CN112415558A

CN112415558A - Processing method of travel track and related equipment

Info

Publication number: CN112415558A
Application number: CN202110098897.6A
Authority: CN
Inventors: 闫伟; 袁义龙
Original assignee: Tencent Technology Shenzhen Co Ltd
Current assignee: Tencent Technology Shenzhen Co Ltd
Priority date: 2021-01-25
Filing date: 2021-01-25
Publication date: 2021-02-26
Anticipated expiration: 2041-01-25
Also published as: CN112415558B

Abstract

The application relates to the technical field of positioning, and provides a processing method of a travel track and related equipment, wherein the processing method of the travel track comprises the following steps: acquiring inertial positioning data and GPS data related to a traveling track; determining predicted positioning information corresponding to the inertial positioning data and calculating the reliability of GPS data; performing data fusion processing on the predicted positioning information and the GPS data based on the credibility of the GPS data to determine fused positioning information; and smoothing the fusion positioning information to determine a travel track. The implementation of the method and the device is beneficial to improving the accuracy of the advancing track and improving the experience of a user in browsing the advancing track on a map interface.

Description

Processing method of travel track and related equipment

Technical Field

The present application relates to the field of positioning technologies, and in particular, to a method for processing a travel track and a related device.

Background

With the development of computer technology, the current application programs APP of various navigation services and sports services already support recording the traveling track of a user. The user can know the current or historical travel process of the user through the travel track.

In the prior art, the APP generation of the advancing track mainly depends on the GPS positioning result. However, in some specific scenarios, such as traveling between overpasses, underground tunnels, and high-rise buildings, the signals of the GPS are weak or there is a break, so that the accuracy of the traveling track determined by using the GPS positioning result is very low, and the user experience is poor.

Disclosure of Invention

The application provides a processing method of a travel track and related equipment, which can solve at least one technical problem. The technical scheme is as follows:

in a first aspect, a method for processing a travel track is provided, including:

acquiring inertial positioning data and GPS data related to a traveling track;

determining predicted positioning information corresponding to the inertial positioning data and calculating the reliability of the GPS data;

performing data fusion processing on the predicted positioning information and the GPS data based on the credibility of the GPS data to determine fusion positioning information;

and smoothing the fusion positioning information to determine a traveling track.

With reference to the first aspect, in a first implementation form of the first aspect, the inertial positioning data includes rotation vector data and accelerometer data; the determining of the predicted positioning information corresponding to the inertial positioning data includes:

performing step detection and step length estimation based on the accelerometer data, and determining step length information;

determining direction information of travel based on the rotation vector data;

performing linear detection on the direction information based on GPS position information of the GPS data to correct the direction information;

and determining the step length information and the corrected direction information as predicted positioning information.

With reference to the first aspect, in a second implementation form of the first aspect, the GPS data includes at least one of GPS location information, GPS direction information, and GPS speed information; determining a trustworthiness of the GPS data, comprising:

determining a trustworthiness of the GPS location information and/or the orientation information based on at least one of:

the difference of adjacent GPS positions in a preset time period;

the difference of adjacent GPS directions within a preset time period;

the difference between the direction determined based on a plurality of GPS positions in a preset time period and the GPS direction at the current moment;

a difference between the position, direction and/or velocity determined based on the GPS data within a preset time period and the current time GPS position, direction and/or velocity, respectively.

With reference to the first aspect, in a third implementation manner of the first aspect, the performing data fusion processing on the predicted positioning information and GPS data based on the reliability of the GPS data to determine fused positioning information includes:

when fusion positioning information is determined aiming at the current moment, the following data fusion steps are executed: acquiring historical position information and historical direction information of a travel track at the current moment based on the GPS data; determining a current moment weight of the GPS data based on the credibility of the GPS data; performing data fusion processing on the predicted positioning information and the GPS data based on the historical position information, the historical direction information and the current time weight of the GPS data to determine the fusion positioning information of the current time;

and taking the fusion positioning information as a part of the historical position information and the historical direction information acquired at the next moment, taking the next moment as the current moment, and repeatedly executing the data fusion step until the current moment is the end moment of the travel track.

With reference to the first aspect, in a fourth implementation manner of the first aspect, the smoothing the fusion positioning information to determine a travel track includes:

based on a target time period or a total time period corresponding to the advancing track, smoothing the fusion positioning information to determine the advancing track; and the target time period is obtained by expanding the corresponding interruption time period when the GPS signal is interrupted.

With reference to the fourth implementation manner of the first aspect, in a fifth implementation manner of the first aspect, the smoothing processing the fusion positioning information based on a target time period or a total time period corresponding to a travel track to determine the travel track includes any one of:

acquiring a first intermediate variable in the fusion positioning information; smoothing the first intermediate variable based on a total time period corresponding to the advancing track to determine the advancing track;

responding to an instruction of GPS signal interruption, and acquiring a first intermediate variable in the fusion positioning information; expanding the corresponding interruption time period when the GPS signal is interrupted to obtain a target time period; smoothing the first intermediate variable based on the target time period to determine a travel track;

wherein the first intermediate variable comprises a state vector, a covariance matrix, and a state transition matrix.

With reference to the fifth implementation manner of the first aspect, in a sixth implementation manner of the first aspect, the extending an interruption time period corresponding to when the GPS signal is interrupted to obtain a target time period includes:

successively expanding corresponding interruption time periods when the GPS signals are interrupted based on a preset step length to obtain a plurality of initial time periods;

a score calculation step is performed for each initial time period: determining position information and direction information corresponding to an initial time period; determining positioning error information of the initial time period based on the difference between the GPS position information and the GPS direction information in the GPS data and the position information and the direction information corresponding to the initial time period respectively; determining the score of the initial time period based on a preset scoring strategy and the positioning error information of the initial time period;

and taking the initial time period with the highest score as a target time period.

With reference to the sixth implementation manner of the first aspect, in a seventh implementation manner of the first aspect, the determining the position information and the direction information corresponding to the initial time period includes:

determining position information and direction information corresponding to a first time period from the end time of the initial time period to the end time of the travel track based on the first intermediate variable;

and determining position information and direction information corresponding to a second time period between the starting time of the travel track and the starting time of the initial time period based on the first intermediate variable.

With reference to the sixth implementation manner of the first aspect, in an eighth implementation manner of the first aspect, the determining the position information and the direction information corresponding to the initial time period further includes:

acquiring a second intermediate variable in the fusion positioning information;

filtering the position information and the direction information corresponding to the initial time period based on the second intermediate variable, and determining the position information and the direction information corresponding to the corrected initial time period;

the second intermediate variables comprise a control input matrix, a weight matrix and an observation noise matrix.

With reference to the sixth implementation manner of the first aspect, in a ninth implementation manner of the first aspect, the determining a score of the initial time period based on the preset scoring policy and the positioning error information of the initial time period includes at least one of:

if the extreme value of the position difference or the extreme value of the direction difference in the positioning error information is larger than a first threshold value related to the interruption time period, determining the score of the initial time period according to the extreme value of the position difference or the extreme value of the direction difference;

if the ratio of the position difference or the ratio of the direction difference in the positioning error information is larger than a second threshold value related to the interruption time period, determining the score of the initial time period according to the ratio of the position difference or the ratio of the direction difference;

and if the initial time period is the same as the interruption time period, determining the score of the initial time period according to the position difference value or the direction difference value in the positioning error information.

