning processing, Kalman filtering processing on the global navigation satellite system data, and obtain filtered global navigation satellite system data.

[0033] The information processing module is configured to perform data fusion processing on the filtered pedestrian trajectory calculation result and the filtered global navigation satellite system data, and obtain a first positioning result.

[0034] In the above scheme,

[0035] The information processing module is configured to determine a first time period threshold value matched with the piecewise smoothing process.

[0036] The information processing module is configured to perform data smoothing processing on the first positioning result based on a trigger time of the piecewise smoothing process and the first time period threshold value, and obtain a first intermediate variable.

[0037] The information processing module is configured to determine a second intermediate variable based on the first time period threshold value when positioning is completed through the first positioning result.

[0038] The information processing module is configured to determine the second positioning result based on the first intermediate variable and the second intermediate variable.

[0039] In the above scheme,

[0040] The information processing module is configured to determine a type of global navigation satellite system according to a type of positioning device corresponding to the set of sensor data.

[0041] The information processing module is configured to adjust the first time period threshold based on the type of global navigation satellite system, so as to match the first time period threshold with the type of global navigation satellite system.

[0042] In the above scheme,

[0043] The information processing module is configured to configure corresponding weight parameters for positioning results of different time periods in the second positioning result based on the second positioning result and the road information data.

[0044] The information processing module is configured to determine a sampling proportion parameter in the second positioning result by using a random sample consensus algorithm.

[0045] The information processing module is configured to perform sampling processing on the second positioning result based on the sampling proportion parameter, to obtain a sampling result of the second positioning result.

[0046] The information processing module is configured to perform data screening processing on the sampling result of the second positioning result based on the first positioning result, to obtain a screened second positioning result.

[0047] The information processing module is configured to perform data smoothing processing on the screened second positioning result, to obtain a third positioning result.

[0048] In the above scheme,

[0049] The information processing module is configured to compare the positioning results of different time periods in the second positioning result with the road information data, to obtain a distance deviation value.

[0050] The information processing module is configured to determine a basic weight parameter based on a use environment of the set of sensor data.

[0051] The information processing module is configured to configure corresponding weight parameters for the positioning results of different time periods in the second positioning result according to the distance deviation value and the basic weight parameter.

[0052] In the above scheme,

[0053] The information processing module is configured to determine a sampling proportion parameter threshold corresponding to the second positioning result.

[0054] The information processing module is configured to determine a data quantity parameter in the second positioning result, and determine a corresponding sampling number based on the data quantity parameter.

[0055] The information processing module is configured to determine a sampling ratio parameter in the second positioning result based on the sampling ratio parameter threshold and the sampling number by using the random sample consensus algorithm.

[0056] In the above scheme,

[0057] The information processing module is configured to, when the first positioning result includes a global navigation satellite system data quality parameter,

[0058] The information processing module is configured to perform data screening processing on the sampling result of the second positioning result based on the global navigation satellite system data quality parameter, to obtain a screened second positioning result; or

[0059] The information processing module is configured to, when the first positioning result does not include a global navigation satellite system data quality parameter, perform equal-proportion data screening processing on the sampling result of the second positioning result, to obtain a screened second positioning result.

[0060] In the above scheme,

[0061] The information processing module is configured to determine a type of global navigation satellite system according to a type of positioning device corresponding to the sensor data set.

[0062] The information processing module is configured to establish data connection with different global navigation satellite systems, to obtain connection data, wherein the connection data includes code phase lock identification information.

[0063] The information processing module is configured to determine satellite numbers corresponding to the different global navigation satellite systems respectively, when the connection data of the different global navigation satellite systems all carry code phase lock identification.

[0064] The information processing module is configured to receive original observation data of the global navigation satellite system according to the satellite numbers corresponding to the different global navigation satellite systems respectively.

[0065] The embodiment of the application further provides an electronic device, which comprises:

[0066] A memory is configured to store executable instructions.

[0067] A processor is configured to run the executable instructions stored in the memory, to implement the preceding positioning information processing method, or implement the preceding positioning information evaluation method.

[0068] The embodiment of the present application also provides a computer readable storage medium, which stores executable instructions, and the executable instructions are executed by a processor to implement the preceding positioning information processing method or the preceding positioning information evaluation method.

[0069] The embodiment of the present application has the following beneficial effects:

[0070] The present application obtains acceleration sensor data, rotation vector sensor data, global navigation satellite system data and road information data by performing data classification processing on a sensor data set; determines a pedestrian trajectory calculation result by performing calculation processing on the acceleration sensor data and the rotation vector sensor data through a pedestrian trajectory calculation process; obtains a first positioning result by performing combined positioning processing based on the pedestrian trajectory calculation result and the global navigation satellite system data; triggers a segmented smoothing process in response to the first positioning result; obtains a second positioning result by performing data smoothing processing on the first positioning result; and obtains a third positioning result by performing data smoothing processing on the first positioning result, the second positioning result and the road information data based on the random sample consensus algorithm. The corresponding positioning result can be obtained automatically through a sensor data set, and the positioning accuracy of the obtained positioning result can be evaluated. The method has small calculation amount, strong real-time performance, is beneficial to small devices, can effectively improve the user experience, and can achieve more accurate positioning information. BRIEF DESCRIPTION OF DRAWINGS

