Pedestrian navigation course constraint method and device, electronic equipment, storage medium and program product

Abstract

The invention relates to a pedestrian navigation course constraint method and device, electronic equipment, a storage medium and a program product. The method comprises the following steps: acquiring inertial data of a target pedestrian; based on the inertial data, extracting the course of the target pedestrian at the current moment; in response to the situation that the course of the target pedestrian in the first sliding window meets a first preset straight going condition, the first straight going condition is determined based on difference information between the course of the current moment and the course of the target pedestrian in the first sliding window and / or error distribution of the course of the target pedestrian in a second sliding window; course constraint processing is conducted on the target pedestrian at the current moment, the right boundaries of the first sliding window and the second sliding window are the current moment, and the length of the second sliding window is larger than that of the first sliding window.

Description

Technical Field

[0001] The present disclosure relates to the technical field of indoor pedestrian navigation, and in particular to a pedestrian navigation heading constraint method, a pedestrian navigation heading constraint device, an electronic device, a computer-readable storage medium, and a computer program product. Background Art

[0002] Dead Reckoning (DR) refers to the process of estimating the future arrival position by using the known starting position combined with the estimated moving speed and direction over time without any external reference. Extending the above concept to the human motion model, estimating the step length and frequency of the movement based on the regularity and periodicity of the pedestrian's motion process is Pedestrian Dead Reckoning (PDR).

[0003] Pedestrian dead reckoning uses an inertial measurement unit (gyroscope and accelerometer) to estimate the heading and position of pedestrians, and has the advantages of autonomy, real-time, and low power consumption. After the initial information is given, the pedestrian's posture can be updated by measuring the angular velocity or angular increment obtained by the gyroscope to obtain the target's movement direction in the navigation coordinate system; the step length and number of steps of the pedestrian's movement can be estimated by the data of the accelerometer, or the speed and position of the pedestrian can be obtained by directly integrating the data of the accelerometer. This is the basic principle of indoor pedestrian navigation.

[0004] Pedestrian dead reckoning does not require prior information such as maps or additional base station deployment or external signals. It has strong autonomy and continuity, so it has attracted widespread attention from scholars. Regarding how to implement dead reckoning, scholars have proposed the following two methods:

[0005] The first is to use the traditional strapdown inertial navigation update algorithm, which is mainly divided into three parts: attitude update, velocity update and position update. This method is simple and fast, and can achieve high-precision dead reckoning by applying methods such as zero-speed correction, and does not require complex modeling of pedestrian step length and step frequency models, but its disadvantage is that it does not make good use of the human motion model.

[0006] The second method is to detect the step length and frequency of pedestrian movement by processing and analyzing the accelerometer data, such as adaptive step length estimation method, neural network step length estimation method, autocorrelation frequency measurement method, zero-crossing frequency measurement method, peak frequency measurement method, etc. At the same time, the attitude is updated according to the gyroscope data to obtain the direction of pedestrian movement. The positioning accuracy of this method is relatively high, and the error divergence is slow, but the model is more complex and the robustness of the model between different pedestrians is not high.

[0007] It can be seen that the accuracy of the pedestrian dead reckoning system will gradually decrease over time due to the continuous accumulation of errors in the inertial navigation system during the integration process. Especially the errors in attitude update. Once there is a large deviation in the attitude, the positioning accuracy of the entire navigation system will be greatly reduced. Summary of the invention

[0008] The present disclosure provides a technical solution for pedestrian navigation heading constraint.

[0009] According to one aspect of the present disclosure, a method for constraining a pedestrian navigation heading is provided, comprising:

[0010] Obtain inertial data of the target pedestrian;

[0011] Based on the inertial data, extracting the heading of the target pedestrian at the current moment;

[0012] In response to the target pedestrian's heading within the first sliding window satisfying a first preset straight-moving condition, based on difference information between the heading at the current moment and the heading of the target pedestrian within the first sliding window, and / or error distribution of the heading of the target pedestrian within the second sliding window, heading constraint processing is performed on the target pedestrian at the current moment, wherein the right boundaries of the first sliding window and the second sliding window are both the current moment, and the length of the second sliding window is greater than the length of the first sliding window.

[0013] In a possible implementation, obtaining the inertial data of the target pedestrian includes:

[0014] The inertial data of the target pedestrian is obtained by installing an inertial measurement unit on the foot of the target pedestrian.

[0015] In a possible implementation, extracting the heading of the target pedestrian at the current moment based on the inertial data includes:

[0016] Based on the inertial data, extracting the moment when the foot of the target pedestrian completely touches the ground;

[0017] At the moment when the foot is completely on the ground, the heading of the target pedestrian is extracted based on the inertial data to obtain the heading of the target pedestrian at the current moment.

[0018] In a possible implementation, extracting the moment when the foot of the target pedestrian completely touches the ground based on the inertial data includes:

[0019] For any moment, extract the amplitude of the inertial data at the moment, and calculate the standard deviation of the inertial data in a third sliding window, wherein the right boundary of the third sliding window is the moment;

[0020] In response to the amplitude being smaller than an amplitude threshold and the standard deviation being smaller than a standard deviation threshold, it is determined that the moment is the moment when the foot of the target pedestrian completely touches the ground.

[0021] In one possible implementation,

[0022] The amplitude of the inertial data includes: specific force amplitude and angular velocity amplitude;

[0023] The standard deviation of the inertial data includes: a standard deviation of specific force and a standard deviation of angular velocity;

[0024] The amplitude thresholds include: a specific force amplitude threshold and an angular velocity amplitude threshold;

[0025] The standard deviation thresholds include: a specific force standard deviation threshold and an angular velocity standard deviation threshold;

[0026] The response to the amplitude being less than an amplitude threshold, and the standard deviation being less than a standard deviation threshold, determining the moment as the moment when the foot of the target pedestrian completely touches the ground includes: responding to the specific force amplitude being less than the specific force amplitude threshold, the angular velocity amplitude being less than the angular velocity amplitude threshold, the specific force standard deviation being less than the specific force standard deviation threshold, and the angular velocity standard deviation being less than the angular velocity standard deviation threshold, determining the moment as the moment when the foot of the target pedestrian completely touches the ground.

[0027] In a possible implementation, extracting the heading of the target pedestrian at the current moment based on the inertial data includes:

[0028] Based on the inertial data, the b system is updated using the equivalent rotation vector method to obtain the quaternion of the b system attitude transformation;

[0029] Based on the inertial data, the n-system is updated using an equivalent rotation vector method to obtain a quaternion of the n-system attitude transformation;

[0030] Calculating the quaternion of the current posture of the target pedestrian according to the quaternion of the b-system posture transformation and the quaternion of the n-system posture transformation;

[0031] Based on the quaternion of the current posture, the heading of the target pedestrian at the current moment is obtained.

[0032] In a possible implementation manner, the first preset straight-moving condition includes:

[0033] The standard deviation of the heading is less than the heading standard deviation threshold.

[0034] In a possible implementation, the method further includes:

[0035] In response to the heading of the target pedestrian within the first sliding window not satisfying the first preset straight-moving condition, it is determined that the heading constraint is invalid at the current moment.

[0036] In a possible implementation, the performing heading constraint processing on the target pedestrian at the current moment based on the difference information between the heading at the current moment and the heading of the target pedestrian in the first sliding window, and / or the error distribution of the heading of the target pedestrian in the second sliding window, includes:

[0037] In response to the difference information between the heading at the current moment and the heading of the target pedestrian within the first sliding window satisfying a second preset straight-moving condition, applying a heading constraint to the target pedestrian at the current moment;

[0038] In response to the error distribution of the heading of the target pedestrian in the second sliding window not conforming to the Gaussian distribution, fault repair is performed on the heading constraint applied in the second sliding window.

[0039] In a possible implementation, the difference information between the current heading and the heading of the target pedestrian in the first sliding window satisfies the second preset straight-moving condition, including:

[0040] The absolute value of the difference between the heading at the current moment and the average heading of the target pedestrian in the first sliding window is less than the heading threshold.

[0041] In a possible implementation, applying a heading constraint to the target pedestrian at the current moment includes:

[0042] At the current moment, a heading constraint is imposed on the target pedestrian through a first Kalman filter.

[0043] In a possible implementation, the performing fault repair on the heading constraint applied in the second sliding window includes:

[0044] The second Kalman filter is used to perform fault repair on the heading constraint applied within the second sliding window.