In a second aspect, a device for processing a travel track is provided, which includes: the acquisition module is used for acquiring inertial positioning data and GPS data related to the traveling track;

the determining module is used for determining predicted positioning information corresponding to the inertial positioning data and calculating the reliability of the GPS data;

the fusion processing module is used for carrying out data fusion processing on the predicted positioning information and the GPS data based on the credibility of the GPS data to determine fusion positioning information;

and the smoothing module is used for smoothing the fusion positioning information and determining a traveling track.

In a third aspect, an electronic device is provided, which includes: one or more processors; a memory; one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more programs configured to: the processing method of the travel trajectory according to the first aspect and any one of the embodiments thereof is performed.

In a fourth aspect, there is provided a computer readable storage medium storing at least one instruction, at least one program, a set of codes, or a set of instructions, which is loaded and executed by a processor to implement the method for processing a travel trajectory of the first aspect and any of its embodiments.

The beneficial effect that technical scheme that this application provided brought is:

compared with the prior art, the method provided by the application has the advantages that basic data for determining the advancing track comprise inertial positioning data besides GPS data; further determining predicted positioning information corresponding to the inertial positioning data and calculating the reliability of the GPS data, and fusing the predicted positioning information and the GPS data through filtering based on the reliability of the GPS data to determine fused positioning information; and the instability problem of the GPS signal is considered, the fusion positioning information is further subjected to smoothing processing, and the accuracy of the user advancing track can be effectively improved.

Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the description of the embodiments of the present application will be briefly described below.

Fig. 1 is a schematic flowchart of a method for processing a travel track according to an embodiment of the present disclosure;

fig. 2 is a schematic flow chart of another method for processing a travel track according to an embodiment of the present disclosure;

fig. 3 is a schematic flow chart of data fusion processing in a method for processing a travel track according to an embodiment of the present application;

fig. 4 is a schematic flowchart of determining the reliability of GPS data in a method for processing a travel track according to an embodiment of the present application;

fig. 5 is a schematic flow chart illustrating data smoothing processing in a method for processing a travel track according to an embodiment of the present application;

fig. 6 is another schematic flow chart of data smoothing processing in a method for processing a travel track according to an embodiment of the present application;

fig. 7 is a schematic operating environment diagram of a method for processing a travel track according to an embodiment of the present application;

FIG. 8a is a comparison graph between a travel track obtained by a user through an overpass by applying the processing method of the travel track provided by the embodiment of the application and a travel track obtained based on original GPS data;

FIG. 8b is a comparison graph of a travel track obtained by a user through an underground passage by applying the processing method of the travel track provided by the embodiment of the application and a travel track obtained based on original GPS data;

fig. 9 is a schematic structural diagram of a device for processing a travel track according to an embodiment of the present application;

fig. 10 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

Reference will now be made in detail to the embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present application.

As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. As used herein, the term "and/or" includes all or any element and all combinations of one or more of the associated listed items.

To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.

The following is a description of terms and related technologies that may be involved in embodiments of the present application:

GPS: is short for Global Positioning System (Global Positioning System), also called satellite Positioning System. The GPS can provide functions of vehicle positioning, theft prevention, hijack prevention, driving route monitoring, call command, and the like, and a system for performing positioning and navigation in real time in a global scope by using a GPS positioning satellite is called a global satellite positioning system. The basic principle of the GPS navigation system is to measure the distance between a satellite with a known position and a user receiver, and then integrate data of multiple satellites to know the specific position of the receiver. In the embodiment of the application, the position of the user can be known in real time through the GPS navigation system.

Inertial sensor (inertial sensor): the sensor is mainly used for detecting and measuring acceleration, inclination, impact, vibration, rotation and multiple-degree-of-freedom (DoF) motion, and is an important part for solving navigation, orientation and motion carrier control. Inertial sensors may include accelerometers (or accelerometer sensors) and angular rate sensors (gyros) and their single, dual, and tri-axial combination IMU (inertial measurement unit), AHRS (attitude reference system including magnetic sensors). In the embodiment of the application, accelerometer data and rotation vector data are mainly acquired through an inertial sensor.

PDR technology: namely Pedestrian Dead Reckoning (PDR), the position information of the user is resolved by inertial sensors (magnetometer, acceleration, gyroscope).

DR technique: that is, Dead Reckoning (DR), is similar to the PDR algorithm, and it is possible to calculate position information such as a speed, a travel distance, and a position of a traveling object from data collected by an inertial sensor.

Kalman filtering (Kalman filtering): the method is also called Kalman filtering, and is an algorithm for performing optimal estimation on the system state by using a linear system state equation and inputting and outputting observation data through a system. In the positioning technology field related to the embodiments of the present application, kalman filtering may be used to perform data fusion (for example, to fuse GPS data and PDR estimated data), and may obtain a high-precision positioning result in real time.

RTS smoothing algorithm: also known as the Kalman smoothing algorithm (Rauch-Tung-Striebel smoothing algorithm), is a smoothing algorithm. In the embodiment of the application, the RTS smoothing method can be used in cooperation with Kalman filtering, and the optimized user position and user direction can be smoothly obtained by using all filtering results in the user navigation process, so that the RTS smoothing is a post-processing method.

In the embodiment of the application, the artificial intelligence technology is also related. Artificial Intelligence (AI) is a theory, method, technique and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend and expand human Intelligence, perceive the environment, acquire knowledge and use the knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technique of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a manner similar to human intelligence. Artificial intelligence is the research of the design principle and the realization method of various intelligent machines, so that the machines have the functions of perception, reasoning and decision making. The artificial intelligence technology is a comprehensive subject and relates to the field of extensive technology, namely the technology of a hardware level and the technology of a software level. The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like. In particular, embodiments of the present application relate to machine learning techniques; machine Learning (ML) is a multi-domain cross discipline, and relates to a plurality of disciplines such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and the like. The special research on how a computer simulates or realizes the learning behavior of human beings so as to acquire new knowledge or skills and reorganize the existing knowledge structure to continuously improve the performance of the computer. Machine learning is the core of artificial intelligence, is the fundamental approach for computers to have intelligence, and is applied to all fields of artificial intelligence. Machine learning and deep learning generally include techniques such as artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and formal education learning. In the embodiment, a PDR algorithm, a DR algorithm, a Kalman filtering technology and a Kalman smoothing technology are adopted to process data, and a machine learning technology is involved.

In the prior art, the advancing track generated by the application program APP of various navigation services and sports services mainly depends on the GPS positioning result. However, in some specific scenarios, such as traveling between overpasses, underground tunnels, and high-rise buildings, the signals of the GPS are weak or there is a break, so that the accuracy of the traveling track determined by using the GPS positioning result is very low, and the user experience is poor. In a common situation, when a user uses an APP of a certain navigation service to perform walking navigation, the user needs to pass through an underground passage, and as the user passes through the underground passage, a GPS signal is weak, the problems of jumping points, position errors and the like when the user passes through the underground passage appear on a map display interface of the APP of the navigation service of the user terminal; more seriously, the GPS signal is interrupted, and the user cannot know the current position of the user through the traveling track of the map display interface of the current terminal, so that the experience is very poor.

In order to solve the above problems, the present application provides a method for processing a travel track, which performs PDR estimation, kalman filtering, kalman smoothing and the like by using GPS data and combining inertial positioning data, so as to recover a travel track corresponding to a positioning result error during a period when a GPS signal is weak or the GPS signal is interrupted, and improve accuracy of the calculated travel track.