[0071] Figure 1 is a use environment schematic diagram of a positioning information processing method provided by the embodiment of the present application;

[0072] Figure 2 is a component structure schematic diagram of a positioning information processing device provided by the embodiment of the present application;

[0073] Figure 3 is an optional flow schematic diagram of a positioning information processing method provided by the embodiment of the present application;

[0074] Figure 4 is an optional flow schematic diagram of a positioning information processing method provided by the embodiment of the present application;

[0075] Figure 5 is an optional flow schematic diagram of a positioning information processing method provided by the embodiment of the present application;

[0076] Figure 6 is an optional flow schematic diagram of a positioning information processing method provided by the embodiment of the present application. DETAILED DESCRIPTION

[0077] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings. The described embodiments should not be regarded as limitations to the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present application.

[0078] In the following description, "some embodiments" are related to a subset of all possible embodiments, but it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0079] Before the embodiments of the present application are further described in detail, the terms and phrases involved in the embodiments of the present application are explained, and the terms and phrases involved in the embodiments of the present application are applicable to the following explanations.

[0080] 1) In response to: used to represent the conditions or states on which the operations performed depend, when the dependent conditions or states are met, one or more operations performed can be real-time or have a set delay; in the absence of special instructions, there is no restriction on the execution order of multiple operations performed.

[0081] 2) Location service: Location Based Services (LBS) is also called positioning service. Location service is a service related to location provided by a wireless operation company for users. Location Based Services (LBS) is to obtain the current location of a positioning device by using various types of positioning technology, and to provide information resources and basic services to the positioning device through mobile Internet. LBS first determines the spatial position of the reader by using positioning technology, and then the reader can obtain location-related resources and information through mobile Internet. LBS service integrates mobile communication, Internet, spatial positioning, location information, big data and other information technologies, uses mobile Internet service platform for data updating and interaction, so that users can obtain corresponding services through spatial positioning.

[0082] 3) Global Navigation Satellite System: Global Navigation Satellite System, also known as Global Navigation Satellite System, is a space-based radio navigation and positioning system that can provide users with all-weather 3D coordinates and speed and time information at any location on the earth's surface or near space. Common systems include GPS, BDS, GLONASS and GALILEO four satellite navigation systems. The earliest is the GPS (Global Positioning System) of the United States, and the most advanced technology is also the GPS system. With the full service of BDS and GLONASS systems in the Asia-Pacific region in recent years, especially the rapid development of BDS system in the civil field. Satellite navigation systems have been widely used in navigation, communication, consumer entertainment, surveying and mapping, time service and car navigation and information service, and the overall development trend is to provide high-precision services for real-time applications.

[0083] 4) Mobile terminal: Mobile terminal or mobile communication terminal refers to a computer device that can be used in mobile, including mobile phone, notebook, tablet computer, POS machine, vehicle-mounted device, etc. With the development of network and technology towards more and more broadband, mobile communication industry will move towards a real mobile information era. With the rapid development of integrated circuit technology, the processing capacity of mobile terminal has a powerful processing capacity, and mobile terminal is changing from a simple call tool to a comprehensive information processing platform. Mobile terminal has very rich communication methods, that is, it can communicate through GSM, CDMA, WCDMA, EDGE, 4G and other wireless operation networks, and can also communicate through wireless local area network, Bluetooth and infrared. In addition, mobile terminal integrates global satellite navigation system positioning chip for processing satellite signals and precise positioning of users, which is currently widely used in location services; mobile terminal contains positioning device.

[0084] 5) PDR technology: Pedestrian Dead Reckoning (PDR) is a pedestrian trajectory estimation technology, which calculates the position information of the user through inertial sensors (magnetometer, acceleration, gyroscope).

[0085] 6) DR technology: Dead Reckoning (DR) is a similar algorithm to PDR, which can measure the speed, running distance and position of the moving object through the data collected by the inertial sensor.

[0086] 7) Kalman filtering: also known as Kalman filter, is a kind of algorithm using linear system state equation, through system input and output observation data, the optimal estimation of system state. In the positioning technology field related to the embodiments of the application, Kalman filter can be used for data fusion (such as fusing GPS data and PDR data), and a high-precision positioning result can be obtained in real time.

[0087] 8) RTS smoothing algorithm: also known as Rauch-Tung-Striebel smoother, is a kind of smoothing algorithm. In the embodiments of the application, it can be used in conjunction with Kalman filter, and all filtering results in the user navigation process can be used to smooth the optimized user position and user direction, so RTS smoothing is a post-processing method.

