36 results about "Pedestrian navigation" patented technology

The invention discloses a pedestrian inertia / vision / satellite integrated navigation method based on a handheld terminal PDR. The pedestrian inertia / vision / satellite integrated navigation method comprises the following steps: calculating to obtain inertial navigation pose information based on strapdown inertial navigation; when the pedestrian is static, constructing an observation constraint of zero-speed motion for correction, and updating inertial navigation position information based on step length information when a new step is carried out; obtaining visual pose information of the pedestrian based on an inertia / visual fusion odometer, obtaining satellite position information of the pedestrian based on satellite positioning, and carrying out coordinate system alignment and step length coefficient correction factor estimation; a pedestrian multi-source factor graph optimization model with a handheld terminal PDR as a core is constructed, inertial navigation pose information and step length information are used as local state constraints, visual pose information and satellite position information are used as global state constraints, and a target optimization function is constructed to obtain a final pose of a pedestrian. The method is applied to the field of pedestrian navigation, has full-scene robust and continuous positioning capability, and can effectively realize high-precision navigation positioning under long endurance.

The invention belongs to the technical field of mobile wireless positioning, and particularly relates to a Bluetooth and UWB fusion positioning method and system based on Kalman filtering, and the method comprises the steps: taking a UWB distance measurement value and a distance converted by a Bluetooth signal distance conversion model as an observation vector through combining the low power consumption characteristic of a Bluetooth signal and the high precision advantage of UWB; according to the real-time position of a detected target, a Kalman filtering algorithm (Kalman filtering algorithm) is adopted to carry out data fusion processing, so that real-time positioning of an observation target is realized. According to the algorithm, a positioning service area is divided into a high-precision positioning area, a common-precision positioning area and a connection area according to the positioning precision requirement, and UWB positioning anchor point equipment and BLE positioning anchor point equipment are arranged in different positioning areas at the same time. And an indoor fixed person navigation positioning model is formed by adopting UWB and BLE pedestrian navigation constraints for the connection area, so that the requirements of relatively high precision and relatively low cost are met.

The invention discloses a pedestrian navigation method and system based on intelligent fusion of optical signals and inertia. The method comprises the following steps: calculating the distance between a receiving end and a transmitting end according to the positions of a visible light transmitting end and a receiving end and visible light original data collected by the receiving end; calculating the collected inertial data by adopting a pedestrian dead reckoning method to obtain position information of a receiving end; and carrying out tight combination fusion on the distance between the receiving end and the transmitting end and the position information of the receiving end based on a particle filtering algorithm to obtain a predicted pedestrian position. According to the method, the pedestrian dead reckoning positioning result and the visible light ranging information are fused, the defects of a single positioning technology are overcome, the method has good performance on a fusion positioning system of visible light and other signals susceptible to environmental interference and inertial and other high-noise signals, the adverse effect of environmental noise on the fusion system is weakened, and the positioning accuracy is improved. And the migration capability and the adaptive capability of the fusion system are effectively improved.

The application discloses a rescue navigation method, device and equipment based on a three-dimensional road network, the method comprising: constructing a three-dimensional road network of a target area, the three-dimensional road network comprising a first layer constructed based on stake number line information and a second layer constructed based on grid data; monitoring a rescue request sent by an intelligent safety helmet in the target area; generating a rescue strategy based on the rescue request in response to the rescue request; generating a navigation route based on the rescue strategy and the three-dimensional road network; and performing rescue navigation based on the navigation route, wherein the navigation route comprises a vehicle navigation route and a pedestrian navigation route, the vehicle navigation route is generated based on the first layer of the three-dimensional road network, and the pedestrian navigation route is generated based on the second layer of the three-dimensional road network, so that the three-dimensional road network can effectively adapt to different rescue navigation scenarios, the rescue response speed is greatly improved, rescue personnel and vehicles can timely arrive at a rescue position, and the problem that an unreasonable navigation plan leads to an incorrect best rescue window is effectively avoided.

The invention discloses a pedestrian navigation method based on self-adaptive search interval zero-speed detection. The pedestrian navigation method comprises the following steps: S1, periodically collecting acceleration data and angular velocity data output by an inertial sensor; s2, judging whether the navigation system is initialized or not, if the navigation system is not initialized, initializing the navigation system, and entering S1 to recollect acceleration data and angular velocity data; if so, entering the step S3; s3, performing adaptive interval zero-speed point search based on the acceleration data and the angular velocity data, obtaining an inertia factor and a zero-speed factor based on an adaptive search interval, and constructing an optimization equation to predict inertia information according to the inertia factor and the zero-speed factor; and S4, predicting the pose change from the previous zero speed point to the current moment based on the inertial information, and outputting carrier navigation information. According to the method, accumulative error divergence can be effectively restrained by combining a self-adaptive zero-speed point detection technology with a graph optimization framework, and pedestrian positioning information is obtained.