In addition, the processing method of the travelling track can display the travelling track obtained by processing on a map interface of the terminal in the related field, and effectively improves the experience of a user browsing the travelling track on the map interface.

The following describes the technical solutions of the present application and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.

The method for processing the travel track provided by the embodiment of the application is applied to a client, and can be specifically executed by the electronic device provided by the embodiment of the application, and specifically, the electronic device may be a terminal, and the terminal may be a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart sound box, a smart watch, or the like, or may be a vehicle-mounted device, but is not limited thereto. As shown in fig. 1 and 2, the method for processing the travel track provided by the embodiment of the present application includes the following steps S101 to S104:

step S101: and acquiring inertial positioning data and GPS data related to the traveling track.

The travel track may be a track generated by walking and moving of the user, or a travel track of a vehicle driven or picked up by the user, and may be adjusted according to an actual application scenario, which is not limited in the present application.

Specifically, a chip for receiving a GPS signal may be built in the terminal, and the purpose of positioning may be achieved by the received GPS signal; the terminal can analyze the geographic position information of the current terminal, such as longitude and latitude coordinates, by reading the GPS signal, and then draw a line on a map displayed on an interface according to the geographic position information to obtain the traveling track of the user. In the present application, the GPS data represents position information, speed information, and direction information at the current time; the speed information can be determined through the adjacent position information and the travel time; the direction information can be deduced from the adjacent position information.

Specifically, an inertial sensor is arranged in the terminal, and the terminal can obtain inertial positioning data related to a traveling track by reading data acquired by the inertial sensor; in this case, the inertial positioning data is also called inertial sensor data.

Optionally, the inertial positioning data and the GPS data may also be data transmitted by other electronic devices, such as: user A uses terminal 1 and terminal 2, and terminal 1 and terminal 2 can be connected in communication; when a user a walks outdoors with the terminal 1, the user B checks the current walking track (traveling track) of the user a through the terminal 2, and then the terminal 1 can transmit inertial positioning data and GPS data to the terminal 2, so that the terminal 2 can calculate the traveling track and display the traveling track to the user B on a map interface of the terminal 2.

Optionally, if a client (e.g., APP of a navigation service) executes the processing method of the travel track provided by the present application, the GPS data related to the travel track obtained by reading the GPS signal by the terminal and the inertial positioning data related to the travel track obtained by reading the data acquired by the inertial sensor by the terminal may be acquired.

Step S102: and determining the predicted positioning information corresponding to the inertial positioning data and calculating the reliability of the GPS data.

Specifically, in this embodiment, a PDR algorithm or a DR algorithm may be used to measure and calculate the position information according to the obtained inertial positioning data, so as to obtain predicted positioning information corresponding to the inertial positioning data. A specific process of determining predicted positioning information corresponding to inertial positioning data will be set forth in the following embodiments.

Specifically, the reliability of the GPS data may be determined by performing quality evaluation on the GPS data, for example, determining a difference in speed based on an assumption of uniform motion by using position information, speed information, and direction information in the historical GPS data; the difference in direction is determined based on the assumption that the direction is invariant. A specific process of determining the trustworthiness of GPS data will be set forth in the following embodiments.

Step S103: and performing data fusion processing on the predicted positioning information and the GPS data based on the credibility of the GPS data to determine fused positioning information.

The data fusion is to fuse the predicted positioning information corresponding to the inertial positioning data on the basis of the existing GPS data to obtain fused positioning information formed by the GPS data and the inertial positioning data. Specifically, a kalman filtering technique may be used to perform data fusion processing to obtain GPS/PDR fusion positioning or GPS/DR fusion positioning.

Step S104: and smoothing the fusion positioning information to determine a travel track.

Specifically, the travel track is obtained by connecting a plurality of positioning points, and therefore, when the travel track is determined, not only the positioning information of the current time needs to be determined, but also the positioning information of each time within the whole time period (whole time period) corresponding to the whole travel track needs to be calculated. In contrast, in the embodiment of the application, the fusion positioning information in the whole time period can be smoothed by adopting a traversal method in combination with an RTS algorithm, and a final travel track is determined.

A specific process for determining the predicted positioning information corresponding to the inertial positioning data will be described with reference to fig. 3.

In an embodiment, the inertial positioning data includes rotation vector data and accelerometer data. The step S102 of determining the predicted positioning information corresponding to the inertial positioning data includes the following steps A1-A4:

step A1: step detection and step length estimation are performed based on accelerometer data, and step length information is determined.

Specifically, the step detection is a module in an inertial sensor based user positioning system, and the classification of the step detection algorithm may include: time domain analysis and frequency domain analysis; in time domain analysis, when a user walks, an acceleration track acquired by an accelerometer in a portable terminal shows periodic change in time domain, and a step detection result can be obtained by detecting a threshold value in the acceleration track. In the frequency domain analysis, assuming that the frequency of the user walking is stable at about 2Hz, and the frequency of other behaviors is different from 2Hz, based on the condition, the frequency of the user walking can be extracted by short-term fourier transform (STFT), and if the frequency is about 2Hz, the user can be determined to step over.

Specifically, in the step length estimation, since the height and the walking manner of each user are different, the step length of each user is also different. Regarding the step length estimation, a constant model or a linear frequency model can be used for estimation. The principle of the constant model is to divide a walking distance obtained by measuring by the number of steps obtained in the step detection to obtain an average step length, namely the step length is regarded as a constant. The principle of the linear frequency model is that a linear relation presented by the step length and the frequency is obtained by analyzing and acquiring data of a plurality of preset steps of walking of users with different heights, and the calculation process of the linear model can be expressed by the following formula (1):

l = a f + b

In formula (1), the step size coefficients a and b may be obtained by training a large amount of acquired data offline, and f may be a step frequency, and then the obtained L represents the step size.

Alternatively, the linear frequency model of the step length estimation can also be a model for calculating based on the step frequency, the height of the user, and the step length, and the calculation process can be expressed by the following formula (2):

l = h (a f + b) + c

In formula (2), a, b and c are step size coefficients, h is the height of the user, f is the stride frequency, and L is the step size.

In an embodiment, other calculation principles may be used to construct the linear frequency model in the step length estimation, or an empirical model may be used to perform the step length estimation.

Step A2: direction information of travel is determined based on the rotation vector data.

Specifically, the step size of the user can be obtained in step a1, and based on this, the position of the user can be determined by combining the walking direction. Generally speaking, a magnetometer and a gyroscope sensor are arranged in the terminal, and can measure an included angle between the Y-axis of the terminal and the magnetic north pole when the Y-axis of the terminal projects to the horizontal plane, and determine the included angle as a direction angle of the terminal, so as to determine the walking direction of each step of the user.

Alternatively, the determination of the direction information of the travel in step a2 can be implemented by a technology such as fusion of inertial sensors, which is not limited in this application.

Step A3: the direction information is linearly detected based on the GPS position information of the GPS data to correct the direction information.

Specifically, it is understood that the walking direction of the user is the position change direction, and on this basis, the GPS position (at least two positions) currently determined in the GPS data may be used to determine whether the direction information predicted based on the inertial positioning data in step a2 deviates from the expected range, and if so, the direction information determined in step a2 may be corrected based on the GPS position to improve the accuracy of predicting the direction based on the inertial positioning data by using the PDR algorithm.

Step A4: and determining the step length information and the corrected direction information as predicted positioning information.

Specifically, the step information obtained in step a1 and the corrected direction information obtained in step A3 are determined as predicted positioning information, and are used as input data for the data fusion process in step S103.