[0088] The positioning information processing method provided by the application is introduced as follows, wherein, Figure 1 The use scenario of the positioning information processing method provided by the embodiments of the application is shown in Figure 1 , the terminal (including terminal 10-1 and terminal 10-2) is provided with a client with map information display software, and the user can realize accurate positioning through the GNSS (Global Navigation Satellite System) module in the smart phone through the set map client, and the received real-time position is displayed to the user; the terminal is connected to the server 200 through the network 300, the network 300 can be a wide area network or a local area network, or a combination of the two, and the data transmission is realized by using a wireless link, realizing the sharing of map information between different terminals. The terminal (including terminal 10-1 and terminal 10-2) can receive the original observation data of the global navigation satellite system, and through the sensor data set, based on the random sampling consistency algorithm, the first positioning result, the second positioning result and the road information data are used for data smoothing processing, and the third positioning result is obtained.

[0089] The structure of the positioning information processing device of the embodiments of the application is described in detail below. The positioning information processing device can be implemented in various forms, such as a special terminal with global navigation satellite system positioning information processing function, or a server with global navigation satellite system positioning information processing function, such as the server 200 in the foregoing Figure 1 . Figure 2 The composition structure of the positioning information processing device provided by the embodiments of the application is shown in the figure, and it can be understood that Figure 2 only an exemplary structure of the positioning information processing device is shown, not all structures, and the Figure 2The illustrated partial or entire structure.

[0090] The positioning information processing apparatus provided by the embodiments of the present application comprises at least one processor 201, a memory 202, a user interface 203 and at least one network interface 204. The various components in the positioning information processing apparatus 20 are coupled together through a bus system 205. It can be understood that the bus system 205 is used to realize the connection communication between the components. The bus system 205 includes not only a data bus, but also a power bus, a control bus and a status signal bus. However, for the purpose of clear illustration, all the buses are marked as the bus system 205 in the Figure 2

[0091] The user interface 203 can include a display, a keyboard, a mouse, a trackball, a click wheel, a key, a button, a touchpad or a touch screen, etc.

[0092] It can be understood that the memory 202 can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. The memory 202 in the embodiments of the present application can store data to support the operation of the terminal (such as 10-1). Examples of these data include any computer programs for operating on the terminal (such as 10-1), such as operating systems and application programs. The operating system contains various system programs, such as framework layer, core library layer, driver layer, etc., for realizing various basic services and processing hardware-based tasks. The application program can include various application programs.

[0093] In some embodiments, the positioning information processing apparatus provided by the embodiments of the present application can be realized in a combination of software and hardware. As an example, the question and answer model training apparatus provided by the embodiments of the present application can be a processor in the form of a hardware decoding processor, which is programmed to execute the positioning information processing method provided by the embodiments of the present application. For example, the processor in the form of a hardware decoding processor can use one or more application specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field programmable gate arrays (FPGAs) or other electronic elements.

[0094] ​As an example of the location information processing device provided in this embodiment of the invention, which is implemented by combining software and hardware, the location information processing device provided in this embodiment of the invention can be directly embodied as a combination of software modules executed by processor 201. The software modules can be located in a storage medium, which is located in memory 202. Processor 201 reads the executable instructions included in the software modules in memory 202 and combines them with necessary hardware (e.g., including processor 201 and other components connected to bus 205) to complete the location information processing method provided in this embodiment of the invention.

[0095] As an example, processor 201 can be an integrated circuit chip with signal processing capabilities, such as a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., wherein the general-purpose processor can be a microprocessor or any conventional processor, etc.

[0096] As an example of the hardware implementation of the positioning information processing device provided in the embodiments of the present invention, the device provided in the embodiments of the present invention can be directly executed by a processor 201 in the form of a hardware decoding processor. For example, it can be executed by one or more application specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components to implement the positioning information processing method provided in the embodiments of the present invention.

[0097] In this embodiment of the invention, the memory 202 is used to store various types of data to support the operation of the location information processing device 20. Examples of such data include any executable instructions for operation on the location information processing device 20, such as executable instructions that can be included in a program implementing the location information processing method of this embodiment of the invention.

[0098] In other embodiments, the positioning information processing device provided in this invention can be implemented in software. Figure 2A location information processing device stored in memory 202 is shown. This device can be software in the form of programs and plugins, and includes a series of modules. As an example of a program stored in memory 202, it may include the location information processing device. The location information processing device includes the following software modules: an information transmission module 2081 and an information processing module 2082. When the software modules in the location information processing device are read into RAM and executed by processor 201, the location information processing method provided in this embodiment of the invention will be implemented. The functions of each software module in the location information processing device will be further described below.

[0099] The information transmission module 2081 is used to classify and process the sensor data set to obtain accelerometer sensor data, rotation vector sensor data, global navigation satellite system data, and road information data.

[0100] The information processing module 2082 is used to perform calculation processing on the acceleration sensor data and the rotation vector sensor data through the pedestrian trajectory calculation process to determine the pedestrian trajectory calculation result.

[0101] The information processing module 2082 is used to perform combined positioning processing based on the human trajectory estimation result and the global navigation satellite system data to obtain a first positioning result.

[0102] The information processing module 2082 is used to trigger a segmented smoothing process in response to the first positioning result.