The present invention relates to an adaptive zero-speed detection algorithm based on interval search under variable speed motion conditions, and belongs to the field of pedestrian navigation technology. It includes: 1) zero-speed interval proportion analysis; 2) gait cycle extraction; 3) preliminary screening of zero-speed intervals; 4) fine determination of zero-speed intervals. The present invention determines the search interval of the gait cycle by performing SWA filtering on the modulus of angular velocity, and preliminarily determines a zero-speed interval based on the statistical characteristics of the zero-speed interval in the gait cycle under different speeds, and sets a new threshold based on the 3σ criterion to achieve adaptive zero-speed detection. The present invention not only provides a new, efficient and accurate positioning solution for wearable devices and location services, but also provides a valuable reference for pedestrian navigation research under complex motion patterns.

The present invention discloses a navigation method based on an inelastic boundary constraint integrated multi-dimensional factor combination. First, environmental magnetic fingerprint data is collected in an offline stage to obtain a geomagnetic fingerprint library; and step length, heading angle, GNSS positioning data and magnetic fingerprint measurement values ​​are collected in real time through pedestrian navigation; step length factor, differential heading angle factor, magnetic fingerprint factor, GNSS factor are calculated and a factor graph model is constructed: a sliding window optimization strategy is adopted to only retain the state nodes of the most recent several moments in the preliminary factor graph model for optimization; the optimized factor graph model is applied to obtain the optimal navigation trajectory. This method improves positioning accuracy and robustness in complex environments. It addresses the problems that traditional PDR systems are susceptible to single-step abnormal estimation, magnetic fingerprint positioning is susceptible to local magnetic interference, and GNSS positioning has poor stability in blocked areas.

The present application relates to a kind of pedestrian collaborative navigation method based on distance and angle, the collaborative navigation method includes: S1, by MIMU output each pedestrian respective three-axis accelerometer and three-axis gyroscope data, and carry out strapdown inertial navigation solution, again by the state of zero speed detection to the data of inertial navigation solution is carried out zero speed correction by extended Kalman filtering;S2, by UWB ranging angle measurement obtains the relative distance and angle between pedestrians as observation, then with the position information and heading angle information of N pedestrians as state quantity, carries out Kalman filtering, obtains the correction data of the position information and heading angle information of each pedestrian;S3, according to the position information and heading angle information of each pedestrian after correction collaborative navigation is carried out.The present application uses distance and angle as observation to correct pedestrian navigation after ZUPT auxiliary, solves the current pedestrian navigation heading angle drift and the problem of large amount of calculation of centralized filtering.

The present invention discloses a visually assisted gait feature constrained pedestrian navigation method, which comprises the following steps: firstly, using the data collected by the inertial measurement unit to analyze the gait characteristics of the pedestrian and calculate the zero-speed interval; then, calibrating the inertial measurement unit and the camera to obtain corresponding internal and external parameters, solving the position and attitude data of the six-degree-of-freedom of the visual inertial odometer, and obtaining the relative posture information of the pedestrian's movement; then adopting the strapdown inertial navigation calculation method to perform initial alignment and inertial navigation solution to obtain the pedestrian's instantaneous attitude, speed and position information; finally, establishing a Kalman filter model, using the speed difference and angular velocity difference in the zero-speed interval, the attitude error and position error when the visual signal is valid as observation quantities for measurement and update; using the current corrected state quantity as the initial quantity for the next inertial navigation solution, and repeating the navigation correction process. The present invention can constrain the divergence of navigation errors in an environment where GNSS signals fail, and improve the accuracy of pedestrian navigation positioning.

The invention discloses a rescue navigation method, device and equipment based on a three-dimensional road network, and the method comprises the steps: constructing the three-dimensional road network of a target area, the three-dimensional road network comprises a first layer constructed based on stake mark line information and a second layer constructed based on raster data, monitoring a rescue request sent by an intelligent safety helmet in the target area, in response to the rescue request, generating a rescue strategy based on the rescue request, generating a navigation route based on the rescue strategy and the three-dimensional road network, and performing rescue navigation based on the navigation route, the navigation route including a vehicle navigation route and a pedestrian navigation route, the vehicle navigation route being generated based on a first layer of the three-dimensional road network, and the pedestrian navigation route being generated based on a second layer of the three-dimensional road network; the pedestrian navigation route is generated based on the second layer of the three-dimensional road network, so that the three-dimensional road network can effectively adapt to different rescue navigation scenes, the rescue response speed is greatly improved, rescue workers and vehicles are ensured to arrive at rescue positions in time, and the problem that the optimal rescue window is wrong due to unreasonable navigation planning is effectively avoided.