[0045] In a possible implementation, the performing heading constraint processing on the target pedestrian at the current moment based on the difference information between the heading at the current moment and the heading of the target pedestrian in the first sliding window, and / or the error distribution of the heading of the target pedestrian in the second sliding window, further includes:

[0046] In response to the difference information between the heading at the current moment and the heading of the target pedestrian within the first sliding window not satisfying a second preset straight-moving condition, it is determined that the heading constraint at the current moment is invalid.

[0047] According to one aspect of the present disclosure, a pedestrian navigation heading constraint device is provided, comprising:

[0048] An acquisition module is used to obtain the inertial data of the target pedestrian;

[0049] An extraction module, used for extracting the heading of the target pedestrian at the current moment based on the inertial data;

[0050] A heading constraint processing module is used to respond to the heading of the target pedestrian in the first sliding window satisfying a first preset straight-moving condition, based on the difference information between the heading at the current moment and the heading of the target pedestrian in the first sliding window, and / or the error distribution of the heading of the target pedestrian in the second sliding window, perform heading constraint processing on the target pedestrian at the current moment, wherein the right boundaries of the first sliding window and the second sliding window are both the current moment, and the length of the second sliding window is greater than the length of the first sliding window.

[0051] In a possible implementation, the obtaining module is used to:

[0052] The inertial data of the target pedestrian is obtained by installing an inertial measurement unit on the foot of the target pedestrian.

[0053] In a possible implementation, the extraction module is used to:

[0054] Based on the inertial data, extracting the moment when the foot of the target pedestrian completely touches the ground;

[0055] At the moment when the foot is completely on the ground, the heading of the target pedestrian is extracted based on the inertial data to obtain the heading of the target pedestrian at the current moment.

[0056] In a possible implementation, the extraction module is used to:

[0057] For any moment, extract the amplitude of the inertial data at the moment, and calculate the standard deviation of the inertial data in a third sliding window, wherein the right boundary of the third sliding window is the moment;

[0058] In response to the amplitude being smaller than an amplitude threshold and the standard deviation being smaller than a standard deviation threshold, it is determined that the moment is the moment when the foot of the target pedestrian completely touches the ground.

[0059] In one possible implementation,

[0060] The amplitude of the inertial data includes: specific force amplitude and angular velocity amplitude;

[0061] The standard deviation of the inertial data includes: a standard deviation of specific force and a standard deviation of angular velocity;

[0062] The amplitude thresholds include: a specific force amplitude threshold and an angular velocity amplitude threshold;

[0063] The standard deviation thresholds include: a specific force standard deviation threshold and an angular velocity standard deviation threshold;

[0064] The extraction module is used to: in response to the specific force amplitude being less than the specific force amplitude threshold, the angular velocity amplitude being less than the angular velocity amplitude threshold, the specific force standard deviation being less than the specific force standard deviation threshold, and the angular velocity standard deviation being less than the angular velocity standard deviation threshold, determine that the moment is the moment when the target pedestrian's foot is completely on the ground.

[0065] In a possible implementation, the extraction module is used to:

[0066] Based on the inertial data, the b system is updated using the equivalent rotation vector method to obtain the quaternion of the b system attitude transformation;

[0067] Based on the inertial data, the n-system is updated using an equivalent rotation vector method to obtain a quaternion of the n-system attitude transformation;

[0068] Calculating the quaternion of the current posture of the target pedestrian according to the quaternion of the b-system posture transformation and the quaternion of the n-system posture transformation;

[0069] Based on the quaternion of the current posture, the heading of the target pedestrian at the current moment is obtained.

[0070] In a possible implementation manner, the first preset straight-moving condition includes:

[0071] The standard deviation of the heading is less than the heading standard deviation threshold.

[0072] In a possible implementation manner, the device further includes:

[0073] The failure determination module is configured to determine that the heading constraint is invalid at the current moment in response to the heading of the target pedestrian within the first sliding window not satisfying the first preset straight-moving condition.

[0074] In a possible implementation, the heading constraint processing module is used to:

[0075] In response to the difference information between the heading at the current moment and the heading of the target pedestrian within the first sliding window satisfying a second preset straight-moving condition, applying a heading constraint to the target pedestrian at the current moment;

[0076] In response to the error distribution of the heading of the target pedestrian in the second sliding window not conforming to the Gaussian distribution, fault repair is performed on the heading constraint applied in the second sliding window.

[0077] In a possible implementation, the difference information between the current heading and the heading of the target pedestrian in the first sliding window satisfies the second preset straight-moving condition, including:

[0078] The absolute value of the difference between the heading at the current moment and the average heading of the target pedestrian in the first sliding window is less than the heading threshold.

[0079] In a possible implementation, the heading constraint processing module is used to:

[0080] At the current moment, a heading constraint is imposed on the target pedestrian through a first Kalman filter.

[0081] In a possible implementation, the heading constraint processing module is used to:

[0082] The second Kalman filter is used to perform fault repair on the heading constraint applied within the second sliding window.

[0083] In a possible implementation, the heading constraint processing module is used to:

[0084] In response to the difference information between the heading at the current moment and the heading of the target pedestrian within the first sliding window not satisfying a second preset straight-moving condition, it is determined that the heading constraint at the current moment is invalid.

[0085] According to one aspect of the present disclosure, an electronic device is provided, comprising: one or more processors; a memory for storing executable instructions; wherein the one or more processors are configured to call the executable instructions stored in the memory to execute the above method.

[0086] According to one aspect of the present disclosure, a computer-readable storage medium is provided, on which computer program instructions are stored, and the computer program instructions implement the above method when executed by a processor.

[0087] According to one aspect of the present disclosure, a computer program product is provided, including a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in an electronic device, a processor in the electronic device executes the above method.

[0088] In the disclosed embodiment, by obtaining the inertial data of the target pedestrian, based on the inertial data, the heading of the target pedestrian at the current moment is extracted, and in response to the heading of the target pedestrian in the first sliding window satisfying the first preset straight-moving condition, based on the difference information between the heading at the current moment and the heading of the target pedestrian in the first sliding window, and / or the error distribution of the heading of the target pedestrian in the second sliding window, the heading constraint processing is performed on the target pedestrian at the current moment, wherein the right boundaries of the first sliding window and the second sliding window are both the current moment, and the length of the second sliding window is greater than the length of the first sliding window, thereby eliminating the need for prior information such as maps, and checking whether the pedestrian is walking straight by counting the error distribution of the heading of the pedestrian in multiple steps (i.e., the heading in the larger second sliding window). If the pedestrian is walking straight, the heading is constrained when the pedestrian's foot lands at each step. The disclosed embodiment can constrain the heading of the pedestrian navigation system in scenes such as long indoor corridors, solving the problem of dependence on prior information of buildings.

[0089] Heading error is the main error in pedestrian navigation. By adopting the pedestrian navigation heading constraint method provided in the embodiment of the present disclosure, the accuracy of pedestrian dead reckoning based on a low-cost inertial measurement unit can be improved, and can be applied to smart devices such as mobile phones and bracelets.

[0090] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

[0091] Further features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used to illustrate the technical solutions of the present disclosure together with the specification.

[0093] Figure 1 A flow chart of a pedestrian navigation heading constraint method provided by an embodiment of the present disclosure is shown.

[0094] Figure 2 It shows several states that the foot goes through during pedestrian movement.

[0095] Figure 3 A schematic diagram showing a gait extraction method in a pedestrian navigation heading constraint method provided in an embodiment of the present disclosure is shown.

[0096] Figure 4 A schematic diagram of a pedestrian navigation heading constraint method provided by an embodiment of the present disclosure is shown.

[0097] Figure 5 A schematic diagram showing pedestrians in a long radius turning section scenario.

[0098] Figure 6 A schematic diagram showing the heading constraint verification in the pedestrian navigation heading constraint method provided in an embodiment of the present disclosure is shown.

[0099] Figure 7 A schematic diagram of a second sliding window used for heading constraint verification in the pedestrian navigation heading constraint method provided in an embodiment of the present disclosure is shown.

[0100] Figure 8 A block diagram of a pedestrian navigation heading constraint device provided in an embodiment of the present disclosure is shown.

[0101] Fig. 9 A block diagram of an electronic device 1900 provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0102] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0103] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0104] The term "and / or" herein is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C can represent including any one or more elements selected from the set consisting of A, B, and C.

[0105] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present disclosure can also be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present disclosure.

[0106] In summary, the related technology has the following defects:

[0107] First, the positioning accuracy of indoor pedestrian navigation is closely related to hardware performance. Low-cost indoor pedestrian navigation systems can often only maintain high accuracy for a short period of time.

