Inertial navigation autonomous positioning method and device for human trunk based on inverted pendulum model

Abstract

The present invention discloses an inertial navigation autonomous positioning method and device at the human body trunk based on an inverted pendulum model, belonging to the field of pedestrian positioning technology, the method includes installing an IMU at the human body trunk and measuring the arm information; after the human body walks a preset distance in a straight line, the installation angle of the IMU is compensated and the output of the IMU is collected; using the output of the accelerometer to determine whether the pedestrian is in a stationary state, if it is a stationary state, the stationary state model is used for constraint; if it is not a stationary state, the inverted pendulum model is used for constraint; using the Kalman filter for information fusion, the final pedestrian position information is obtained. The present invention can obtain accurate and real-time position information of the target person at a relatively low cost, and provide data support for personnel management.

Description

Technical Field

[0001] The present invention belongs to the technical field of pedestrian positioning, and in particular relates to an inertial navigation autonomous positioning method and device for a human body trunk based on an inverted pendulum model. Background Art

[0002] In the field of pedestrian autonomous positioning, the inertial PDR (Pedestrian Dead Reckoning) positioning system with the Miniature Inertial Measurement Unit (MIMU) as the main device has the characteristics of full autonomy, high short-term positioning accuracy, and no influence from the external environment. However, it is affected by the noise of the low-cost MIMU sensor and cannot meet the long-term positioning needs of pedestrians. It is necessary to introduce external constraints to correct the accumulation of inertial errors. PDR methods are mainly divided into two categories: stepping model and inertial integral model. The stepping model includes three parts: footstep detection, step length estimation, and heading estimation; the inertial integral model uses the inertial mechanical orchestration algorithm as the basic framework, including three parts: attitude update, velocity update, and position update.

[0003] At present, the mainstream method of autonomously positioning pedestrians using inertial navigation installed on the human body (such as the chest, back, and waist) is to use a stepping model, that is, to use different methods to improve the accuracy of footstep detection and improve the accuracy of step length estimation and heading estimation results, so as to improve the accuracy of the final position. However, this method cannot overcome the shortcomings of large randomness and weak robustness of step length estimation results and heading estimation results in the stepping model. In addition, the step length estimation usually uses an empirical model combined with some parameters to calculate the step length result. This empirical model needs to match different model parameters for different users, has weak generalization, and is difficult to promote and apply.

[0004] Another mainstream method is to combine the stepping model with the inertial integral model to enhance the robustness of the algorithm. At the same time, the non-integrity constraint algorithm in the vehicle-mounted inertial navigation solution method is referred to, that is, only the pedestrian's forward speed exists, and the lateral and vertical speeds are 0. That is, the footstep detection algorithm and step length estimation algorithm in the stepping model are used to construct a pseudo-observation of the pedestrian's forward speed, which is integrated with the result of the inertial mechanical arrangement algorithm as constraint information, and only the forward speed is calculated, and the lateral speed and vertical speed are forced to be 0. However, this assumption does not conform to the actual situation of pedestrians walking. As shown in Figure 1, during the pedestrian's walking, the original output of the y-axis of the accelerometer has obvious periodic changes and cannot be ignored. This change reflects the change of the lateral speed during the pedestrian's movement, which is not 0.

[0005] In summary, the existing methods are essentially the same, that is, they use the footstep detection and step length estimation methods in the stepping model to calculate the forward speed while ignoring the lateral speed and vertical speed. This will attribute the results of the lateral and vertical speed calculations to the system noise during data fusion, which is not in line with the actual situation. Summary of the invention

[0006] To solve the above technical problems, the present invention provides an inertial navigation autonomous positioning method and device for the human torso based on an inverted pendulum model. On the premise of fully analyzing the original output of the sensor during the pedestrian's walking process, the motion state of the pedestrian during the walking process is modeled as an inverted pendulum model, and the inverted pendulum model is used to calculate the pedestrian's three-dimensional velocity information. This information is integrated with the results of the Kalman filter algorithm and the inertial solution, thereby achieving high-precision and robust pedestrian autonomous positioning.