[0103] The information processing module 2082 is used to perform data smoothing processing on the first positioning result to obtain a second positioning result.

[0104] The information processing module is used to perform data smoothing processing based on the random sampling consensus algorithm, using the first positioning result, the second positioning result, and the road information data, to obtain a third positioning result.

[0105] according to Figure 2 The electronic device shown, in one aspect of this application, also provides a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform various embodiments and combinations of embodiments provided in the various optional implementations of the above-described positioning information processing method.

[0106] In use Figure 2The professional testers of the electronic device for showing the positioning information of navigation can record the real track artificially or obtain the real track by using high-precision equipment (such as RTK equipment) when evaluating the positioning information of navigation. For a large number of online users, the GPS result and the planned path are used as the position reference. However, in some specific scenarios, such as when walking between a bridge, an underground passage, a tunnel and a high-rise building, the signal of the GPS is weak or interrupted, so that the walking track determined by using the positioning result of the GPS has very low accuracy, the user experience is poor, and the accuracy of the positioning by using the satellite navigation system is affected.

[0107] In order to solve the above defects, combined with Figure 2 The positioning information processing device shows the positioning information processing method provided by the embodiment of the application. Referring to Figure 3 , Figure 3 An optional flowchart of the positioning information processing method provided by the embodiment of the application can be understood as Figure 3 The steps shown can be executed by various electronic devices running the positioning information processing device, for example, can be electronic devices capable of receiving global navigation satellite system data, such as special terminals with the positioning information processing device, smart phones, smart watches and the like. The special terminal with the positioning information processing device can be the special terminal with the positioning information processing device shown in the foregoing Figure 2 The electronic device with the positioning information processing device in the embodiment shown. The steps shown in Figure 3 will be described below.

[0108] Step 301: The positioning information processing device performs data classification processing on the sensor data set to obtain acceleration sensor data, rotation vector sensor data, global navigation satellite system data and road information data.

[0109] In some embodiments of the present application, a type of global navigation satellite system is determined according to a type of positioning device corresponding to a set of sensor data; the positioning device establishes a data connection with different global navigation satellite systems to obtain connection data, wherein the connection data includes code phase lock identification information; when the connection data of the different global navigation satellite systems all carries code phase lock identification, satellite numbers corresponding to the different global navigation satellite systems are determined respectively; and original observation data of the global navigation satellite system is received according to the satellite numbers corresponding to the different global navigation satellite systems respectively. Specifically, the initial position of the user is (X, Y, Z), and there are N satellite GNSS Clock and GNSSMeasurement original observation values, wherein each satellite contains code phase lock identification (Code Lock flag), wherein Time Nanos represents the time of satellite observation value in nanoseconds; Time Offset Nanos represents the time offset value of satellite observation value in nanoseconds; Full Bias Nanos represents the difference between the internal time of the mobile phone and the true GPS time in nanoseconds; and Bias Nanos represents the time in nanoseconds. After obtaining the global navigation satellite system data, the quality of the global navigation satellite system data can be sorted,

[0110] When obtaining road information data, a chip for receiving a GPS signal can be built in the terminal, and the received GPS signal can achieve the purpose of positioning; the terminal can parse the geographic location information of the current terminal, such as latitude and longitude coordinates, by reading the GPS signal, and then draw a line on the map displayed on the interface according to the geographic location information to obtain the user's travel trajectory. In the present application, GPS data refers to position information, speed information and direction information representing the current time; speed information can be determined through adjacent position information and travel time; direction information can be calculated through adjacent position information.

[0111] Step 302: The positioning information processing device determines the pedestrian trajectory calculation result by performing calculation processing on the acceleration sensor data and the rotation vector sensor data through the pedestrian trajectory calculation process.

[0112] In some embodiments of the present application, the determination of the pedestrian trajectory calculation result can be realized by the following methods:

[0113] The first correction parameter corresponding to the acceleration sensor data is determined through a pedestrian trajectory estimation process; the acceleration sensor data is corrected based on the first correction parameter to obtain corrected acceleration sensor data; the second correction parameter corresponding to the rotation vector sensor data is determined through the pedestrian trajectory estimation process; the rotation vector sensor data is corrected based on the second correction parameter to obtain corrected rotation vector sensor data; and the pedestrian trajectory estimation result is determined based on the corrected acceleration sensor data and the corrected rotation vector sensor data. It should be noted that, in addition to the GPS / PDR fusion method and the GPS / DR fusion method described in the embodiments, other fusion methods or fusion strategies can also be used in the data fusion process in step 302, such as particle filtering, factor graph method, or Kalman filtering design method under different state vectors and different observation vectors. In addition to the RTS algorithm described in the embodiments, other smoothing methods can also be used in the smoothing process, such as global optimization-based smoothing method, machine learning-based smoothing method, deep learning-based smoothing method, etc., which are not limited in the present application.

[0114] Step 303: The positioning information processing device performs combined positioning processing based on the pedestrian trajectory estimation result and the global navigation satellite system data to obtain a first positioning result.