[0108] Second, for some indoor scenes, the spatial structure is characterized by a long and narrow area with buildings on both sides that strictly restrict the range of pedestrian activities. At this time, pedestrians have very obvious movement characteristics, that is, they habitually walk along the center line of the road. However, the indoor pedestrian navigation algorithm in related technologies rarely considers the indoor environment and constrains the error of the navigation system.

[0109] Third, some scholars use the Manhattan model of buildings to constrain pedestrian heading, but this requires prior knowledge of building information, which is not conducive to promotion in practical applications.

[0110] Fourth, the heading constraint method in the related art is less applicable to long-radius turning sections and is prone to causing erroneous heading constraints.

[0111] In order to solve the technical problems similar to those described above, the embodiment of the present disclosure provides a method for constraining the navigation heading of a pedestrian, by obtaining the inertial data of the target pedestrian, extracting the heading of the target pedestrian at the current moment based on the inertial data, and in response to the heading of the target pedestrian in the first sliding window satisfying the first preset straight-moving condition, based on the difference information between the heading at the current moment and the heading of the target pedestrian in the first sliding window, and / or the error distribution of the heading of the target pedestrian in the second sliding window, the heading of the target pedestrian is constrained at the current moment, wherein the right boundaries of the first sliding window and the second sliding window are both the current moment, and the length of the second sliding window is greater than the length of the first sliding window, thereby eliminating the need for prior information such as maps, and checking whether the pedestrian is walking straight by counting the error distribution of the heading of the pedestrian in multiple steps (i.e., the heading in the larger second sliding window). If the pedestrian is walking straight, the heading is constrained when the pedestrian's foot lands at each step. The embodiment of the present disclosure can constrain the heading of the pedestrian navigation system in scenes such as long indoor corridors, solving the problem of dependence on prior information of buildings.

[0112] Heading error is the main error in pedestrian navigation. By adopting the pedestrian navigation heading constraint method provided in the embodiment of the present disclosure, the accuracy of pedestrian dead reckoning based on a low-cost inertial measurement unit can be improved, and can be applied to smart devices such as mobile phones and bracelets.

[0113] The pedestrian navigation heading constraint method provided by the embodiment of the present disclosure is described in detail below with reference to the accompanying drawings.

[0114] Figure 1A flow chart of a pedestrian navigation heading constraint method provided by an embodiment of the present disclosure is shown. In one possible implementation, the executor of the pedestrian navigation heading constraint method may be a pedestrian navigation heading constraint device. For example, the pedestrian navigation heading constraint method may be executed by a terminal device or a server or other electronic device. The terminal device may be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device or a wearable device. In some possible implementations, the pedestrian navigation heading constraint method may be implemented by a processor calling computer-readable instructions stored in a memory. For example Figure 1 As shown, the pedestrian navigation heading constraint method includes steps S11 to S13.

[0115] In step S11 , inertial data of the target pedestrian is obtained.

[0116] In step S12, the heading of the target pedestrian at the current moment is extracted based on the inertial data.

[0117] In step S13, in response to the heading of the target pedestrian in the first sliding window satisfying the first preset straight-moving condition, based on the difference information between the heading at the current moment and the heading of the target pedestrian in the first sliding window, and / or the error distribution of the heading of the target pedestrian in the second sliding window, the target pedestrian is subjected to heading constraint processing at the current moment, wherein the right boundaries of the first sliding window and the second sliding window are both the current moment, and the length of the second sliding window is greater than the length of the first sliding window.

[0118] In the disclosed embodiments, the target pedestrian may represent an object of interest for the pedestrian navigation system. In the field of pedestrian dead reckoning and indoor pedestrian navigation, the movement of the target pedestrian needs to be measured and analyzed to provide accurate navigation information. The target pedestrian may be any individual using the pedestrian navigation system, such as a consumer who needs navigation services in a large shopping mall, airport, exhibition, or other indoor environment, or a pedestrian who needs precise navigation in an outdoor environment such as a city or canyon where the GPS (Global Positioning System) signal is poor.

[0119] In the disclosed embodiment, inertial data may refer to data collected by an inertial measurement unit (IMU). Inertial data may include acceleration, angular velocity, etc., and may be used to calculate information such as the step length, step frequency, posture, and heading of the target pedestrian.

[0120] In a possible implementation, the obtaining of the inertial data of the target pedestrian includes: obtaining the inertial data of the target pedestrian by installing an inertial measurement unit on the foot of the target pedestrian. In this implementation, the inertial data of the target pedestrian is obtained by installing an inertial measurement unit on the foot of the target pedestrian, thereby obtaining more accurate footstep landing moment (i.e., the moment when the foot of the target pedestrian completely lands on the ground) and gait information, thereby improving the indoor navigation accuracy.

[0121] In the embodiment of the present disclosure, the heading of the target pedestrian may refer to the moving direction of the target pedestrian in the navigation coordinate system.

[0122] In one possible implementation, extracting the heading of the target pedestrian at the current moment based on the inertial data includes: extracting the moment when the target pedestrian's feet are completely on the ground based on the inertial data; and at the moment when the feet are completely on the ground, extracting the heading of the target pedestrian based on the inertial data to obtain the heading of the target pedestrian at the current moment.

[0123] In this implementation, the footstep movement of the target pedestrian can be monitored by analyzing the inertial data collected by the inertial measurement unit installed on the foot of the target pedestrian. Figure 2 The figure shows the states that the foot goes through during the pedestrian movement: heel on the ground, toe on the ground, foot completely on the ground, heel off the ground, toe off the ground (i.e. Figure 2 When the foot is completely on the ground, it is in a relatively static state, and the heading at this moment is more accurate. Therefore, in this implementation, the gait with the foot completely on the ground can be used as the target for extraction.

[0124] In this implementation, the moment when the target pedestrian's feet completely touch the ground is extracted based on the inertial data, and at the moment when the feet completely touch the ground, the heading of the target pedestrian at the current moment is extracted based on the inertial data, thereby obtaining the heading of the target pedestrian at the current moment. By extracting the heading of the target pedestrian at the current moment when the target pedestrian's feet completely touch the ground, the heading estimation error caused by footstep movement can be reduced, thereby improving the accuracy of pedestrian positioning.

[0125] In one possible implementation, extracting the moment when the foot of the target pedestrian completely touches the ground based on the inertial data includes: for any moment, extracting the amplitude of the inertial data at the moment, and calculating the standard deviation of the inertial data in a third sliding window, wherein the right boundary of the third sliding window is the moment; in response to the amplitude being less than an amplitude threshold and the standard deviation being less than a standard deviation threshold, determining that the moment is the moment when the foot of the target pedestrian completely touches the ground.

[0126] In this implementation, for any moment, in response to the amplitude of the inertial data at the moment being greater than or equal to an amplitude threshold, or the standard deviation of the inertial data within the third sliding window being greater than or equal to a standard deviation threshold, it can be determined that the moment is not the moment when the target pedestrian's foot is completely on the ground.

[0127] For pedestrian navigation systems, accurate gait extraction is very important. Only by accurately extracting the moment when the pedestrian's foot completely touches the ground can the step frequency be estimated, the zero speed be corrected, and the heading be constrained. In this implementation, for the pedestrian navigation system installed on the foot, by judging the amplitude of the inertial data and the standard deviation of the inertial data in the third sliding window, it is possible to suppress noise and errors to a certain extent, thereby improving the accuracy of the extracted moment when the foot completely touches the ground.

[0128] In a possible implementation, the amplitude of the inertial data includes: a specific force amplitude and an angular velocity amplitude; the standard deviation of the inertial data includes: a specific force standard deviation and an angular velocity standard deviation; the amplitude threshold includes: a specific force amplitude threshold and an angular velocity amplitude threshold; the standard deviation threshold includes: a specific force standard deviation threshold and an angular velocity standard deviation threshold; the response to the amplitude being less than the amplitude threshold, and the standard deviation being less than the standard deviation threshold, determining that the moment is the moment when the target pedestrian's foot is completely on the ground includes: responding to the specific force amplitude being less than the specific force amplitude threshold, the angular velocity amplitude being less than the angular velocity amplitude threshold, the specific force standard deviation being less than the specific force standard deviation threshold, and the angular velocity standard deviation being less than the angular velocity standard deviation threshold, determining that the moment is the moment when the target pedestrian's foot is completely on the ground.