[0007] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0008] A method for autonomous inertial navigation positioning at a human body trunk based on an inverted pendulum model, the method comprising:

[0009] Step 1: Install the IMU on the human body and measure the arm information;

[0010] Step 2: After the human body walks a preset distance in a straight line, the installation angle of the IMU is compensated, including compensation of the horizontal installation angle under static conditions and compensation of the heading installation angle using straight line correction, and the output of the IMU is collected;

[0011] Step 3: Use the accelerometer output to determine whether the pedestrian is in a stationary state. If so, use the stationary state model to constrain. If not, determine whether it is at a characteristic moment. If so, use the inverted pendulum model to constrain the forward speed. If not, use the inverted pendulum model to constrain the vertical speed and lateral speed.

[0012] Step 4: Use the Kalman filter to perform information fusion, use the speed information constructed by the inverted pendulum model to constrain the speed error accumulation in the inertial solution process, and then integrate the speed again to obtain the final pedestrian position information.

[0013] On the other hand, the present invention also provides an inertial navigation autonomous positioning device for a human body trunk based on an inverted pendulum model, comprising:

[0014] A lever arm information acquisition unit is used to install an IMU on the human body trunk and measure the lever arm information;

[0015] The installation angle compensation unit is used to compensate the installation angle of the IMU after the human body walks a preset distance in a straight line, including compensation of the horizontal installation angle under static conditions and compensation of the heading installation angle using straight line correction, and collects the output of the IMU;

[0016] The speed constraint unit is used to use the accelerometer output to determine whether the pedestrian is in a stationary state. If the pedestrian is in a stationary state, the stationary state model is used for constraint. If the pedestrian is not in a stationary state, the speed constraint unit determines whether the pedestrian is in a characteristic moment. If the pedestrian is in a characteristic moment, the inverted pendulum model is used for forward speed constraint. If the pedestrian is in a non-characteristic moment, the inverted pendulum model is used for vertical speed constraint and lateral speed constraint.

[0017] The positioning information acquisition unit is used to use the Kalman filter to perform information fusion, use the speed information constructed by the inverted pendulum model to constrain the speed error accumulation in the inertial solution process, and then integrate the speed again to obtain the final pedestrian position information.

[0018] In a third aspect, the present invention provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned inertial navigation autonomous positioning method at the human torso based on the inverted pendulum model.

[0019] In a fourth aspect, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the aforementioned inertial navigation autonomous positioning method for the human torso based on an inverted pendulum model.

[0020] The beneficial effects of the present invention are:

[0021] Compared with the existing inertial PDR constraint algorithms based on step-by-step models or non-holonomic constraint models, which face low positioning accuracy, poor adaptability, poor robustness, and incompatibility with the motion patterns in the real world, and cannot effectively constrain the positioning error of pedestrians in the case of long-term indoor motion, the present invention starts from the motion characteristics of pedestrians themselves, models the motion of pedestrians as an inverted pendulum model, designs an autonomous solution method for pedestrian inertial PDR based on the inverted pendulum model, and completes the compensation of the installation angle error. This method has strong adaptability to different pedestrian carriers, can effectively constrain the speed information of pedestrians during their movement, and has strong versatility and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a graph of the original output curve of the y-axis of the accelerometer in the prior art;

[0023] Figure 2 The flowchart of the inertial navigation autonomous positioning method at the human body trunk based on the inverted pendulum model of the present invention;

[0024] Figure 3 It is a schematic diagram of heading angle deviation;

[0025] Figure 4 This is the velocity decomposition diagram of the inverted pendulum model;

[0026] Figure 5 is a schematic diagram of forward speed;

[0027] Figure 6 It is a schematic diagram of the lateral velocity and vertical velocity model;

[0028] Figure 7 This is the actual solution trajectory result of the method. DETAILED DESCRIPTION

[0029] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0030] like Figure 2 As shown, the present invention provides an inertial navigation autonomous positioning method at a human body trunk based on an inverted pendulum model, which specifically includes:

[0031] Step 1: Install the IMU on the human body and measure the arm information, that is, the height information of the IMU installation position from the ground;

[0032] Step 2: Walk a certain distance (e.g. 5m) along a straight line to compensate the installation angle, including compensation of the horizontal installation angle under static conditions and compensation of the heading installation angle using straight line correction; then enter the formal working stage and collect the output of the IMU;

[0033] Step 3: Use the accelerometer output to determine whether the pedestrian is in a stationary state. If so, use the stationary state model to constrain. If not, determine whether it is at a characteristic moment. If so, use the inverted pendulum model to constrain the forward speed. If not, use the inverted pendulum model to constrain the vertical speed and lateral speed.