[0115] In some embodiments of the present application, the combined positioning processing to obtain the first positioning result can be realized by the following method:

[0116] In the combined positioning processing, Kalman filtering processing is performed on the pedestrian trajectory estimation result to obtain a filtered pedestrian trajectory estimation result; Kalman filtering processing is performed on the global navigation satellite system data to obtain a filtered global navigation satellite system data; and data fusion processing is performed on the filtered pedestrian trajectory estimation result and the filtered global navigation satellite system data to obtain the first positioning result. Based on the GPS data, PDR estimation, Kalman filtering and Kalman smoothing processing are performed in combination with inertial positioning data, which can restore the travel trajectory corresponding to the positioning result error during the weak GPS signal or GPS signal interruption period, and improve the accuracy of the calculated travel trajectory. Specifically, further, Kalman filtering is a recursive estimation, that is, as long as the estimated value of the previous state and the observation value of the current state are known, the estimated value of the current state can be calculated. When applying Kalman filtering, a Kalman filtering matrix needs to be established first, and the filtering parameters in the Kalman filtering matrix need to be determined. The update value of the Kalman filtering matrix is Update, and the first adjustment value is downSpeed; the formula is as follows:

[0117] Update = init_update - (1.0 / 1.0 + exp(-(downSpeed * Delta)) - A) * 2.0, wherein A is a dynamic coefficient, which can be adjusted according to different terminal types, for example, the positioning information processing method is applied to a mobile phone, the dynamic coefficient A can be configured as 0.5, and the positioning information processing method is applied to a smart watch or a bracelet, the dynamic coefficient A can be configured as 0.65, so that the update value of the Kalman filtering matrix can be flexibly determined. Further, let the weight adjustment value of the Kalman filtering matrix be fPredicated, and the second adjustment value be global_q, which can realize the influence of adjusting the Delta component on the final prediction prediction weight.

[0118] Step 304: The positioning information processing device performs data smoothing processing on the first positioning result through the piecewise smoothing process to obtain a second positioning result.

[0119] Figure 4 An optional flowchart of the positioning information processing method provided by the embodiments of the present application can be understood as follows, Figure 4 The steps shown can be executed by various electronic devices running the positioning information processing device, for example, can be electronic devices capable of receiving global navigation satellite data, such as special terminals with positioning information processing devices, smart phones, smart watches, etc. The steps shown will be described below with respect to Figure 4 .

[0120] Step 401: Determine a first time period threshold value matched with the piecewise smoothing process.

[0121] In some embodiments of the present application, the type of global navigation satellite system can be determined according to the type of positioning device corresponding to the sensor data set; the first time period threshold value is adjusted based on the type of global navigation satellite system, so that the first time period threshold value matches the type of global navigation satellite system.

[0122] Step 402: Based on the trigger time of the piecewise smoothing process and the first time period threshold value, perform data smoothing processing on the first positioning result to obtain a first intermediate variable.

[0123] Step 403: When the positioning is completed through the first positioning result, determine a second intermediate variable based on the first time period threshold value.

[0124] Taking GPS data as an example, a segmented time period length can be set, for example, 10s, when the time of the first positioning is 0, every time period of 10s, the data in the 10s is processed by the RTS process to obtain a first intermediate variable (wherein the first intermediate variable is the matrix information in the RTS algorithm, such as the state matrix X array, the observation noise array R array, etc.), and is stored, and the storage result is recorded as the first intermediate variable. After the positioning is completed, the last time period (which can be less than 10s) is processed by the RTS process to obtain and store the intermediate data to the first intermediate variable, which can realize the supplement of the first intermediate variable.

[0125] Step 404: determining the second positioning result based on the first intermediate variable and the second intermediate variable.

[0126] When the second positioning result is determined, step 305 is continued to be executed.

[0127] Step 305: the positioning information processing device triggers the random sample consensus algorithm by the global smoothing process, and performs data smoothing processing on the first positioning result, the second positioning result and the road information data to obtain a third positioning result.

[0128] Reference Figure 5 , Figure 5 An optional flowchart of a positioning information processing method provided by an embodiment of the present application is shown in Figure 5 The steps shown in the figure can be executed by various electronic devices running the positioning information processing device, for example, can be electronic devices capable of receiving global navigation satellite data, such as special terminals, smart phones, smart watches and the like with positioning information processing devices, and the following will be described with reference to the steps shown in Figure 5 .

[0129] Step 501: based on the second positioning result and the road information data, configuring a corresponding weight parameter for the positioning result of different time periods in the second positioning result.

[0130] The positioning results of different time periods in the second positioning result can be compared with the road information data to obtain a distance deviation value; a basic weight parameter is determined based on the use environment of the sensor data set; and the positioning results of different time periods in the second positioning result are configured with corresponding weight parameters according to the distance deviation value and the basic weight parameter. Specifically, a first intermediate variable and road data information are used to assign a weight to the result of each time period in the first intermediate variable. For a map APP, a user usually walks along a planned road, and at this time, the road data can be used as a reference standard: if the result of a certain time period in the first intermediate variable deviates greatly from the road information, the weight is calculated according to the distance deviation, and the greater the distance deviation, the smaller the weight. For example, in the time period of 20-30s, the GPS deviates from the road by 10m; in the time period of 40-50s, the distance is 3m; and the weight of the time period of 40-50s is greater than that of the time period of 20-30s. At the same time, when designing the weight, since the user may actually walk off the route, even if the GPS deviates from the road by a large distance, for example, 20m, a certain minimum weight should be retained.