[0129] For the inertial measurement unit mounted on the foot, its six-axis output can be written as:

[0130] o=[f x ,f y ,f z ,ω x ,ω y ,ω z ] T

[0131] Among them, f i Can represent the specific force output of the i-axis, ω i It can represent the angular velocity output of the i-axis.

[0132] In this implementation, the amplitude of the angular velocity and specific force measured by the inertial measurement unit and the standard deviation within the third sliding window can be taken into account, so as to accurately capture the moment when the target pedestrian's feet completely touch the ground.

[0133] Figure 3A schematic diagram of a gait extraction method in a pedestrian navigation heading constraint method provided by an embodiment of the present disclosure is shown. In this implementation, first, the system can be left still for about 10 seconds before starting to work, and the average value output during this period is taken as a constant zero bias and subtracted from the subsequent data. Then, the three-axis specific force vector and amplitude and the three-axis angular velocity vector and amplitude output at each moment can be calculated. Next, a third sliding window of length n can be set to calculate the angular velocity standard deviation and the specific force standard deviation in each third sliding window. Since the foot is relatively still at the moment of complete landing, the three-axis specific force vector and amplitude, the three-axis angular velocity vector and amplitude, the angular velocity standard deviation and the specific force standard deviation output by the inertial measurement unit should all be approximately 0. Therefore, in this implementation, appropriate specific force amplitude threshold f1, angular velocity amplitude threshold ω1, specific force standard deviation threshold f2 and angular velocity standard deviation threshold ω2 can be set to extract the gait of the target pedestrian. If the result is less than the corresponding threshold (i.e., the specific force amplitude is less than the specific force amplitude threshold, the angular velocity amplitude is less than the angular velocity amplitude threshold, the specific force standard deviation is less than the specific force standard deviation threshold, and the angular velocity standard deviation is less than the angular velocity standard deviation threshold), it can be indicated that the detection is passed, that is, it can be determined that the target pedestrian's feet are completely on the ground. The following four expressions:

[0134]

[0135] in The vector sum of the measured values ​​of the specific force can be represented by The vector sum of the measured values ​​of angular velocity, σ f Can represent the standard deviation of the measurement of specific force, σ ω It can represent the standard deviation of the angular velocity measurements.

[0136] like Figure 3 As shown, if the measurement result at a certain moment passes the specific force amplitude detection, angular velocity amplitude detection, specific force sliding standard deviation detection and angular velocity sliding standard deviation detection at the same time, it can be determined that the foot is in a completely grounded state, otherwise it can be determined that the foot is not completely grounded. That is, for any moment, if the specific force amplitude at that moment is greater than or equal to the specific force amplitude threshold, it can be determined that the moment is not the moment when the foot of the target pedestrian is completely grounded; for any moment, if the angular velocity amplitude at that moment is greater than or equal to the angular velocity amplitude threshold, it can be determined that the moment is not the moment when the foot of the target pedestrian is completely grounded; for any moment, if the specific force standard deviation at that moment is greater than or equal to the specific force standard deviation threshold, it can be determined that the moment is not the moment when the foot of the target pedestrian is completely grounded; for any moment, if the angular velocity standard deviation at that moment is greater than or equal to the angular velocity standard deviation threshold, it can be determined that the moment is not the moment when the foot of the target pedestrian is completely grounded.

[0137] In the disclosed embodiment, the heading of the target pedestrian at the current moment may be extracted based on the inertial data.

[0138] In a possible implementation, the posture of the target pedestrian may be updated based on the inertial data, and the heading of the target pedestrian may be determined based on the updated posture information.

[0139] For example, attitude updates may include: using gyroscope data to determine angular velocity and then inferring rotational changes in attitude; using accelerometer data to correct attitude errors caused by gravity; and fusing the above data to obtain a more accurate attitude estimate.

[0140] As an example of this implementation, the attitude of the target pedestrian can be recursively obtained by strapdown inertial navigation attitude update algorithm. Strapdown inertial navigation is a technology that uses an inertial measurement unit for navigation. It does not rely on external references, but determines the current position and attitude by calculating continuous motion starting from an initial position. For example, the strapdown inertial navigation attitude update algorithm may include: using the angular velocity measured by a gyroscope to update the heading; using the acceleration measured by an accelerometer to update the speed and position; integrating the above updated data through a mathematical model (such as a quaternion or a rotation matrix) to obtain the current attitude estimate.

[0141] In a possible implementation, the extracting the heading of the target pedestrian at the current moment based on the inertial data includes: based on the inertial data, updating the b system using the equivalent rotation vector method to obtain the quaternion of the b system attitude transformation; based on the inertial data, updating the n system using the equivalent rotation vector method to obtain the quaternion of the n system attitude transformation; calculating the quaternion of the current posture of the target pedestrian based on the quaternion of the b system attitude transformation and the quaternion of the n system attitude transformation; and obtaining the heading of the target pedestrian at the current moment based on the quaternion of the current posture.

[0142] In this implementation, the first step is to update the B system based on the inertial data using the equivalent rotation vector method to obtain the quaternion of the B system attitude transformation for

[0143]

[0144] in The corresponding equivalent rotation vector can be expressed as, It can represent the modulus of the equivalent rotation vector. If the twin-sample algorithm is used, then where Δθ k is the current angular increment output, Δθ k-1 Output is the angular increment of the previous epoch.

[0145] In the second step, based on the inertial data, the n-system can be updated using the equivalent rotation vector method to obtain the quaternion of the n-system attitude transformation for

[0146]

[0147] where ξ k represents the equivalent rotation vector corresponding to the n-system transformation, then

[0148]

[0149] in Represents the n-system rotation caused by the Earth's rotation (the Earth's rotation angular velocity). Represents the n-system rotation caused by the curvature of the earth's surface when the inertial navigation system moves near the earth's surface. is the latitude (in radians), v E and v N are the eastward and northward velocities, R M and R N They are the meridian radius and the meridian radius respectively.

[0150] In inertial navigation and geophysics, B-frame and N-frame are two commonly used coordinate systems, which are used to describe and calculate the motion of objects on the earth. B-frame is a coordinate system fixed to a carrier (such as an aircraft, ship or pedestrian), and it moves and rotates with the carrier. In a strapdown inertial navigation system, sensors (such as gyroscopes and accelerometers) are fixed to the carrier, and the measured data is relative to the B-frame. These data need to be converted to a geographic coordinate system or a navigation coordinate system in order to calculate the position and velocity. N-frame is a geographic coordinate system, which is usually fixed to the earth and defined by the longitude, latitude and altitude of the earth. In navigation calculations, N-frame can be used to define position and velocity. For example, in the Earth-centered inertial coordinate system (ECI) or the local horizontal coordinate system, the position and velocity are usually expressed in terms of the north, east and down coordinates of the N-frame.

[0151] The third step is to calculate the quaternion of the current posture of the target pedestrian (that is, the quaternion of the posture of the target pedestrian at the current moment), then we have

[0152]

[0153] in represents quaternion multiplication, Represents the quaternion of the attitude at the previous moment, Represents the recursive attitude quaternion at the current moment.

[0154] As an example of this implementation, after calculating the quaternion of the current posture of the target pedestrian, the updated posture quaternion can be normalized, and then

[0155]

[0156] Therefore, the heading of the target pedestrian at the current moment can be obtained according to the quaternion of the current posture through the principle of converting quaternion to Euler angle. Similarly, the heading of the target pedestrian when each step is completely on the ground can be obtained.

[0157] In this implementation, the b system is updated based on the inertial data using the equivalent rotation vector method to obtain the quaternion of the b system attitude transformation, and the n system is updated based on the inertial data using the equivalent rotation vector method to obtain the quaternion of the n system attitude transformation. According to the quaternion of the b system attitude transformation and the quaternion of the n system attitude transformation, the quaternion of the current posture of the target pedestrian is calculated, and based on the quaternion of the current posture, the heading of the target pedestrian at the current moment is obtained, thereby effectively avoiding the gimbal lock problem, improving numerical stability and computational efficiency, and achieving more accurate, continuous and stable pedestrian heading estimation, thereby enhancing the robustness and positioning accuracy of the indoor navigation system in real-time response and complex dynamic environments.

[0158] In the embodiment of the present disclosure, a first sliding window and a second sliding window may be set, wherein the right boundaries of the first sliding window and the second sliding window are both the current moment, and the size of the second sliding window is larger than the size of the first sliding window. In one example, the length of the first sliding window may be recorded as W, and the length of the second sliding window may be recorded as Q, wherein W<Q. The first sliding window may be used to preliminarily determine whether the target pedestrian maintains a straight state, and the second sliding window may be used to verify whether the target pedestrian maintains a straight state.