Optionally, in consideration of that the accuracy of the direction prediction performed by using the PDR algorithm for the inertial positioning data of the same user is improved correspondingly with the iteration of time, on the basis, the step length information obtained in step a1 and the direction information obtained in step a2 may be determined as predicted positioning information, and further used as input data for the data fusion process in step S103.

Alternatively, as shown in fig. 3, the above embodiment is currently adaptively explained for a user walking scene. In one embodiment, when the application scenario is vehicle driving, the DR algorithm may be used to replace the execution of the above steps a1-a 4.

A specific process for determining the trustworthiness of GPS data is described below in conjunction with fig. 4.

In one embodiment, the GPS data includes at least one of GPS location information, GPS direction information, and GPS velocity information. Specifically, the related art can be adopted to determine the GPS position, direction and speed corresponding to the user based on the received GPS signal, which is not described in detail in this application. The step S102 of determining the reliability of the GPS data includes the step B1: determining the credibility of the GPS position information and/or the direction information based on at least one of the following (1) - (4):

(1) the difference of adjacent GPS positions within a preset time period.

Specifically, assuming that the current time is t3 (the time period before t3 is t 0-t 3), the time period [ x, t3] can be adopted as a preset time period, and all GPS position information in the preset time period is acquired to calculate the difference between adjacent GPS positions; where x is any one of [ t0, t 3). Wherein, assuming that the current preset time period is [ t1, t3], the GPS positions included in the time period include position 1, position 2 and position 3, then calculating a difference value a between position 1 and position 2, and calculating a difference value B between position 2 and position 3; and determining an expected range of adjacent GPS positions based on the walking speed of the user, wherein if the difference value A is within the expected range, the reliability of the difference value A is 1, if the difference value B is outside the expected range, the reliability of the difference value B is 0.5, and finally, taking the difference mean value 0.75 of all adjacent GP positions as the reliability of the GPS position information in the GPS data. Optionally, regarding the expected range, the set value of the confidence level of each difference value may be adjusted to adapt to practical applications, and the application does not limit the specific value (for example, when the difference value B is outside the expected range, the confidence level lower than 0.5 may be determined according to the distance exceeding the expected range, and the value range of the confidence level may be between [0,0.5 ]).

(2) The difference of adjacent GPS directions within a preset time period.

Specifically, assuming that the current time is t5 (the time period before t5 is t 0-t 5), the time period [ x, t5] can be adopted as a preset time period, and all GPS direction information in the preset time period is acquired to calculate the difference between adjacent GPS directions; where x is any one of [ t0, t 5). Wherein, it is assumed that the current preset time period is [ t4, t5], i.e. x = t 4; if the GPS directions included in the time period are a direction 4 and a direction 5, calculating a difference value C between the direction 4 and the direction 5; determining an expected range of adjacent GPS directions (certainly, other factors can also be adopted to determine the expected range) based on each position corresponding to walking of the user, wherein if the difference value C is within the expected range, the reliability of the difference value C is 1; if the difference value C is outside the expected range, the confidence level of the difference value C is 0.5. And then the corresponding credibility is used as the credibility of the GPS direction information in the GPS data.

(3) And the difference between the direction determined based on the plurality of GPS positions within the preset time period and the GPS direction at the current moment.

Specifically, assuming that the current time is t4 (the time period before t4 is t 0-t 4), the time period may be [ x, t4] used as a preset time period, and a plurality of pieces of GPS position information within the preset time period are acquired to determine the direction corresponding to the estimated t 4; where x is any one of [ t0, t 3). Assume that the current preset time period is [ t1, t4), i.e., x = t 1; the GPS position information included in the time period is position 1, position 2, and position 3. Firstly, the position 1, the position 2 and the position 3 can be adopted to calculate the direction of t 4; next, the predicted direction obtained by the estimation of t4 is compared with the GPS direction in the GPS data at the current time, the difference between the two is determined, and the reliability of the GPS direction information in the GPS data is determined based on the difference. Assume that the reliability of the GPS direction information in the currently obtained GPS data is 1.

(4) A difference between the position, direction and/or velocity determined based on the GPS data within a preset time period and the current time GPS position, direction and/or velocity, respectively.

Specifically, the principle of the implementation in item (4) is the same as that of the implementation in item (3), except that item (4) also determines a difference value between the estimated predicted position and the GPS position in the GPS data at the current time, and determines a difference value between the estimated predicted speed and the GPS speed in the GPS data at the current time.

Further, when the reliability of the GPS position information and the GPS direction information is determined in step B1 using the above-mentioned plural or all cases, the average value of the respective reliabilities may be used as the finally determined reliability value (for example, when the reliability of the GPS direction information is determined to be 0.5 in case (2), and the reliability of the GPS direction information is determined to be 1 in case (3), the average value of the two values is 0.75 as the reliability of the GPS direction information in the final GPS data).

The following describes a specific procedure of the data fusion process with reference to fig. 3.

In one embodiment, the step S103 of performing data fusion processing on the predicted positioning information and the GPS data based on the reliability of the GPS data to determine the fused positioning information includes the following steps C1-C2:

step C1: when determining the fused positioning information for the current time, performing the following data fusion steps C11-C13:

step C11: historical position information and historical direction information of a travel track at the current time are acquired based on GPS data.

Specifically, it is assumed that when the present embodiment is determining the position information and the direction information at the current time, calculation needs to be performed based on the position information and the direction information at the previous time, and therefore, the position information and the direction information in the GPS data may be adopted as the historical position information and the historical direction information for calculating the position information and the direction information at the current time, respectively, in the present embodiment.

When the overall time period corresponding to the travel track is assumed to be [ t0... t1.. t2], and when the position information and the direction information of the current time t1 are calculated, the historical position information and the historical direction information are the position information and the direction information corresponding to the time t 0; when the position information and the direction information at the current time t2 are calculated, the historical position information and the historical direction information comprise the position information and the direction information corresponding to the time t 0-t 1.

Step C12: determining the weight of the GPS data at the current moment based on the credibility of the GPS data;

alternatively, in combination with the example shown in step B1, it is assumed that the reliability of the GPS position information in the current GPS data is 1, and the reliability of the GPS direction information is 0.75; the reliability can be directly used as the weight of the corresponding GPS data during data fusion.

Alternatively, in combination with the example shown in step B1, it is assumed that the reliability of the GPS position information in the current GPS data is 1, and the reliability of the GPS direction information is 0.75; on the basis, each credibility can be multiplied by the corresponding coefficient to obtain the corresponding weight. If the preset position coefficient is 0.8, the weight =1 × 0.8=0.8 of the GPS position information in the GPS data; if the predetermined directional coefficient is 0.8, the weight =0.75 × 0.8=0.6 of the GPS directional information in the GPS data. The preset position coefficient and the preset direction coefficient may be determined by empirical values, and may be the same or different, which is not limited in the present application.

Step C13: and performing data fusion processing on the predicted positioning information and the GPS data based on the historical position information, the historical direction information and the current time weight of the GPS data to determine the fusion positioning information of the current time.