[0131] Step 502: determining a sampling ratio parameter in the second positioning result by using a random sample consensus algorithm.

[0132] In some embodiments of the present application, a sampling ratio parameter threshold corresponding to the second positioning result can be determined; a data quantity parameter in the second positioning result is determined, and a corresponding sampling number is determined based on the data quantity parameter; and the sampling ratio parameter in the second positioning result is determined based on the sampling ratio parameter threshold and the sampling number by using the random sample consensus algorithm. For the RTS algorithm, a large number of observation quantities are not necessarily required, and a small number of GPS with accurate quality evaluation can well ensure the algorithm; on the contrary, if a large error observation is introduced, the accuracy will be reduced. Therefore, when sampling from the first intermediate variable, the sampling ratio needs to be limited. For example, if the positioning duration is 1000s, there are 100 groups of data in the first intermediate variable; according to the RANSAN algorithm, the sampling process is as follows: 1) 10 groups of data are sampled from 100 groups, and 100 samplings are performed; each sampling needs to be sampled according to the weight obtained in step 501; 2) 20 groups of data are sampled from 100 groups, and 80 samplings are performed; 2) 20 groups of data are sampled from 100 groups, and 60 samplings are performed; and so on. That is, the more groups of data sampled, the fewer samplings performed. At the same time, the maximum number of sampling groups is limited to 50% of the total number of groups. All the sampling results are recorded to the second intermediate variable.

[0133] It should be noted that the positioning information processing method provided in the present application utilizes the random sample consensus algorithm to determine the sampling ratio parameter in the second positioning result, and the sampling ratio parameter can be dynamically adjusted according to different use environments. For example, in a virtual scene or a somatosensory game scene, 40 groups of data are sampled from 100 groups of data samples, and a total of 80 samplings are performed to evaluate the accuracy of the game user trajectory calculation result and the positioning information in the third positioning result, so that the game user obtains a more accurate game experience.

[0134] Step 503: performing sampling processing on the second positioning result based on the sampling ratio parameter to obtain a sampling result of the second positioning result.

[0135] Step 504: performing data screening processing on the sampling result of the second positioning result based on the first positioning result to obtain a screened second positioning result.

[0136] In some embodiments of the present application, when the first positioning result includes a global navigation satellite system data quality parameter, the data screening processing on the sampling result of the second positioning result is performed based on the global navigation satellite system data quality parameter to obtain the screened second positioning result; or when the first positioning result does not include a global navigation satellite system data quality parameter, the sampling result of the second positioning result is subjected to equal-proportion data screening processing to obtain the screened second positioning result. Specifically, if the GPS quality is calculated in the segmented RTS algorithm and the GPS / PDR fusion algorithm, the second intermediate variable is removed according to the GPS quality. In some 2 groups of samples P and Q in the second intermediate variable, if the number of points with poor GPS quality in P is more than that in Q, Q is retained and P is removed. Since there will be errors in the GPS quality calculation, even if it is found according to the GPS quality calculation that the GPS quality of a certain time period in the first intermediate variable is poor as a whole, the samples in B containing the time period are not allowed to be completely removed, at least 30% are retained, and for different sample group numbers, equal-proportion removal can be performed. For example, 30% of the samples of 10 groups of data in the second intermediate variable are removed, and 30% of the samples of 20 groups of data are removed, thereby completing the data screening.

[0137] Step 505: performing data smoothing processing on the screened second positioning result to obtain a third positioning result.

[0138] The positioning information processing method of the present application will be described below with reference to a terminal device, such as a smart bracelet (pedometer), Figure 6 An optional flowchart of the positioning information processing method provided in the embodiments of the present application can be understood as, Figure 6The steps shown can be executed by various electronic devices running the positioning information processing apparatus, for example, can be electronic devices capable of receiving global navigation satellite system data such as special terminals with positioning information processing apparatus, smart phones, smart watches, etc., wherein the special terminals with global navigation satellite system positioning information processing apparatus process any kind of global navigation satellite system data in advance. The steps shown will be described below. Figure 6 The steps shown will be described below.

[0139] Step 601: The positioning device receives a positioning request, receives raw observation data of a global navigation satellite system in response to the positioning request, and acquires real-time sensor data.

[0140] Step 602: Based on the type of the global navigation satellite system, decode the corresponding real-time navigation ephemeris to determine the ephemeris parameters of the matching satellite.

[0141] Step 603: Start the pedestrian trajectory calculation process, and calculate the acceleration sensor data and the rotation vector sensor data to determine the pedestrian trajectory calculation result.