[0159] In a possible implementation manner, the first preset straight-traveling condition includes: a standard deviation of the heading is less than a heading standard deviation threshold.

[0160] In this implementation, in response to the standard deviation of the target pedestrian's heading within the first sliding window being less than a heading standard deviation threshold, the target pedestrian may be subjected to heading constraint processing at the current moment based on difference information between the heading at the current moment and the heading of the target pedestrian within the first sliding window, and / or the error distribution of the heading of the target pedestrian within the second sliding window.

[0161] In another possible implementation manner, the first preset straight-traveling condition includes: a heading variance is less than a heading variance threshold.

[0162] In this implementation, in response to the variance of the target pedestrian's heading within the first sliding window being less than a heading variance threshold, the target pedestrian may be subjected to heading constraint processing at the current moment based on difference information between the heading at the current moment and the heading of the target pedestrian within the first sliding window, and / or the error distribution of the heading of the target pedestrian within the second sliding window.

[0163] In a possible implementation, the method further includes: in response to the heading of the target pedestrian within the first sliding window not satisfying the first preset straight-moving condition, determining that the heading constraint is invalid at the current moment.

[0164] As an example of this implementation, in response to the standard deviation of the heading of the target pedestrian in the first sliding window being greater than or equal to a heading standard deviation threshold, it can be determined that the heading constraint is invalid at the current moment, that is, no heading constraint is performed at the current moment.

[0165] In the disclosed embodiment, in response to the target pedestrian's heading in the first sliding window satisfying the first preset straight-moving condition, the target pedestrian may be subjected to heading constraint processing at the current moment based on the difference information between the current moment's heading and the target pedestrian's heading in the first sliding window, and / or the error distribution of the target pedestrian's heading in the second sliding window. The target pedestrian's heading may be integrated based on the target pedestrian's inertial data in the second sliding window, and the error distribution of the target pedestrian's heading may be statistically calculated.

[0166] Gaussian distribution (i.e., normal distribution) has a clear central tendency, that is, the data fluctuates around the mean, and the probability of occurrence gradually decreases as the distance from the mean increases. In pedestrian navigation, if the target pedestrian is in a straight-ahead state, their heading changes should be relatively stable, and the error (i.e., the deviation between the actual heading and the expected heading) should fluctuate randomly around zero error, which is consistent with the characteristics of Gaussian distribution.

[0167] In the disclosed embodiment, an error distribution can be obtained by statistically analyzing the heading error of the target pedestrian over a period of time. Statistical tests (such as the Shapiro-Wilk test) can be used to evaluate whether this distribution conforms to the Gaussian distribution. If the test results show that the error distribution is not significantly different from the Gaussian distribution, that is, the null hypothesis is accepted, then it can be considered that the pedestrian is in a straight state. If the heading error distribution does not conform to the Gaussian distribution, it means that the error does not fluctuate randomly, and there may be systematic deviations or non-random patterns, which usually indicates that the pedestrian is not going straight, but is turning or performing other non-straight movements. In this case, the error may increase systematically over time, reflecting the continuous changes in the pedestrian's heading, rather than random noise.

[0168] In a possible implementation, in response to the target pedestrian's heading within the first sliding window satisfying the first preset straight-moving condition, the target pedestrian may be subjected to heading constraint processing at the current moment based on the difference information between the heading at the current moment and the heading of the target pedestrian within the first sliding window. For example, in response to the difference information between the heading at the current moment and the heading of the target pedestrian within the first sliding window not satisfying the second preset straight-moving condition, it may be determined that the target pedestrian is not in a straight-moving state, and it is determined that the heading constraint is invalid at the current moment.

[0169] In another possible implementation, in response to the target pedestrian's heading within the first sliding window satisfying the first preset straight-moving condition, the target pedestrian may be subjected to heading constraint processing at the current moment based on the difference information between the heading at the current moment and the heading of the target pedestrian within the first sliding window, and the error distribution of the heading of the target pedestrian within the second sliding window.

[0170] As an example of this implementation method, in response to the difference information between the heading at the current moment and the heading of the target pedestrian in the first sliding window satisfying the second preset straight-moving condition, and the error distribution of the heading of the target pedestrian in the second sliding window conforms to the Gaussian distribution, it can be determined that the target pedestrian is in a straight-moving state, and a heading constraint is imposed on the target pedestrian at the current moment.

[0171] As another example of this implementation method, in response to the difference information between the heading at the current moment and the heading of the target pedestrian in the first sliding window satisfying the second preset straight-moving condition, and the error distribution of the heading of the target pedestrian in the second sliding window does not conform to the Gaussian distribution, it can be determined that the target pedestrian is not in a straight-moving state, it is determined that the heading constraint at the current moment is invalid, and the heading constraint applied in the second sliding window is repaired.

[0172] In another possible implementation, in response to the target pedestrian's heading in the first sliding window satisfying the first preset straight-moving condition, the target pedestrian may be subjected to heading constraint processing at the current moment based on the error distribution of the target pedestrian's heading in the second sliding window. For example, in response to the error distribution of the target pedestrian's heading in the second sliding window not conforming to the Gaussian distribution, it may be determined that the target pedestrian is not in a straight-moving state, it may be determined that the heading constraint is invalid at the current moment, and the heading constraint applied in the second sliding window may be repaired.

[0173] In the disclosed embodiment, when the target pedestrian's heading in the first sliding window satisfies the first preset straight-moving condition, and the difference information between the current heading and the target pedestrian's heading in the first sliding window satisfies the second preset straight-moving condition, the error distribution of the target pedestrian's heading in the second sliding window may be compared with the Gaussian distribution to verify whether the target pedestrian is going straight. If the error distribution of the target pedestrian's heading in the second sliding window conforms to the Gaussian distribution, it usually means that the error is random and the target pedestrian is likely to be going straight; if the error distribution of the target pedestrian's heading in the second sliding window does not conform to the Gaussian distribution, it may mean that the target pedestrian is turning or making other non-straight movements.

[0174] In the embodiment of the present disclosure, the characteristics of Gaussian distribution can be used to more accurately identify the straight-moving state of pedestrians, thereby applying heading constraints in the straight-moving state, reducing cumulative errors, and improving positioning accuracy.

[0175] In a possible implementation, based on the difference information between the heading at the current moment and the heading of the target pedestrian in the first sliding window, and / or the error distribution of the heading of the target pedestrian in the second sliding window, the target pedestrian is subjected to heading constraint processing at the current moment, including: in response to the difference information between the heading at the current moment and the heading of the target pedestrian in the first sliding window satisfying a second preset straight-moving condition, applying a heading constraint to the target pedestrian at the current moment; in response to the error distribution of the heading of the target pedestrian in the second sliding window not conforming to a Gaussian distribution, performing fault repair on the heading constraint applied in the second sliding window.

[0176] In this implementation, if the target pedestrian's heading within the first sliding window satisfies the first preset straight-moving condition, and the difference information between the current heading and the target pedestrian's heading within the first sliding window satisfies the second preset straight-moving condition, it can be preliminarily determined that the target pedestrian is in a straight-moving state, and a heading constraint can be imposed on the target pedestrian at the current moment.

[0177] As an example of this implementation method, the difference information between the heading at the current moment and the heading of the target pedestrian in the first sliding window satisfies the second preset straight-moving condition, including: the absolute value of the difference between the heading at the current moment and the average heading of the target pedestrian in the first sliding window is less than the heading threshold.

[0178] As another example of this implementation method, the difference information between the heading at the current moment and the heading of the target pedestrian in the first sliding window satisfies the second preset straight-moving condition, including: the absolute value of the difference between the heading at the current moment and the weighted average of the heading of the target pedestrian in the first sliding window is less than the heading threshold.

[0179] As another example of this implementation method, the difference information between the heading at the current moment and the heading of the target pedestrian in the first sliding window satisfies the second preset straight-moving condition, including: the absolute value of the difference between the median of the heading at the current moment and the heading of the target pedestrian in the first sliding window is less than the heading threshold.

[0180] As an example of this implementation, applying a heading constraint to the target pedestrian at the current moment includes: at the current moment, applying a heading constraint to the target pedestrian through a first Kalman filter.