[0034] Step 4: Use the Kalman filter to perform information fusion, use the speed information constructed by the inverted pendulum model to constrain the speed error accumulation in the inertial solution process, and then integrate the speed again to obtain the final pedestrian position information.

[0035] Furthermore, in step 2, since the constraints based on the inverted pendulum model are constructed under the h system, due to the existence of installation errors, the b system and the h system are not aligned in the initial situation. Therefore, it is necessary to calculate the rotation matrix between the two systems to achieve alignment between the b system and the h system, that is, to complete the compensation of the installation angle error between the h system and the b system. Specifically, the b system is the carrier coordinate system: the origin is at the center point of the IMU, and the x, y, and z axes form an orthogonal right-handed coordinate system according to the situation when the IMU is produced; the h system is the pedestrian system: the origin coincides with the b system, the x axis is directly in front of the human body, the y axis points to the right of the human body, and the z axis points to the ground. The three axes form an orthogonal right-handed system of front-right-down.

[0036] A. Horizontal installation angle compensation;

[0037] In the initial static stage, when there is no additional motion acceleration, the specific force output measured by the triaxial accelerometer is the projection of the local gravity in the b system. Therefore, the roll angle in the horizontal installation angle can be calculated by gravity constraint. and pitch angle , the calculation formula is as follows:

[0038] (1)

[0039] in, is the inverse tangent function, is the original output of the accelerometer in the mth direction at time k in frame b, It is the result of mean filtering in the mth direction in the b system within a period of time, where m is the x, y or z direction and N is the total number of moments. The purpose of using mean filtering is to reduce the impact of measurement noise.

[0040] B. Heading installation angle compensation;

[0041] like Figure 3 As shown, due to the heading installation angle The existence of, in the case of straight line walking, from the starting point Departure, should have reached the end , but eventually reached the pseudo-end point Therefore, the relative correction method is used to calculate the heading error. In the initial stage, the pedestrian walks in a straight line, and then the heading angle is set Defined as:

[0042] (2)

[0043] If the local coordinate system is used, and the pedestrian walks along a straight line during the correction phase, then , , then the above formula can be simplified to:

[0044] (3)

[0045] C. Installation angle compensation;

[0046] To get the roll installation angle , Pitch installation angle and heading installation angle Then the installation angle error compensation matrix from b system to h system can be calculated , the calculation formula is:

[0047] (4)

[0048] Furthermore, in step 3, in the static state model, that is, when the pedestrian is static, the three-dimensional velocities are all 0, and the judgment is based on whether the modulus of the acceleration is greater than a set threshold, and the threshold is set to the modulus of the local gravity acceleration; Figure 4 As shown in the figure, when a pedestrian walks, the motion trajectory of the IMU installed on the torso is an arc, which can be modeled as an inverted pendulum model. Its velocity direction is along the tangent direction of the plane where the arc trajectory is located, which can be decomposed into the velocity along the x-axis and y-axis directions of the pedestrian system, while the velocity in the z-axis direction is 0, and the velocities in the x-axis and y-axis directions can be calculated using the angular velocity and the lever arm.