Specifically, the fusion of the GPS data and the predicted positioning information may be implemented by using a kalman filtering technique. The Kalman filter can be used, and the specific formula is as follows:

in the above formulas, formula (3) and formula (4) constitute a prediction part of kalman filtering, and formula (5), formula (6), and formula (7) constitute an update part of kalman filtering; wherein, X represents a state vector and can comprise four dimensions of latitude, longitude, speed and direction; p represents the covariance matrix of the state vector; k represents a weight matrix (also understood as kalman gain); f represents a state transition matrix; z represents an observation vector (which may be determined by position information, direction information, and velocity information in GPS data); r represents an observation noise matrix (the magnitude of R is related to the confidence level of the GPS data); b represents a control input matrix; q represents the covariance of the process of the system (which can be described by equation (3)); h is denoted as the system parameter.

The initial position, the initial direction and the weight of the GPS data are used as the data calculation basis of the Kalman filter, the predicted positioning information and the GPS data are used as the input data of the Kalman filter, and the fused positioning information at the current moment can be determined through the processing procedures described by the formulas (3) to (7), and can be used for representing the relevant information of the position, the speed and the direction of the user at the current moment.

Step C2: and taking the fusion positioning information as a part of the historical position information and the historical direction information acquired at the next moment, taking the next moment as the current moment, and repeatedly executing the data fusion step until the current moment is the end moment of the travel track.

Specifically, as shown in fig. 3, after the step C13 is adopted to determine the fusion positioning information at the current time, the step size coefficient in the fusion positioning information may be fed back to the step a1 to be used as a calculation basis for performing step size estimation at the next time; feeding back the fusion direction (the direction information of the current moment determined by fusing the GPS data and the inertial positioning data) in the fusion positioning information to step A2 and/or step A3 to be used as a calculation basis for estimating the advancing direction at the next moment; the position and direction fused in the fused positioning information are fed back to step C12 (and may also be fed back to step B1) to be used as a basis for calculating the weight for determining the GPS position and the GPS direction at the next time.

Next, a specific procedure of the data smoothing process will be described with reference to fig. 5.

In an embodiment, the method for processing the travel track provided in the foregoing embodiment is further optimized for the case of GPS signal interruption, and based on this, as shown in fig. 5, a specific process of the smoothing processing in the subsequent step S104 may be distinguished for the case of whether there is GPS signal interruption, specifically, the step S104 performs smoothing processing on the fusion positioning information to determine the travel track, including the step S1041: based on a target time period or a total time period corresponding to the advancing track, smoothing the fusion positioning information to determine the advancing track; and the target time period is obtained by expanding the corresponding interruption time period when the GPS signal is interrupted.

The total time period corresponding to the travel track can correspond to two conditions, (1) in a scene of calculating the travel track in real time, the total time period corresponding to the travel track refers to a time period corresponding to the current time from the starting time of travel; (2) in a scene of calculating the travel track in non-real time, for example, when the user views the motion track after finishing the motion, the total time period corresponding to the travel track is a time period corresponding to the starting time to the ending time of the travel.

Further, the step S1041 includes any one of the following steps D1 to D2:

step D1: acquiring a first intermediate variable in the fusion positioning information; and smoothing the first intermediate variable based on the total time period corresponding to the travel track to determine the travel track.

Specifically, the fused positioning information may also be expressed as kalman data (kalman data), and the first intermediate variable may also be expressed as a conventional intermediate variable, specifically including a state vector, a covariance matrix, and a state transition matrix. On the basis, the RTS algorithm can be adopted to carry out smoothing processing on the first intermediate variable to obtain a corresponding advancing track; specifically, the output from step D1 may be the positioning information (including position information and direction information) at the current time, and after calculating the positioning information at all times during the travel, the travel track may be determined accordingly.

Alternatively, the smoothed user position may be calculated in reverse order using a kalman smoother described by the following formula:

wherein the estimated state

The state of the system at time k is indicated,

is the system parameter at the time point k,

for the observed quantity, N is the corresponding probability density function, and F is the state transition matrix.

Step D2: responding to an instruction of GPS signal interruption, and acquiring a first intermediate variable in the fusion positioning information; expanding the corresponding interruption time period when the GPS signal is interrupted to obtain a target time period; and smoothing the first intermediate variable based on the target time period to determine a travel track.

Specifically, before step S104 is executed, it is determined whether there is a GPS outage currently using raw data in the GPS data (data directly interpreted based on the GPS signal), and if so, a command to abort the GPS signal is issued. In step D2, responding to the instruction of GPS signal interruption, acquiring a first intermediate variable in the fusion positioning information as basic data for determining the advancing track; considering that the accuracy of the positioning result before and after the GPS signal interruption is low, step D2 further expands the interruption time period corresponding to the GPS signal interruption (which may also be described as "increasing" the GPS interruption interval at both ends), to obtain a target time period; and further, the target time period is taken as the time of GPS signal interruption, the first intermediate variable is subjected to smoothing processing, and the travel track is determined. For example, the following steps are carried out: if the GPS signal is interrupted during the traveling process, the interruption interval (interruption time period) is [ t0, t1], the interval may be expanded, and the target time period after the expansion may be [ t0- Δ t0, t1+ Δ t1], that is, the GPS data corresponding to the [ t0- Δ t0, t0) (t1, t1+ Δ t1] time period is ignored.

Wherein, the difference between the step D1 and the step D2 is that the step D2 further extends the interruption time period of the GPS signal interruption, and the first intermediate variable is smoothed on the basis of ignoring the GPS data for a time period before and after the interruption time period.

Optionally, as shown in fig. 5, the expanding the interruption time period corresponding to the interruption of the GPS signal in the step D2 to obtain the target time period includes the following steps D21-D23:

step D21: and successively expanding the corresponding interruption time period when the GPS signal is interrupted based on the preset step length to obtain a plurality of initial time periods.

In particular, a maximum extension range, such as 60s, may be set for the interrupt period to extend ("increase"). For example with respect to step D21: assuming that the preset step length is 5s, the corresponding increasing process of each extended interruption time period is 5s, 10s and 15s.. 60 s; that is, the difference between the initial period [ t0- Δ t0, t1+ Δ t1] and the interruption period [ t0, t1] is incremented in 5s, 10s, 15s.

Step D22: a score calculation step is performed for each initial time period: determining position information and direction information corresponding to an initial time period; determining positioning error information of the initial time period based on the difference between the GPS position information and the GPS direction information in the GPS data and the position information and the direction information corresponding to the initial time period respectively; and determining the score of the initial time period based on a preset scoring strategy and the positioning error information of the initial time period.

Specifically, for the plurality of initial time periods obtained in step D21, step D22 performs score calculation for the initial time periods "growing" to different degrees by using a traversal method. The score calculation steps performed for the processing of one initial period are explained below: firstly, determining position information and direction information corresponding to each moment in an initial time period; further, the difference value between the position information and the direction information corresponding to each time in the initial time period and the position information and the direction information corresponding to each time in the GPS data is determined by taking the GPS data as a reference, and then the difference value at each time is used as the positioning error information of the initial time period; and determining the score of the initial time period according to the positioning error information of the initial time period by adopting a preset scoring strategy.

Step D23: and taking the initial time period with the highest score as a target time period.

Specifically, through the implementation of step D22, the scores corresponding to the initial time periods may be obtained, and then the initial time period with the highest score is selected as the target time period; i.e. the highest score characterizes the current "growth" best.

Optionally, with reference to fig. 6, the determining, in step D22, the position information and the direction information corresponding to the initial time period includes the following steps D221 to D222:

step D221: and determining position information and direction information corresponding to a first time period from the end time of the initial time period to the end time of the travel track based on the first intermediate variable.

Step D222: and determining position information and direction information corresponding to a second time period between the starting time of the travel track and the starting time of the initial time period based on the first intermediate variable.