[0181] The following introduces the heading constraint implementation of the first Kalman filter. In this example, the state model of the pedestrian navigation system can be given as follows:

[0182]

[0183] Where X k is the state of the pedestrian at time k, They contain three-dimensional posture and three-dimensional velocity respectively. H is the measurement matrix of the model, and the specific expression of H is as follows:

[0184]

[0185] In this example, the Kalman filter can be updated to the system when the target pedestrian's feet are completely on the ground, that is, the heading constraint is imposed, where the imposed observation is

[0186] Figure 4 FIG. 1 is a schematic diagram showing a method for constraining a pedestrian navigation heading provided by an embodiment of the present disclosure. Figure 4 As shown, the attitude of the target pedestrian can be updated by strapdown inertial navigation. The moment when the feet of the target pedestrian are completely on the ground can be extracted based on the inertial data of the target pedestrian, and the heading of the target pedestrian can be extracted based on the inertial data at the moment when the feet are completely on the ground. In this way, the heading of the target pedestrian at each moment when the feet of the target pedestrian are completely on the ground over a period of time can be recorded. The length W of the first sliding window and the heading standard deviation threshold Y can be set appropriately. The heading obtained based on the attitude-based quaternion can be judged. If the standard deviation of the heading of the target pedestrian in the first sliding window is less than the heading standard deviation threshold Y, it can be considered that the target pedestrian in the first sliding window is in a straight state, and the mean of the heading in the first sliding window can be calculated as the next constrained heading Y res Otherwise, it can be considered that the target pedestrian is not moving straight in the first sliding window, and the sliding window is continued to find the heading of the target pedestrian when moving straight. When the target pedestrian is in a straight state, the strapdown inertial navigation attitude update algorithm can be used to obtain the heading Y when the next foot is completely on the ground.now If Y now and Y res The absolute value of the difference |Y now -Y res | is less than the heading threshold T, it can be considered that the target pedestrian is still moving straight and can enter the first Kalman filter for heading constraint; otherwise, it can be considered that the target pedestrian is no longer moving straight.

[0187] In this implementation, in response to the error distribution of the heading of the target pedestrian in the second sliding window not conforming to the Gaussian distribution, fault repair may be performed on the heading constraint applied in the second sliding window.

[0188] Figure 5 A schematic diagram showing pedestrians in a long radius turning section scenario. Considering that some buildings have long radius turning sections, such as Figure 5 As shown, in this case, since the turning angle is very small, the heading constraint algorithm may cause an erroneous heading constraint. Therefore, in this implementation, the heading constraint check can be performed based on the error distribution of the heading within the second sliding window.

[0189] Figure 6 A schematic diagram of the heading constraint verification in the pedestrian navigation heading constraint method provided by the embodiment of the present disclosure is shown. Figure 6 As shown, a suitable length Q of the second sliding window can be set. In this implementation, the heading of the target pedestrian in the first sliding window satisfies the first preset straight-moving condition, and the heading Y at the current moment now The mean heading Y of the target pedestrian in the first sliding window res The absolute value of the difference between |Y now -Y res | is less than the heading threshold T, the heading error distribution in the second sliding window can be counted and the Shapiro-Wilk test can be performed. The Shapiro-Wilk test is a method for testing whether random sample data conforms to the Gaussian distribution. In this implementation, x1 can be set <x2<x3<…<x n It is a set of sequentially arranged samples of number n. The null hypothesis H0 indicates that there is no significant difference between the sample data and the Gaussian distribution (that is, the sample data conforms to the Gaussian distribution), and the alternative hypothesis H1 indicates that there is a significant difference between the sample data and the Gaussian distribution (that is, the sample data does not conform to the Gaussian distribution).

[0190] The statistic W used in the Shapiro-Wilk test can be defined as

[0191]

[0192] where x (i)represents the i-th order statistic, that is, the i-th minimum number in the sample; Represents the mean of the sample.

[0193] Constants (a1,…,a n ) can be obtained by the following formula:

[0194]

[0195] where m=(m1,…,m n ) T ,m1,…,m n is the expected value of ordered independent and identically distributed statistics sampled from a standard Gaussian distributed random variable, and V is the covariance of these ordered statistics.

[0196] If the distribution of the sample data is more similar to the Gaussian distribution, the statistic W will tend to a constant of 1, otherwise it will tend to a constant of 0. Then, the p-value obtained by the test can be compared with the significance level α. If the p-value is less than the significance level α, H0 can be rejected, and it is believed that there is a significant difference between the sample data and the Gaussian distribution, and the heading constraint is invalid; otherwise, H0 is accepted, and it is believed that the sample data conforms to the Gaussian distribution, and the heading constraint is successful (that is, the heading constraint is valid).

[0197] Through heading constraint verification, long-radius turning sections can be accurately identified, effectively preventing incorrect constraints of the heading constraint algorithm. It has the advantages of high robustness and simple design.

[0198] As an example of this implementation, the fault repairing of the heading constraint applied in the second sliding window includes: performing fault repairing of the heading constraint applied in the second sliding window through a second Kalman filter.

[0199] Figure 7A schematic diagram of the second sliding window for heading constraint verification in the pedestrian navigation heading constraint method provided by the embodiment of the present disclosure is shown. Since the heading constraint verification comprehensively considers a longer period of time, the second sliding window set can be larger and have hysteresis during detection. In the heading constraint algorithm, if the sample data in the second sliding window passes the Shapiro-Wilk test, that is, satisfies the Gaussian distribution, the time of the last data in the second sliding window and the constrained heading at this time can be recorded. If the sample data of the second sliding window is considered not to satisfy the Gaussian distribution when the window is slid next time, the heading with the wrong constraint between the t-1 time can be corrected according to the time (t-1 time) of the last data in the previous window that has been recorded and the constrained heading. In this example, the system state can be rolled back to the t-1 time by the second Kalman filter, and then the strapdown inertial navigation can be used to update until the system state meets the conditions of the heading constraint algorithm and the heading constraint verification algorithm again.

[0200] As an example of this implementation method, the heading constraint processing is performed on the target pedestrian at the current moment based on the difference information between the heading at the current moment and the heading of the target pedestrian in the first sliding window, and / or the error distribution of the heading of the target pedestrian in the second sliding window, and also includes: in response to the difference information between the heading at the current moment and the heading of the target pedestrian in the first sliding window not satisfying the second preset straight travel condition, determining that the heading constraint is invalid at the current moment.

[0201] In this example, if the difference information between the heading at the current moment and the heading of the target pedestrian in the first sliding window does not meet the second preset straight-moving condition, it can be determined that the target pedestrian is not in a straight-moving state, and it can be determined that the heading constraint is invalid at the current moment.

[0202] In the disclosed embodiment, if the target pedestrian is not in a straight-moving state, for example, the pedestrian is turning or moving in a non-straight path, it can be determined that the heading constraint is invalid, that is, no heading constraint is performed, thereby being able to flexibly respond to changes in the motion state of the target pedestrian and maintain a high level of navigation accuracy.

[0203] The pedestrian navigation heading constraint method provided by the embodiment of the present disclosure is explained below through two specific application scenarios.

[0204] Application scenario 1:

[0205] In this application scenario, the inertial measurement unit installed on the foot of the target pedestrian can obtain the inertial data of the target pedestrian. Based on the inertial data, the moment when the foot of the target pedestrian completely touches the ground can be extracted. And at the moment when the foot completely touches the ground, the heading of the target pedestrian at the current moment can be obtained based on the inertial data.

[0206] Next, three levels of judgment can be made:

[0207] First-level judgment: judge whether the standard deviation of the target pedestrian's heading in the first sliding window is less than the heading standard deviation threshold. If so, the second-level judgment can be entered; otherwise, it can be determined that the target pedestrian is not in a straight-ahead state, that is, the heading constraint is invalid at the current moment.

[0208] Second level judgment: judge whether the absolute value of the difference between the current heading and the target pedestrian's heading average in the first sliding window is less than the heading threshold. If so, it can be preliminarily judged that the target pedestrian is in a straight state, and a heading constraint is imposed on the target pedestrian at the current moment, and the third level judgment can be entered; otherwise, it can be judged that the target pedestrian is not in a straight state, that is, the heading constraint is invalid at the current moment.

[0209] The third level judgment: judge whether the error distribution of the target pedestrian's heading in the second sliding window conforms to the Gaussian distribution, where the length of the second sliding window is greater than the length of the first sliding window. If so, no fault repair is required; otherwise, the heading constraint applied in the second sliding window can be repaired.

[0210] Application scenario 2:

[0211] In this application scenario, the inertial measurement unit installed on the foot of the target pedestrian can obtain the inertial data of the target pedestrian. Based on the inertial data, the moment when the foot of the target pedestrian completely touches the ground can be extracted. And at the moment when the foot completely touches the ground, the heading of the target pedestrian at the current moment can be obtained based on the inertial data.