[0049] A. Forward speed correction;

[0050] Depend on Figure 5 As shown in the figure, when looking at the pedestrian from the side, the pedestrian's body structure can be abstracted into two parts: the legs and the torso. The x-axis of the h-axis points to the front, and the z-axis points to the ground. There are two main active joints between the pedestrian's feet and the IMU: the ankle joint and the hip joint. The legs use the feet as the fulcrum and the ankle joint as the center of the circle to rotate; while the torso moves with the hip joint as the center of the circle, and the torso generally remains upright. In a gait cycle of pedestrian movement, there is always a situation where the supporting leg is upright and forms a straight line with the body, that is, Figure 5 The intermediate moment shown is the special moment when the forward velocity observation can be constructed. At this time, the instantaneous displacement of the IMU is:

[0051] (5)

[0052] in, is the displacement of IMU, is the arc of rotation of the IMU relative to the ankle joint, is the length of the arm, that is, the height from the IMU to the ground. Then, take the time derivative of both sides of the equation and project it to the h system to obtain:

[0053] (6)

[0054] in, is the velocity in the x direction in the h frame, is the installation angle error compensation matrix from b system to h system; is the length of the arm, i.e. the height from the IMU to the ground; It is the y-axis component of the rotational angular velocity vector of the b system relative to the n system projected on the b system. Considering the axial direction of the y axis, it takes a negative value here. Among them, the n system is a local horizontal coordinate system, the x axis points to the virtual north direction, the y axis points to the virtual east direction, and the z axis points to the ground and forms a right-handed coordinate system with the x and y axes.

[0055] B. Lateral speed correction and vertical speed correction;

[0056] When a pedestrian walks, the left and right swing of the pedestrian's body can be seen from behind. Figure 6 In the case shown, the motion trajectory of the IMU is a circle with the foot as the center and the arm as the center. The y-axis of the h system points to the tangent line of the arc, and the z-axis points from the IMU to the center of the circle (foot). Similar to the analysis of the forward velocity, the lateral velocity can be expressed as:

[0057] (7)

[0058] in is the velocity in the y direction in the h frame; is the installation angle error compensation moment from b system to h system, It is the x-axis component of the projection of the angular velocity vector of system b relative to system n onto system b.

[0059] Considering that MEMS-level IMU cannot sense the angular velocity of the earth's rotation, , is the projection of the rotation angular velocity vector of system b relative to system n in system b, is the projection of the rotation angular velocity vector of the inertial system (i system) relative to the b system in the b system. In summary, the velocity observation based on the inverted pendulum model can be written as:

[0060] (8)

[0061] is the three-dimensional velocity observation vector in the h system, is the installation angle error compensation matrix from b system to h system, is the y-axis component of the projection of the rotational angular velocity vector of system b relative to system i on system b, is the x-axis component of the projection of the rotational angular velocity vector of system b relative to system i on system b, is the length of the lever arm, is the true value of the three-dimensional velocity of the passerby in the h frame, is the observed error of velocity.

[0062] The constraints on lateral and vertical velocities apply at all times, and the constraints on forward velocity apply only at Figure 5 This is only true for the specific moment shown when the supporting leg and body trunk are in a straight line.

[0063] Furthermore, in step 4, Kalman filtering is used for data fusion processing, and a total of 15-dimensional vectors including three-dimensional position error, three-dimensional velocity error, attitude error, accelerometer bias error and gyroscope bias error are set as error states, namely:

[0064] (9)

[0065] in, is the three-dimensional position error vector, The three-dimensional velocity error vector, The attitude error vector, and are the accelerometer bias error vector and the gyroscope bias error vector respectively.

[0066] The speed of the inertial recursion with error state is:

[0067] (10)

[0068] in, is the identity matrix, is the antisymmetric matrix form of the vector, is the estimated value of the three-dimensional velocity vector under h with error, is the rotation matrix from system n to system b with error, is the rotation matrix from the n system to the b system without error, is the true value of the three-dimensional velocity of the passerby in the h frame, is the estimated value of the three-dimensional velocity in the n system, obtained by inertial solution.

[0069] The antisymmetric matrix is ​​defined as a three-dimensional vector ,but:

[0070] (11)

[0071] Then the innovation vector of the error state is:

[0072] (12)

[0073] The corresponding observation matrix in the Kalman filter is:

[0074] (13)

[0075] In the formula, is a 3-row, 3-column zero matrix, is the rotation matrix from system n to system b at time k, is the three-dimensional velocity vector of the n system at time k.

[0076] Using the above formula, we can complete the speed constraint for pedestrian inertial autonomous navigation positioning, reduce the error generated when calculating position information by constraining the speed, and finally realize pedestrian autonomous positioning. The positioning results of the method are as follows: Figure 7 As shown in the figure, the solid line is the reference trajectory, and the dotted line is the trajectory obtained by the method.