Specifically, the corresponding position information and direction information may be calculated for the interruption time period of the GPS signal interruption in the manner of the above-mentioned step D1; and the calculation is performed using step D221 for a first period of time after the initial period of time with respect to the interruption period of time, and the calculation is performed using step D222 for a second period of time before the initial period of time with respect to the interruption period of time.

In connection with fig. 6, there is illustrated: assume that the overall time period (full time) of the current travel trajectory is [ t0 … t1 … t2 … t3 … t4 … t5], where t 1-t 2 is the first interruption and t 3-t 4 is the second interruption. For the first interruption t 1-t 2, increasing (expanding) to obtain [ t1- Δ t1, t2+ Δ t2] as an initial time period 1; in this case, the initial period 1 is (extended) the second period preceding the interruption period of the first interruption by [ t0, t1- Δ t1], and the first period following is (t 2+ Δ t2, t5 ]. Increasing the second interruption t 3-t 4 to obtain [ t 3-delta t3, t4+ delta t4] as an initial time period 2; in this case, the initial period 2 is [ t0, t3- Δ t3] with respect to the second period before the interruption period, and the first period after is (t 4+ Δ t4, t5 ].

Specifically, considering that the RTS algorithm is an algorithm that calculates the position information and the direction information in reverse order from the end time to the start time, when calculating the corresponding position information and the direction information for the following first time period (t 4+ Δ t4, t5), the position information and the direction information corresponding to the first time period are calculated by using the full-time conventional RTS algorithm (the RTS algorithm without the "increase" condition) (it is only required to calculate from the time t5 to t0 directly), when calculating the corresponding position information and the direction information for the preceding second time period [ t0, t3- Δ t3), the corresponding position information and the direction information are calculated from the time t3- Δ t3 to the time t0 by using the conventional RTS algorithm before GPS interruption (the RTS algorithm with the "increase" condition).

Optionally, with reference to fig. 5, on the basis of steps D221 to D222, the determining, in step D22, the position information and the direction information corresponding to the initial time period further includes the following steps D223 to D224:

step D223: and acquiring a second intermediate variable in the fusion positioning information.

Specifically, the second intermediate variable is also referred to as an additional intermediate variable in the embodiments of the present application, and includes a control input matrix, a weight matrix, and an observation noise matrix.

Step D224: and performing filtering processing on the position information and the direction information corresponding to the initial time period based on the second intermediate variable, and determining the position information and the direction information corresponding to the corrected initial time period.

Specifically, for the initial period, the state vector X and the covariance P are predicted using the second intermediate variable and the above equation (3) and equation (4) in the kalman filter.

Alternatively, for the position information and the direction information at each time instant (t2, t2+ Δ t 2) following the initial period [ t1- Δ t1, t2+ Δ t2] from the interruption period [ t1, t2], the state vector X and the covariance P in (t2, t2+ Δ t2] may be calculated using the second intermediate variable, using the state vector X and the covariance P at the corresponding time instant t2 before the end of the interruption period.

The embodiment of the application performs steps D223-D224 to remove the influence of the update part on the state vector X and the covariance P in the kalman filter.

Optionally, the step D22 of determining the score of the initial time period based on the preset scoring strategy and the positioning error information of the initial time period includes at least one of the following steps E1-E2:

step E1: and if the extreme value of the position difference or the extreme value of the direction difference in the positioning error information is larger than a first threshold value related to the interruption time period, determining the score of the initial time period according to the extreme value of the position difference or the extreme value of the direction difference.

Specifically, the positioning error information includes an extreme value of the position difference and an extreme value of the direction difference, and when determining the score of the initial time period, the extreme values of the position difference and the direction difference may be compared with the first threshold, respectively. The positioning error information includes position difference values at each time in the initial time period, and an extreme value of the position difference may be a maximum value of the position difference values at each time. The positioning error information includes direction difference values at each time in the initial time period, and an extreme value of the direction difference may be a maximum value of the direction difference values at each time.

Here, the first threshold corresponding to the location information may be determined using a difference between the GPS location a corresponding to the interruption start time t1 and the GPS location B corresponding to the interruption end time t2 in the interruption time period [ t1, t2 ]. That is, if the extreme value of the position difference is greater than the difference (absolute value) between the position a and the position B, determining the score corresponding to the initial time period according to the extreme value of the position difference; if the extreme value of the position difference is greater than the first threshold value when the full score is 100, the corresponding score between 0 and 50 may be taken as the score of the initial time period, and the specific value may be determined according to the magnitude of the extreme value of the difference (or 50 may be directly selected as the score of the initial time period).

Here, the first threshold corresponding to the direction information may be determined using a difference between the GPS direction C corresponding to the interruption start time t1 and the GPS direction D corresponding to the interruption end time t2 in the interruption time period [ t1, t2 ]. That is, if the extreme value of the direction difference is greater than the difference (absolute value) between the direction C and the direction D, determining the score corresponding to the initial time period according to the extreme value of the direction difference; if the extreme value of the direction difference is greater than the first threshold value when the full score is 100, the corresponding score between 0 and 50 may be taken as the score of the initial time period, and the specific value may be determined according to the magnitude of the extreme value of the difference (or 50 may be directly selected as the score of the initial time period).

Optionally, in step E1, when either the extreme value of the position difference or the extreme value of the direction difference in the initial time period is greater than the first threshold, it may be determined that a lower score is obtained; if the extreme value of the position difference and the extreme value of the direction difference in the initial time period are not greater than the first threshold, it can be determined that the initial time period corresponds to a higher score (for example, a value is taken between 80 and 100 according to the magnitude of the difference value, or 80 scores can be directly selected as the score of the initial time period).

Step E2: and if the ratio of the position difference or the ratio of the direction difference in the positioning error information is greater than a second threshold value related to the interruption time period, determining the score of the initial time period according to the ratio of the position difference or the ratio of the direction difference.

Specifically, the positioning error information includes a ratio of the position difference and a ratio of the direction difference, and when determining the score of the initial time period, the ratio of the position difference and the ratio of the direction difference may be compared with a second threshold, respectively. The positioning error information includes position difference values at each time in the initial time period, and the ratio of the position difference may be a ratio obtained by comparing the minimum value and the maximum value of the position difference values at each time. The positioning error information includes direction difference values at each time in the initial time period, and the ratio of the direction difference may be a ratio obtained by comparing the minimum value and the maximum value of the direction difference values at each time.

Wherein the second threshold corresponding to the position information may be determined using a ratio of a minimum value of the adjacent GPS position difference to a maximum value of the adjacent GPS position difference in the interrupt period [ t1, t2 ]. Namely, if the ratio of the position difference is greater than the ratio of the minimum value of the adjacent GPS position difference to the maximum value of the adjacent GPS position difference in the interruption time period [ t1, t2], determining the score corresponding to the initial time period according to the ratio of the position difference; if the ratio of the position difference is greater than the second threshold value, the corresponding score can be taken from 0 to 50 points as the score of the initial time period, and the specific value can be determined according to the size of the ratio of the difference (or 50 points can be directly selected as the score of the initial time period).