[0142] Step 604: Based on the sensor data set, obtain the first positioning result, the second positioning result and the third positioning result through positioning information processing.

[0143] Step 605: Through the third positioning result, evaluate the first positioning result and the second positioning result to obtain the evaluation result of the first positioning result and the evaluation result of the second positioning result.

[0144] Thus, the positioning accuracy can be evaluated to make the positioning information more accurate.

[0145] Beneficial technical effects:

[0146] The application obtains acceleration sensor data, rotation vector sensor data, global navigation satellite system data and road information data by performing data classification processing on a sensor data set; determines a pedestrian trajectory calculation result by performing calculation processing on the acceleration sensor data and the rotation vector sensor data through a pedestrian trajectory calculation process; obtains a first positioning result by performing combined positioning processing based on the pedestrian trajectory calculation result and the global navigation satellite system data; triggers a segmented smoothing process in response to the first positioning result; obtains a second positioning result by performing data smoothing processing on the first positioning result; and obtains a third positioning result by performing data smoothing processing on the first positioning result, the second positioning result and the road information data based on the random sample consensus algorithm. The corresponding positioning result can be obtained automatically through the sensor data set, and the positioning accuracy can be evaluated. The method has small calculation amount, strong real-time performance, is beneficial to small devices, can effectively improve the user experience, and can achieve more accurate positioning information.

[0147] The above is only an embodiment of the application and is not used to limit the protection scope of the application. Any modification, equivalent replacement, improvement and the like within the spirit and principle of the application shall be included in the protection scope of the application.

Claims

1. A positioning information processing method characterized by comprising: The method comprises: performing data classification processing on a sensor data set to obtain acceleration sensor data, rotation vector sensor data, global navigation satellite system data, and road information data; performing calculation processing based on the acceleration sensor data and the rotation vector sensor data to determine a pedestrian trajectory calculation result; performing combined positioning processing based on the pedestrian trajectory calculation result and the global navigation satellite system data to obtain a first positioning result; performing data smoothing processing on the first positioning result to obtain a second positioning result; configuring corresponding weight parameters for positioning results in different time periods in the second positioning result based on the second positioning result and the road information data; determining a sampling ratio parameter in the second positioning result based on the weight parameters using a random sample consensus algorithm; performing sampling processing on the second positioning result based on the sampling ratio parameter to obtain a sampling result of the second positioning result; performing data screening processing on the sampling result of the second positioning result based on the first positioning result to obtain a screened second positioning result; performing data smoothing processing on the screened second positioning result to obtain a third positioning result.

2. The method of claim 1, wherein, The calculation processing based on the acceleration sensor data and the rotation vector sensor data to determine a pedestrian trajectory calculation result comprises: determining a first correction parameter corresponding to the acceleration sensor data; performing correction processing on the acceleration sensor data based on the first correction parameter to obtain corrected acceleration sensor data; determining a second correction parameter corresponding to the rotation vector sensor data; performing correction processing on the rotation vector sensor data based on the second correction parameter to obtain corrected rotation vector sensor data; determining a pedestrian trajectory calculation result based on the corrected acceleration sensor data and the corrected rotation vector sensor data.

3. The method of claim 1, wherein, The combined positioning processing based on the pedestrian trajectory calculation result and the global navigation satellite system data to obtain a first positioning result comprises: performing combined positioning processing, performing Kalman filtering processing on the pedestrian trajectory calculation result to obtain a filtered pedestrian trajectory calculation result; performing combined positioning processing, performing Kalman filtering processing on the global navigation satellite system data to obtain filtered global navigation satellite system data; performing data fusion processing on the filtered pedestrian trajectory calculation result and the filtered global navigation satellite system data to obtain a first positioning result.

4. The method of claim 1, wherein, The data smoothing processing on the first positioning result to obtain a second positioning result comprises: determining a first time period threshold value matched with a segmented smoothing process; performing data smoothing processing on the first positioning result based on a trigger time of the segmented smoothing process and the first time period threshold value to obtain a first intermediate variable; determining a second intermediate variable based on the first time period threshold value when positioning is completed through the first positioning result; determining the second positioning result based on the first intermediate variable and the second intermediate variable.

5. The method of claim 4, wherein, The method further comprises: determining a type of global navigation satellite system according to a type of positioning device corresponding to a sensor data set; adjusting the first time period threshold based on the type of global navigation satellite system to match the first time period threshold with the type of global navigation satellite system.

6. The method of claim 1, wherein, configuring corresponding weight parameters for positioning results of different time periods in the second positioning result based on the second positioning result and the road information data, including: comparing the positioning results of different time periods in the second positioning result with the road information data to obtain a distance deviation value; determining a basic weight parameter based on an environment of use of the sensor data set; configuring corresponding weight parameters for positioning results of different time periods in the second positioning result according to the distance deviation value and the basic weight parameter.

7. The method of claim 1, wherein, determining a sampling proportion parameter in the second positioning result based on the weight parameters by using a random sample consensus algorithm, including: determining a sampling proportion parameter threshold corresponding to the second positioning result; determining a data quantity parameter in the second positioning result and determining a corresponding sampling number based on the weight parameter and the data quantity parameter; determining the sampling proportion parameter in the second positioning result based on the sampling proportion parameter threshold and the sampling number by using the random sample consensus algorithm.