[0077] In summary, the present invention utilizes the geometric relationship between the accelerometer output at the stationary moment and the calculated position and the actual position during the initial straight-line walking process to achieve compensation for the IMU installation angle and improve the accuracy of the subsequent processing algorithm. Based on the motion characteristics of pedestrians walking, the motion of pedestrians walking is constructed as an inverted pendulum model, which effectively solves the problem of lack of reliable, robust, and adaptive speed constraints in pedestrian autonomous inertial positioning and navigation, and improves the accuracy and reliability of the inertial-based pedestrian autonomous positioning solution.

[0078] On the other hand, the present invention also provides an inertial navigation autonomous positioning device for a human body trunk based on an inverted pendulum model, which includes units capable of implementing the aforementioned steps, specifically including:

[0079] A lever arm information acquisition unit is used to install an IMU on the human body trunk and measure the lever arm information;

[0080] The installation angle compensation unit is used to compensate the installation angle of the IMU after the human body walks a preset distance in a straight line, including compensation of the horizontal installation angle under static conditions and compensation of the heading installation angle using straight line correction, and collects the output of the IMU;

[0081] The speed constraint unit is used to use the accelerometer output to determine whether the pedestrian is in a stationary state. If the pedestrian is in a stationary state, the stationary state model is used for constraint. If the pedestrian is not in a stationary state, the speed constraint unit determines whether the pedestrian is in a characteristic moment. If the pedestrian is in a characteristic moment, the inverted pendulum model is used for forward speed constraint. If the pedestrian is in a non-characteristic moment, the inverted pendulum model is used for vertical speed constraint and lateral speed constraint.

[0082] The positioning information acquisition unit is used to use the Kalman filter to perform information fusion, use the speed information constructed by the inverted pendulum model to constrain the speed error accumulation in the inertial solution process, and then integrate the speed again to obtain the final pedestrian position information.

[0083] In a third aspect, the present invention provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned inertial navigation autonomous positioning method at the human torso based on the inverted pendulum model.

[0084] In a fourth aspect, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the aforementioned inertial navigation autonomous positioning method for the human torso based on an inverted pendulum model.

[0085] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An inertial navigation autonomous positioning method for human trunk based on an inverted pendulum model, characterized in that: The method comprises: Step 1: Install the IMU on the human body and measure the arm information; Step 2: After the human body walks a preset distance in a straight line, the installation angle of the IMU is compensated, including compensation of the horizontal installation angle under static conditions and compensation of the heading installation angle using straight line correction, and the output of the IMU is collected; Step 3: Use the accelerometer output to determine whether the pedestrian is in a stationary state. If so, use the stationary state model to constrain. If not, determine whether it is at a characteristic moment. If it is at a characteristic moment, use the inverted pendulum model to constrain the forward speed. If it is not at a characteristic moment, use the inverted pendulum model to constrain the vertical speed and the lateral speed. The characteristic moment represents a special moment when the pedestrian's supporting leg and body trunk are in a straight line. Step 4: Use the Kalman filter to perform information fusion, use the speed information constructed by the inverted pendulum model to constrain the speed error accumulation in the inertial solution process, and then integrate the speed again to obtain the final pedestrian position information.

2. The method for autonomous inertial navigation positioning at the human body trunk based on an inverted pendulum model according to claim 1, characterized in that: The compensation of the horizontal installation angle under static conditions in step 2 includes calculating the rolling installation angle in the horizontal installation angle by gravity constraint. and pitch angle : （1） in, is the inverse tangent function, is the original output of the accelerometer in the mth direction at time k in frame b, That is, it is the result of mean filtering in the mth direction under the b system within a period of time, where m is the x, y or z direction, and N represents the total number of moments.