Wherein, the second threshold corresponding to the direction information may be determined by a ratio of a minimum value of the adjacent GPS direction difference to a maximum value of the adjacent GPS direction difference in the interrupt time period [ t1, t2 ]. If the ratio of the direction difference is greater than the ratio of the minimum value of the adjacent GPS direction difference to the maximum value of the adjacent GPS direction difference in the interruption time period [ t1, t2], determining the score corresponding to the initial time period according to the ratio of the direction difference; if the ratio of the direction difference is greater than the second threshold value, the corresponding score can be taken from 0 to 50 points as the score of the initial time period, and the specific value can be determined according to the size of the ratio of the difference (or 50 points can be directly selected as the score of the initial time period).

Optionally, in step E2, when either the ratio of the position difference or the ratio of the direction difference in the initial time period is greater than the second threshold, it may be determined that a lower score is obtained; if the ratio of the position difference and the ratio of the direction difference in the initial time period are not greater than the second threshold, it can be determined that the initial time period corresponds to a higher score (for example, a value is taken between 80 and 100 according to the ratio of the difference, or 80 can be directly selected as the score of the initial time period).

Optionally, in the above steps E1 and E2, a specific process of taking a value as the score of the initial time period within a certain numerical range according to the size of the extreme value of the difference or the size of the ratio of the difference is as follows, and a scenario in which the score of the initial time period is determined according to the value of the extreme value of the difference in step E1 between 0 and 50 is taken as an example to describe: the numerical range of 0-50 can be divided into 2 intervals including [0,25 ] and [25,50 "); assuming that the current time period comprises an initial time period 1 and an initial time period 2 (the extreme values of the position difference are both greater than the first threshold), wherein the extreme value of the position difference of the initial time period 1 is greater than the extreme value of the position difference of the initial time period 2, taking a random value between [0,25 ] for the initial time period 1 as the score of the initial time period 1; take a random value between [25,50) for initial period 2 as the score for initial period 2.

Step E3: and if the initial time period is the same as the interruption time period, determining the score of the initial time period according to the position difference value or the direction difference value in the positioning error information.

In one embodiment, the default scoring policy is also configured with a limited policy for the initial period of time (i.e., "grow" to 0 s) without an extended outage period. Specifically, a higher score value is configured for an initial time period that is the same as the interruption time period (a specific value is set, for example, 85), and the score value of the initial time period can also be determined by using the position difference value and the direction difference value in the initial time period; for example, a numerical value obtained by multiplying the mean value of the position difference values at each time in the initial time period by a preset score coefficient, or a numerical value obtained by multiplying the mean value of the direction difference values at each time in the initial time period by a preset score coefficient is taken.

In an embodiment, the processing method of the travel track provided in the foregoing embodiment may also be applied to a non-real-time travel process, for example, in some APP of sports service class, a user may start a GPS module of a terminal to record the travel track in a motion process (such as long-distance running and walking), and generally, the user does not check the travel track while moving, but checks the travel track in the motion process after the motion is finished. Therefore, when the user determines to end the movement (end the movement), and checks the additional movement track of the map, the situation that the movement track of the part is disordered and the positioning result is wrong due to the fact that the user passes through the underground passage in the movement process is found, and at the moment, the user can manually trigger the execution of the processing method of the movement track provided by the application, so that the movement track is corrected. That is, before acquiring the inertial positioning data and the GPS data related to the travel track in step S101, the method further includes the steps of: and receiving a correction instruction of the travel track. Optionally, when the user finishes moving and checks the travel track in the movement process, the client may automatically trigger and analyze whether the travel track to be displayed currently has the problems of jumping points, position errors, and a travel track terminal, and if so, automatically trigger and execute the processing method of the travel track, so as to obtain the corrected travel track to be displayed to the user.

In the above embodiment, in addition to the GPS/PDR fusion method and the GPS/DR fusion method explained in the embodiment, other fusion methods or fusion strategies, such as particle filtering, factor graph method, or Kalman filtering design method under different state vectors and different observation vectors, may be adopted for the data fusion processing in step S103. As for the smoothing processing in step S104, other smoothing methods than the RTS algorithm described in the embodiments may be used, such as: a global optimization based smoothing method, a machine learning based smoothing method, a deep learning based smoothing method, and the like are utilized.

Some application examples are given below for the processing method of the travel track provided in the embodiment of the present application with reference to fig. 7, 8a, and 8 b.

Specifically, as shown in fig. 7, the present application may be executed in an operating environment including a server 701 and a terminal 702. When the processing method of the travel track is executed in the terminal 702, the relevant data generated by the processing can be uploaded to the server 701 to be stored. When the method for processing the travel track is executed in the server 701, the travel track obtained by the processing may be fed back to the terminal 702 for presentation.

Specifically, as shown in fig. 8a, a travel track a obtained by connecting dots in fig. 8a is a travel track obtained without using the embodiment of the present application; the travel track B corresponding to the rough bending line is the travel track obtained by the embodiment of the application. Specifically, the difference between the travel track A and the travel track B when the user passes through an area with good GPS signals in the walking process and the GPS jumps for a long time when the user passes through an overpass is shown.

Specifically, as shown in fig. 8b, a travel track C obtained by connecting dots in fig. 8b is a travel track obtained without using the embodiment of the present application; and the travel track D corresponding to the rough bending line is the travel track obtained by the embodiment of the application. Specifically, the difference between the travel track C and the travel track D is that the GPS signal is interrupted for a long time when the user passes through the underground passage in the walking process, and the position precision is not high within a period of time after the GPS signal is interrupted after the user passes through the underground passage.

In an embodiment, as shown in fig. 9, there is provided a processing apparatus 600 of a travel trajectory, including: an acquisition module 901, a determination module 902, a fusion processing module 903, and a smoothing processing module 904.

Specifically, the acquiring module 901 is configured to acquire inertial positioning data and GPS data related to a travel track; a determining module 902, configured to determine predicted positioning information corresponding to the inertial positioning data and calculate a reliability of GPS data; a fusion processing module 903, configured to perform data fusion processing on the predicted positioning information and the GPS data based on the reliability of the GPS data, and determine fusion positioning information; and a smoothing module 904, configured to smooth the fusion positioning information and determine a travel track.

Optionally, the inertial positioning data comprises rotation vector data and accelerometer data; the determining module 902, when configured to perform the step of determining the predicted positioning information corresponding to the inertial positioning data, includes the following steps: performing step detection and step length estimation based on accelerometer data, and determining step length information; determining direction information of travel based on the rotation vector data; performing linear detection on the direction information based on GPS position information of the GPS data to correct the direction information; and determining the step length information and the corrected direction information as predicted positioning information.

Optionally, the GPS data includes at least one of GPS location information, GPS direction information, and GPS speed information; the determining module 902, when configured to perform the step of determining the trustworthiness of the GPS data, comprises performing the steps of: determining a trustworthiness of the GPS location information and/or the orientation information based on at least one of:

the difference of adjacent GPS positions in a preset time period;

the difference of adjacent GPS directions within a preset time period;

Optionally, the fusion processing module 903 is configured to perform data fusion processing on the predicted positioning information and the GPS data based on the reliability of the GPS data, and when the step of determining the fused positioning information includes the following steps:

when fusion positioning information is determined aiming at the current moment, the following data fusion steps are executed: acquiring historical position information and historical direction information of a travel track at the current moment based on GPS data; determining the weight of the GPS data at the current moment based on the credibility of the GPS data; performing data fusion processing on the predicted positioning information and the GPS data based on the historical position information, the historical direction information and the current time weight of the GPS data to determine the fusion positioning information of the current time;

Optionally, the smoothing module 904 is configured to perform smoothing on the fusion positioning information, and when the step of determining the travel track includes performing the following steps: based on a target time period or a total time period corresponding to the advancing track, smoothing the fusion positioning information to determine the advancing track; and the target time period is obtained by expanding the corresponding interruption time period when the GPS signal is interrupted.