[0212] Next, you can make a secondary judgment:

[0213] First-level judgment: judge whether the standard deviation of the target pedestrian's heading in the first sliding window is less than the heading standard deviation threshold. If so, the second-level judgment can be entered; otherwise, it can be determined that the target pedestrian is not in a straight-ahead state, that is, the heading constraint is invalid at the current moment.

[0214] Second level judgment: judge whether the absolute value of the difference between the current heading and the mean heading of the target pedestrian in the first sliding window is less than the heading threshold, and whether the error distribution of the heading of the target pedestrian in the second sliding window conforms to the Gaussian distribution, wherein the length of the second sliding window is greater than the length of the first sliding window. If the absolute value of the difference between the current heading and the mean heading of the target pedestrian in the first sliding window is less than the heading threshold, and the error distribution of the heading of the target pedestrian in the second sliding window conforms to the Gaussian distribution, it can be determined that the target pedestrian is in a straight state, and a heading constraint is imposed on the target pedestrian at the current moment. If the absolute value of the difference between the current heading and the mean heading of the target pedestrian in the first sliding window is less than the heading threshold, and the error distribution of the heading of the target pedestrian in the second sliding window does not conform to the Gaussian distribution, it can be determined that the target pedestrian is not in a straight state, and the heading constraint imposed in the second sliding window can be repaired.

[0215] Since the errors of the inertial navigation system will accumulate over time, the attitude, speed and position errors calculated by the entire system will gradually diverge. The disclosed embodiment can constrain the heading without the need for prior information, and verify the heading constraint at the same time, which can effectively suppress the heading error divergence of the navigation system, significantly improve the positioning accuracy of the inertial pedestrian navigation system, and has the advantages of good stability, low cost and easy implementation.

[0216] It can be understood that the above-mentioned various method embodiments mentioned in the present disclosure can be combined with each other to form a combined embodiment without violating the principle logic. Due to space limitations, the present disclosure will not repeat them. It can be understood by those skilled in the art that in the above-mentioned method of the specific implementation method, the specific execution order of each step should be determined according to its function and possible internal logic.

[0217] In addition, the present disclosure also provides a pedestrian navigation heading constraint device, an electronic device, a computer-readable storage medium, and a computer program product, all of which can be used to implement any pedestrian navigation heading constraint method provided by the present disclosure. The corresponding technical solutions and technical effects can be found in the corresponding records in the method part and will not be repeated here.

[0218] Figure 8 FIG. 1 is a block diagram of a pedestrian navigation heading constraint device provided by an embodiment of the present disclosure. Figure 8 As shown, the pedestrian navigation heading constraint device includes:

[0219] An acquisition module 81 is used to obtain inertial data of a target pedestrian;

[0220] An extraction module 82, configured to extract the heading of the target pedestrian at a current moment based on the inertial data;

[0221] A heading constraint processing module 83 is used to respond to the heading of the target pedestrian in the first sliding window satisfying a first preset straight-moving condition, and based on the difference information between the heading at the current moment and the heading of the target pedestrian in the first sliding window, and / or the error distribution of the heading of the target pedestrian in the second sliding window, perform heading constraint processing on the target pedestrian at the current moment, wherein the right boundaries of the first sliding window and the second sliding window are both the current moment, and the length of the second sliding window is greater than the length of the first sliding window.

[0222] In a possible implementation, the obtaining module 81 is used to:

[0223] The inertial data of the target pedestrian is obtained by installing an inertial measurement unit on the foot of the target pedestrian.

[0224] In a possible implementation, the extraction module 82 is used to:

[0225] Based on the inertial data, extracting the moment when the foot of the target pedestrian completely touches the ground;

[0226] At the moment when the foot is completely on the ground, the heading of the target pedestrian is extracted based on the inertial data to obtain the heading of the target pedestrian at the current moment.

[0227] In a possible implementation, the extraction module 82 is used to:

[0228] For any moment, extract the amplitude of the inertial data at the moment, and calculate the standard deviation of the inertial data in a third sliding window, wherein the right boundary of the third sliding window is the moment;

[0229] In response to the amplitude being smaller than an amplitude threshold and the standard deviation being smaller than a standard deviation threshold, it is determined that the moment is the moment when the foot of the target pedestrian completely touches the ground.

[0230] In one possible implementation,

[0231] The amplitude of the inertial data includes: specific force amplitude and angular velocity amplitude;

[0232] The standard deviation of the inertial data includes: a standard deviation of specific force and a standard deviation of angular velocity;

[0233] The amplitude thresholds include: a specific force amplitude threshold and an angular velocity amplitude threshold;

[0234] The standard deviation thresholds include: a specific force standard deviation threshold and an angular velocity standard deviation threshold;

[0235] The extraction module 82 is used to: in response to the specific force amplitude being less than the specific force amplitude threshold, the angular velocity amplitude being less than the angular velocity amplitude threshold, the specific force standard deviation being less than the specific force standard deviation threshold, and the angular velocity standard deviation being less than the angular velocity standard deviation threshold, determine that the moment is the moment when the target pedestrian's foot is completely on the ground.

[0236] In a possible implementation, the extraction module 82 is used to:

[0237] Based on the inertial data, the b system is updated using the equivalent rotation vector method to obtain the quaternion of the b system attitude transformation;

[0238] Based on the inertial data, the n-system is updated using an equivalent rotation vector method to obtain a quaternion of the n-system attitude transformation;

[0239] Calculating the quaternion of the current posture of the target pedestrian according to the quaternion of the b-system posture transformation and the quaternion of the n-system posture transformation;

[0240] Based on the quaternion of the current posture, the heading of the target pedestrian at the current moment is obtained.

[0241] In a possible implementation manner, the first preset straight-moving condition includes:

[0242] The standard deviation of the heading is less than the heading standard deviation threshold.

[0243] In a possible implementation manner, the device further includes:

[0244] The failure determination module is configured to determine that the heading constraint is invalid at the current moment in response to the heading of the target pedestrian within the first sliding window not satisfying the first preset straight-moving condition.

[0245] In a possible implementation, the heading constraint processing module 83 is used to:

[0246] In response to the difference information between the heading at the current moment and the heading of the target pedestrian within the first sliding window satisfying a second preset straight-moving condition, applying a heading constraint to the target pedestrian at the current moment;

[0247] In response to the error distribution of the heading of the target pedestrian in the second sliding window not conforming to the Gaussian distribution, fault repair is performed on the heading constraint applied in the second sliding window.

[0248] In a possible implementation, the difference information between the current heading and the heading of the target pedestrian in the first sliding window satisfies the second preset straight-moving condition, including:

[0249] The absolute value of the difference between the heading at the current moment and the average heading of the target pedestrian in the first sliding window is less than the heading threshold.

[0250] In a possible implementation, the heading constraint processing module 83 is used to:

[0251] At the current moment, a heading constraint is imposed on the target pedestrian through a first Kalman filter.

[0252] In a possible implementation, the heading constraint processing module 83 is used to:

[0253] The second Kalman filter is used to perform fault repair on the heading constraint applied within the second sliding window.

[0254] In a possible implementation, the heading constraint processing module 83 is used to:

[0255] In response to the difference information between the heading at the current moment and the heading of the target pedestrian within the first sliding window not satisfying a second preset straight-moving condition, it is determined that the heading constraint at the current moment is invalid.

[0256] In some embodiments, the functions or modules included in the device provided by the embodiments of the present disclosure can be used to execute the method described in the above method embodiments. Its specific implementation and technical effects can refer to the description of the above method embodiments. For the sake of brevity, they will not be repeated here.

[0257] The present disclosure also provides a computer-readable storage medium having computer program instructions stored thereon, wherein the computer program instructions implement the above method when executed by a processor. The computer-readable storage medium may be a non-volatile computer-readable storage medium, or may be a volatile computer-readable storage medium.

[0258] The embodiment of the present disclosure further provides a computer program, including a computer-readable code. When the computer-readable code is executed in an electronic device, a processor in the electronic device executes the above method.

[0259] The embodiments of the present disclosure also provide a computer program product, including a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in an electronic device, a processor in the electronic device executes the above method.

[0260] An embodiment of the present disclosure also provides an electronic device, comprising: one or more processors; and a memory for storing executable instructions; wherein the one or more processors are configured to call the executable instructions stored in the memory to execute the above method.

[0261] The electronic device may be provided as a terminal, a server, or a device in other forms.