3. The method for autonomous inertial navigation positioning at a human body trunk based on an inverted pendulum model according to claim 1, characterized in that: The compensation of the heading installation angle using the straight line correction in step 2 includes: assuming that in the case of straight line walking, from the starting point Departure, should have reached the end , and finally reached the pseudo-end point , set the heading installation angle Defined as: （2） Among them, the local coordinate system is used. In the correction stage, the pedestrian walks along a straight line, so there is , , then the above formula (2) is simplified to: （3）。 4. The method for autonomous inertial navigation positioning at a human body trunk based on an inverted pendulum model according to claim 1, characterized in that: The step 2 of compensating the installation angle includes: obtaining the rolling installation angle , Pitch installation angle and heading installation angle Then calculate the installation angle error compensation matrix from b system to h system : （4）。 5. The method for autonomous inertial navigation positioning at the human body trunk based on an inverted pendulum model according to claim 1, characterized in that: The stationary state model in step 3 includes that when the pedestrian is stationary, the three-dimensional speed is 0, and the judgment basis is whether the modulus of the acceleration is greater than a set threshold.

6. The method for autonomous inertial navigation positioning of a human body trunk based on an inverted pendulum model according to claim 1, characterized in that: In step 3, using the inverted pendulum model to perform forward velocity constraint includes: The instantaneous displacement of the IMU at the characteristic moment is recorded as: （5） in, is the displacement of IMU, is the arc of rotation of the IMU relative to the ankle joint, is the length of the arm, i.e. the height from the IMU to the ground; Taking the time derivative of both sides of equation (5) and projecting them into the h system yields: （6） in, is the velocity in the x direction in the pedestrian system, i.e., the h system, is the installation angle error compensation matrix from the carrier coordinate system, i.e., the b system, to the h system; It is the y-axis component of the projection of the rotation angular velocity vector of the b system relative to the local horizontal coordinate system, i.e., the n system, on the b system; The vertical velocity constraint and lateral velocity constraint using the inverted pendulum model include: The lateral velocity is expressed as: （7） in, is the velocity in the y direction in the h system, It is the x-axis component of the projection of the rotation angular velocity vector of system b relative to system n on system b; Considering , is the projection of the rotation angular velocity vector of system b relative to system n in system b, is the projection of the rotation angular velocity vector of the inertial system, i.e., system i relative to system b, on system b. The velocity observation based on the inverted pendulum model is written as: （8） is the three-dimensional velocity observation vector in the h system, is the y-axis component of the projection of the rotational angular velocity vector of system b relative to system i on system b, is the x-axis component of the projection of the rotational angular velocity vector of system b relative to system i on system b, is the true value of the three-dimensional velocity of the passerby in the h frame, is the observed error of velocity.

7. An inertial navigation autonomous positioning device for human trunk based on an inverted pendulum model, characterized in that: include: A lever arm information acquisition unit is used to install an IMU on the human body trunk and measure the lever arm information; The installation angle compensation unit is used to compensate the installation angle of the IMU after the human body walks a preset distance in a straight line, including compensation of the horizontal installation angle under static conditions and compensation of the heading installation angle using straight line correction, and collects the output of the IMU; The speed constraint unit is used to use the output of the accelerometer to determine whether the pedestrian is in a stationary state. If the pedestrian is in a stationary state, the stationary state model is used for constraint; if the pedestrian is not in a stationary state, the speed constraint unit determines whether the pedestrian is in a characteristic moment. If the pedestrian is in a characteristic moment, the inverted pendulum model is used for forward speed constraint. If the pedestrian is in a non-characteristic moment, the inverted pendulum model is used for vertical speed constraint and lateral speed constraint. The characteristic moment indicates a special moment when the pedestrian's supporting leg and body trunk are in a straight line. The positioning information acquisition unit is used to use the Kalman filter to perform information fusion, use the speed information constructed by the inverted pendulum model to constrain the speed error accumulation in the inertial solution process, and then integrate the speed again to obtain the final pedestrian position information.

8. An electronic device, characterized in that: include: one or more processors; A memory for storing one or more programs; Wherein, when one or more programs are executed by the one or more processors, the one or more processors implement the inertial navigation autonomous positioning method for the human torso based on the inverted pendulum model as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that: Executable instructions are stored thereon, which, when executed by a processor, enable the processor to implement the inertial navigation autonomous positioning method for the human body trunk based on an inverted pendulum model as described in any one of claims 1-6.

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