Optionally, the smoothing module 904 is configured to perform smoothing on the fusion positioning information based on a target time period or a total time period corresponding to the travel track, and when the step of determining the travel track includes performing any one of the following:

acquiring a first intermediate variable in the fusion positioning information; smoothing the first intermediate variable based on the total time period corresponding to the advancing track to determine the advancing track;

the first intermediate variable comprises a state vector, a covariance matrix and a state transition matrix.

Optionally, the smoothing module 904 is configured to execute the step of extending the interrupt time period corresponding to the interrupt of the GPS signal to obtain the target time period, and includes the following steps:

Optionally, when the smoothing module 904 is configured to perform the step of determining the position information and the direction information corresponding to the initial time period, the following steps are performed:

determining position information and direction information corresponding to a first time period between the end time of the initial time period and the end time of the travel track based on the first intermediate variable;

Optionally, when the smoothing module 904 is configured to perform the step of determining the position information and the direction information corresponding to the initial time period, the following steps are further performed:

acquiring a second intermediate variable in the fusion positioning information;

the second intermediate variables include a control input matrix, a weight matrix, and an observed noise matrix.

Optionally, the smoothing module 904 is configured to perform the step of determining the score of the initial time period based on the preset scoring policy and the positioning error information of the initial time period, and includes at least one of:

The processing device of the travel track according to the embodiment of the present application may execute the processing method of the travel track provided by the embodiment of the present application, and the implementation principles thereof are similar, the actions performed by the modules in the processing device of the travel track according to the embodiments of the present application correspond to the steps in the processing method of the travel track according to the embodiments of the present application, and for the detailed functional description of the modules in the processing device of the travel track, reference may be specifically made to the description in the processing method of the corresponding travel track shown in the foregoing, and no further description is given here.

In an alternative embodiment, an electronic device is provided, as shown in fig. 10, the electronic device 1000 shown in fig. 10 comprising: a processor 1001 and a memory 1003. Where the processor 1001 is coupled to the memory 1003, such as via a bus 1002. Optionally, the electronic device 1000 may also include a transceiver 1004. It should be noted that the transceiver 1004 is not limited to one in practical application, and the structure of the electronic device 1000 is not limited to the embodiment of the present application.

The Processor 1001 may be a CPU (Central Processing Unit), a general-purpose Processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other Programmable logic device, a transistor logic device, a hardware component, or any combination thereof. Which may implement or perform the various illustrative logical blocks, modules, and circuits described in connection with the disclosure. The processor 1001 may also be a combination of computing functions, e.g., comprising one or more microprocessors, DSPs and microprocessors, and the like.

Bus 1002 may include a path that transfers information between the above components. The bus 1002 may be a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus 1002 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 10, but this is not intended to represent only one bus or type of bus.

The Memory 1003 may be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical Disc storage, optical Disc storage (including Compact Disc, laser Disc, optical Disc, digital versatile Disc, blu-ray Disc, etc.), a magnetic Disc storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to these.

The memory 1003 is used for storing application program codes for executing the present application, and the processor 1001 controls the execution. The processor 1001 is configured to execute application program codes stored in the memory 1003 to implement the contents shown in the foregoing method embodiments.

Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 10 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.

According to an aspect of the application, a computer program product or computer program is provided, comprising computer instructions, the computer instructions being stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the processing method of the travel track provided in the above-mentioned various alternative implementations.

The present application provides a computer-readable storage medium, on which a computer program is stored, which, when running on a computer, enables the computer to execute the corresponding content in the foregoing method embodiments. Compared with the prior art, the basic data for determining the travel track in the method provided by the application not only comprise GPS data, but also comprise inertial positioning data; then, determining predicted positioning information corresponding to the inertial positioning data, calculating the reliability of the GPS data, and fusing the predicted positioning information and the GPS data through filtering based on the reliability of the GPS data to determine fused positioning information; and in consideration of the instability problem of the GPS signal, the fusion positioning information is further subjected to smoothing processing, so that the accuracy of the advancing track of the user can be effectively improved, and the experience of the user in browsing the advancing track on a map interface is improved.

It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

It should be noted that the computer readable medium mentioned above in the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.

The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to perform the methods shown in the above embodiments.

Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The modules described in the embodiments of the present application may be implemented by software or hardware. The name of the module does not limit the module itself in some cases, and for example, the acquiring module may be further described as a "module for acquiring inertial positioning data and GPS data related to a travel track".

The above description mainly describes that the execution main body is hardware, and here, please briefly describe that the execution main body may be software. In some embodiments, the processing Device of the travel track provided by the embodiments of the present invention may be implemented by a combination of hardware and software, and by way of example, the processing Device of the travel track provided by the embodiments of the present invention may be a processor in the form of a hardware decoding processor, which is programmed to execute the processing method of the travel track provided by the embodiments of the present invention, for example, the processor in the form of a hardware decoding processor may be implemented 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.

In other embodiments, the processing device of the travel track provided by the embodiment of the present invention may be implemented by software, fig. 9 respectively shows the processing device 900 of the travel track stored in the memory, which may be software in the form of programs and plug-ins, and includes a series of modules, and the device 900 includes an obtaining module 901, a determining module 902, a fusing processing module 903, and a smoothing processing module 904; the processing method for realizing the travel track is provided by the embodiment of the application.

The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the disclosure. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.

Claims

1. A method for processing a travel track is characterized by comprising the following steps:

acquiring inertial positioning data and GPS data related to a traveling track;

2. The method of claim 1, wherein the inertial positioning data comprises rotation vector data and accelerometer data; the determining of the predicted positioning information corresponding to the inertial positioning data includes:

determining direction information of travel based on the rotation vector data;

3. The method of claim 1, wherein the GPS data includes at least one of GPS location information, GPS direction information, and GPS velocity information; determining a trustworthiness of the GPS data, comprising:

the difference of adjacent GPS positions in a preset time period;

the difference of adjacent GPS directions within a preset time period;

4. The method of claim 1, wherein performing data fusion processing on the predicted positioning information and the GPS data based on the reliability of the GPS data to determine fused positioning information comprises:

5. The method of claim 1, wherein smoothing the fused positioning information to determine a travel trajectory comprises:

6. The method according to claim 5, wherein the smoothing of the fused positioning information based on the target time period or the total time period corresponding to the travel track to determine the travel track comprises any one of:

7. The method of claim 6, wherein extending the corresponding interrupt time period when the GPS signal is interrupted to obtain the target time period comprises:

8. The method of claim 7, wherein determining the position information and the direction information corresponding to the initial time period comprises:

9. The method of claim 7, wherein determining the position information and the direction information corresponding to the initial time period further comprises:

acquiring a second intermediate variable in the fusion positioning information;

10. The method of claim 7, wherein determining the score for the initial time period based on the predetermined scoring strategy and the positioning error information for the initial time period comprises at least one of:

11. A travel track processing apparatus, comprising:

the acquisition module is used for acquiring inertial positioning data and GPS data related to the traveling track;

12. An electronic device, comprising:

one or more processors;

a memory;

one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more programs configured to: carrying out the method according to any one of claims 1 to 10.

13. A computer readable storage medium storing at least one instruction, at least one program, a set of codes, or a set of instructions, which is loaded and executed by a processor to implement the method according to any one of claims 1 to 10.