[0262] Fig. 9 1 is a block diagram of an electronic device 1900 provided in an embodiment of the present disclosure. For example, the electronic device 1900 may be provided as a server or a terminal. Fig. 9 , the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions executable by the processing component 1922, such as an application. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above method.

[0263] The electronic device 1900 may also include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output interface 1958 (I / O interface). The electronic device 1900 may operate based on an operating system stored in the memory 1932, such as a Microsoft Server operating system (Windows Server 2003). TM ), a graphical user interface operating system launched by Apple (MacOS X TM ), a multi-user, multi-process computer operating system (Unix TM ), a free and open source Unix-like operating system (Linux TM ), an open source Unix-like operating system (FreeBSD TM ) or similar.

[0264] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the electronic device 1900 to perform the above method.

[0265] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0266] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0267] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0268] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0269] Various aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0270] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0271] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0272] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of special hardware and computer instructions.

[0273] The computer program product may be implemented in hardware, software or a combination thereof. In one optional embodiment, the computer program product is embodied as a computer storage medium, and in another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (SDK) and the like.

[0274] The above description of various embodiments tends to emphasize the differences between the various embodiments. The same or similar aspects can be referenced to each other, and for the sake of brevity, they will not be repeated herein.

[0275] If the technical solution of the embodiments of the present disclosure involves personal information, the product using the technical solution of the embodiments of the present disclosure has clearly informed the personal information processing rules and obtained the individual's voluntary consent before processing the personal information. If the technical solution of the embodiments of the present disclosure involves sensitive personal information, the product using the technical solution of the embodiments of the present disclosure has obtained the individual's separate consent before processing the sensitive personal information, and at the same time meets the requirement of "explicit consent". For example, at personal information collection devices such as cameras, clear and prominent signs are set to inform that the personal information collection scope has been entered and personal information will be collected. If the individual voluntarily enters the collection scope, it is deemed that he or she agrees to collect his or her personal information; or on the device for processing personal information, when the personal information processing rules are notified by obvious signs / information, the individual's authorization is obtained through pop-up information or by asking the individual to upload his or her personal information; the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the type of personal information processed.

[0276] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for constraining a pedestrian navigation heading, characterized in that: include: Obtain inertial data of the target pedestrian; Based on the inertial data, extracting the heading of the target pedestrian at the current moment; In response to the target pedestrian's heading within the first sliding window satisfying a first preset straight-moving condition, based on difference information between the heading at the current moment and the heading of the target pedestrian within the first sliding window, and / or error distribution of the heading of the target pedestrian within the second sliding window, heading constraint processing is performed on the target pedestrian at the current moment, wherein the right boundaries of the first sliding window and the second sliding window are both the current moment, and the length of the second sliding window is greater than the length of the first sliding window.

2. The method according to claim 1, characterized in that The step of obtaining the inertial data of the target pedestrian includes: The inertial data of the target pedestrian is obtained by installing an inertial measurement unit on the foot of the target pedestrian.

3. The method according to claim 1, characterized in that The step of extracting the heading of the target pedestrian at the current moment based on the inertial data includes: Based on the inertial data, extracting the moment when the foot of the target pedestrian completely touches the ground; At the moment when the foot is completely on the ground, the heading of the target pedestrian is extracted based on the inertial data to obtain the heading of the target pedestrian at the current moment.

4. The method according to claim 3, characterized in that The step of extracting the moment when the foot of the target pedestrian completely touches the ground based on the inertial data comprises: For any moment, extract the amplitude of the inertial data at the moment, and calculate the standard deviation of the inertial data in a third sliding window, wherein the right boundary of the third sliding window is the moment; In response to the amplitude being smaller than an amplitude threshold and the standard deviation being smaller than a standard deviation threshold, it is determined that the moment is the moment when the foot of the target pedestrian completely touches the ground.

5. The method according to claim 4, characterized in that The amplitude of the inertial data includes: specific force amplitude and angular velocity amplitude; The standard deviation of the inertial data includes: a standard deviation of specific force and a standard deviation of angular velocity; The amplitude thresholds include: a specific force amplitude threshold and an angular velocity amplitude threshold; The standard deviation thresholds include: a specific force standard deviation threshold and an angular velocity standard deviation threshold; The response to the amplitude being less than an amplitude threshold, and the standard deviation being less than a standard deviation threshold, determining the moment as the moment when the foot of the target pedestrian completely touches the ground includes: responding to the specific force amplitude being less than the specific force amplitude threshold, the angular velocity amplitude being less than the angular velocity amplitude threshold, the specific force standard deviation being less than the specific force standard deviation threshold, and the angular velocity standard deviation being less than the angular velocity standard deviation threshold, determining the moment as the moment when the foot of the target pedestrian completely touches the ground.

6. The method according to claim 1, characterized in that The step of extracting the heading of the target pedestrian at the current moment based on the inertial data includes: Based on the inertial data, the b system is updated using the equivalent rotation vector method to obtain the quaternion of the b system attitude transformation; Based on the inertial data, the n-system is updated using an equivalent rotation vector method to obtain a quaternion of the n-system attitude transformation; Calculating the quaternion of the current posture of the target pedestrian according to the quaternion of the b-system posture transformation and the quaternion of the n-system posture transformation; Based on the quaternion of the current posture, the heading of the target pedestrian at the current moment is obtained.

7. The method according to claim 1, characterized in that The first preset straight-ahead condition includes: The standard deviation of the heading is less than the heading standard deviation threshold.

8. The method according to claim 1 or 7, characterized in that: The method further comprises: In response to the heading of the target pedestrian within the first sliding window not satisfying the first preset straight-moving condition, it is determined that the heading constraint is invalid at the current moment.

9. The method according to claim 1, characterized in that: The method of performing heading constraint processing on the target pedestrian at the current moment based on the difference information between the heading at the current moment and the heading of the target pedestrian in the first sliding window, and / or the error distribution of the heading of the target pedestrian in the second sliding window, comprises: In response to the difference information between the heading at the current moment and the heading of the target pedestrian within the first sliding window satisfying a second preset straight-moving condition, applying a heading constraint to the target pedestrian at the current moment; In response to the error distribution of the heading of the target pedestrian in the second sliding window not conforming to the Gaussian distribution, fault repair is performed on the heading constraint applied in the second sliding window.

10. The method according to claim 9, characterized in that The difference information between the current heading and the heading of the target pedestrian within the first sliding window satisfies a second preset straight-moving condition, including: The absolute value of the difference between the heading at the current moment and the average heading of the target pedestrian in the first sliding window is less than the heading threshold.

11. The method according to claim 9, characterized in that Imposing a heading constraint on the target pedestrian at the current moment includes: At the current moment, a heading constraint is imposed on the target pedestrian through a first Kalman filter.

12. The method according to claim 9, characterized in that The performing fault repair on the heading constraint applied in the second sliding window includes: The second Kalman filter is used to perform fault repair on the heading constraint applied within the second sliding window.

13. The method according to claim 9 or 10, characterized in that: The performing heading constraint processing on the target pedestrian at the current moment based on the difference information between the heading at the current moment and the heading of the target pedestrian in the first sliding window, and / or the error distribution of the heading of the target pedestrian in the second sliding window, further includes: In response to the difference information between the heading at the current moment and the heading of the target pedestrian within the first sliding window not satisfying a second preset straight-moving condition, it is determined that the heading constraint at the current moment is invalid.

14. A pedestrian navigation heading constraint device, characterized in that: include: An acquisition module is used to obtain the inertial data of the target pedestrian; An extraction module, used for extracting the heading of the target pedestrian at the current moment based on the inertial data; A heading constraint processing module is used to respond to the heading of the target pedestrian in the first sliding window satisfying a first preset straight-moving condition, based on the difference information between the heading at the current moment and the heading of the target pedestrian in the first sliding window, and / or the error distribution of the heading of the target pedestrian in the second sliding window, perform heading constraint processing on the target pedestrian at the current moment, wherein the right boundaries of the first sliding window and the second sliding window are both the current moment, and the length of the second sliding window is greater than the length of the first sliding window.

15. An electronic device, characterized in that: include: one or more processors; a memory for storing executable instructions; The one or more processors are configured to call the executable instructions stored in the memory to execute the method described in any one of claims 1 to 13.

16. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 13 is implemented.

17. A computer program product comprising computer readable code, or a non-volatile computer readable storage medium carrying computer readable code, characterized in that: When the computer readable code is executed in an electronic device, a processor in the electronic device executes the method according to any one of claims 1 to 13.

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