8. The method of claim 1, wherein, performing data screening processing on the sampling result of the second positioning result based on the first positioning result to obtain a screened second positioning result, including: when the first positioning result includes a global navigation satellite system data quality parameter, performing data screening processing on the sampling result of the second positioning result based on the global navigation satellite system data quality parameter to obtain a screened second positioning result; or when the first positioning result does not include a global navigation satellite system data quality parameter, performing equal-proportion data screening processing on the sampling result of the second positioning result to obtain a screened second positioning result.

9. The method of claim 1, wherein, The method further includes: determining a type of global navigation satellite system according to a type of positioning device corresponding to a sensor data set; establishing data connection with different global navigation satellite systems by the positioning device to obtain connection data, wherein the connection data includes code phase lock identification information; when the connection data of the different global navigation satellite systems all carry code phase lock identification, determining satellite numbers respectively corresponding to the different global navigation satellite systems; receiving original observation data of the global navigation satellite system according to the satellite numbers respectively corresponding to the different global navigation satellite systems.

10. A positioning information evaluation method characterized by, The method includes: a positioning device receives a positioning request; in response to the positioning request, the positioning device receives a sensor data set; based on the sensor data set, the positioning device obtains a first positioning result, a second positioning result and a third positioning result by using the positioning information processing method of any one of claims 1-9; the positioning device evaluates the first positioning result and the second positioning result by using the third positioning result to obtain an evaluation result of the first positioning result and an evaluation result of the second positioning result.

11. A positioning information processing apparatus characterized by comprising: The apparatus includes: The information transmission module is configured to perform data classification processing on the sensor data set to obtain acceleration sensor data, rotation vector sensor data, global navigation satellite system data, and road information data. The information processing module is configured to perform calculation processing based on the acceleration sensor data and the rotation vector sensor data to determine a pedestrian trajectory calculation result. The information processing module is configured to perform combined positioning processing based on the pedestrian trajectory calculation result and the global navigation satellite system data to obtain a first positioning result. The information processing module is configured to perform data smoothing processing on the first positioning result to obtain a second positioning result. The information processing module is configured to configure corresponding weight parameters for positioning results of different time periods in the second positioning result based on the second positioning result and the road information data, determine a sampling ratio parameter in the second positioning result based on the weight parameters by using a random sample consensus algorithm, perform sampling processing on the second positioning result based on the sampling ratio parameter to obtain a sampling result of the second positioning result, perform data screening processing on the sampling result of the second positioning result based on the first positioning result to obtain a screened second positioning result, and perform data smoothing processing on the screened second positioning result to obtain a third positioning result.

12. The apparatus of claim 11, wherein, The information processing module is configured to: determine a first correction parameter corresponding to the acceleration sensor data; perform correction processing on the acceleration sensor data based on the first correction parameter to obtain corrected acceleration sensor data; determine a second correction parameter corresponding to the rotation vector sensor data; perform correction processing on the rotation vector sensor data based on the second correction parameter to obtain corrected rotation vector sensor data; determine a pedestrian trajectory calculation result based on the corrected acceleration sensor data and the corrected rotation vector sensor data.

13. The apparatus of claim 11, wherein, The information processing module is configured to: perform combined positioning processing and Kalman filtering processing on the pedestrian trajectory calculation result to obtain a filtered pedestrian trajectory calculation result; perform combined positioning processing and Kalman filtering processing on the global navigation satellite system data to obtain filtered global navigation satellite system data; and perform data fusion processing on the filtered pedestrian trajectory calculation result and the filtered global navigation satellite system data to obtain a first positioning result.

14. A positioning information evaluation device, characterized by, The device includes: a positioning acquisition module configured to receive a positioning request by a positioning device; a positioning processing module configured to receive a sensor data set in response to the positioning request; The positioning processing module is configured to obtain a first positioning result, a second positioning result, and a third positioning result by the positioning information processing method of any one of claims 1-9 based on the sensor data set. The positioning processing module is configured to perform evaluation processing on the first positioning result and the second positioning result by the third positioning result to obtain an evaluation result of the first positioning result and an evaluation result of the second positioning result.

15. An electronic device, comprising: The electronic device includes: a memory for storing executable instructions; a processor for implementing the positioning information processing method of any one of claims 1 to 9 or the positioning information evaluation method of claim 10 when running the executable instructions stored in the memory.

16. A computer-readable storage medium storing executable instructions, wherein the instructions, when executed by a processor, cause the processor to perform operations comprising: The executable instructions, when executed by the processor, implement the positioning information processing method of any one of claims 1 to 9 or the positioning information evaluation method of claim 10.

17. A computer program product, characterised in that, The computer program product comprises computer instructions, which, when executed by the processor, implement the positioning information processing method of any one of claims 1 to 9 or the positioning information evaluation method of claim 10.

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