The following generally relates to using Augmented Reality (AR) to enhance pedestrian navigation. In some examples, AR techniques are applied to provide AR indications of a location of a group member that includes two or more AR devices. In these examples, AR techniques may be applied to determine a relative position of the group member and / or provide navigational guidance to the group member. In other examples, AR techniques are applied to provide AR-assisted pedestrian guidance. In these examples, AR techniques may be applied to present an AR display that includes information for a point of interest along a route.

The present disclosure relates to a pedestrian navigation heading constraint method, device, electronic equipment, storage medium and program product. The method comprises: obtaining inertial data of a target pedestrian; based on the inertial data, extracting the heading of the target pedestrian at the current time; in response to the heading of the target pedestrian within a first sliding window satisfying a first preset straight line condition, based on the difference information between the heading at the current time 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 a second sliding window, performing heading constraint processing on the target pedestrian at the current time, wherein the right boundaries of the first sliding window and the second sliding window are both the current time, and the length of the second sliding window is greater than the length of the first sliding window.

The application relates to the technical field of pedestrian navigation, in particular to a pedestrian positioning method and system based on building structured information assistance, which comprises the following steps: initializing a track angle through a dynamic window self-adaptive track angle initialization method according to pedestrian motion structured features; judging straight movement and turning according to the pedestrian motion structured features and the initial track angle; performing multi-dimensional error collaborative correction through an improved self-adaptive Kalman filter; performing multi-dimensional collaborative correction through a flexible constraint model and a state-by-state Kalman filter when turning; dynamically compensating state quantities after straight movement correction and state quantities after turning correction according to environmental interference structured features; obtaining compensated state quantities; and positioning the pedestrian through the compensated state quantities. The application can fully exploit multi-dimensional structured information, dynamically optimize correction strategies for different motion states, and realize adaptive compensation of environmental interference for pedestrian positioning.

The present invention belongs to the technical field of micro-electromechanical systems, and specifically discloses a badge-type consumer-grade IMU pedestrian navigation method, including: determining whether a pedestrian is in a static state; obtaining the pedestrian's motion parameters at the current moment, correcting the gyroscope and accelerometer, clearing the pedestrian's speed and performing S5; obtaining the angular velocity measured by the corrected gyroscope, and projecting the acceleration measured by the corrected accelerometer to a navigation system, and updating the pedestrian's motion parameters at the current moment according to the acceleration under the navigation system; compensating and updating the gyroscope correction according to the acceleration calculation under the navigation system obtained in S3, and performing S5; the method has the following advantages: the inertial navigation method based on the MEMS inertial measurement unit (MIMU) has the advantages of high-precision positioning effect in a short-flight scenario, is independent of external information, and is not easily affected by interference, which makes up for the shortcomings of radio positioning and is combined with the radio positioning method to improve the accuracy of pedestrian navigation positioning in an indoor environment.

The invention discloses a path navigation method and device for a mobile robot in a dense crowd environment and a medium, and the method comprises the steps: recognizing things in the dense crowd environment, and constructing semantic map information based on the things; carrying out semantic information analysis on the object, and describing human-machine-object by utilizing a unified interaction space method so as to construct an interaction space model; constructing a sampling path tree by using a plurality of sampling path grafting modes, and providing sampling paths with reasonable density based on dense crowd environment requirements; different sampling path time distribution is adopted based on different navigation modes so as to simulate different navigation behaviors of pedestrians; performing collision detection and comfort evaluation on the sampling path; setting evaluation indexes, comprehensively evaluating and screening the sampling paths through the evaluation indexes, and obtaining the sampling path corresponding to the optimal evaluation index as the optimal navigation path. The motion trail of the pedestrian is predicted in real time, path planning is rapidly updated, and safe navigation of the mobile robot in a dynamic environment is ensured.

The invention discloses a navigation method based on inelastic boundary constraint integrated multi-dimensional factor combination, which comprises the following steps: firstly, acquiring environmental magnetic fingerprint data in an offline stage to obtain a geomagnetic fingerprint database; a step length, a course angle, GNSS positioning data and a magnetic fingerprint measurement value are collected in real time through pedestrian navigation; a step length factor, a difference course angle factor, a magnetic fingerprint factor and a GNSS factor are obtained through calculation, and a factor graph model is constructed; a sliding window optimization strategy is adopted, and only state nodes at a plurality of latest moments in the preliminary factor graph model are reserved for optimization; and applying the optimized factor graph model to obtain an optimal navigation track. According to the method, the positioning precision and robustness in a complex environment are improved. The problems that a traditional PDR system is prone to being influenced by single-step abnormal estimation, magnetic fingerprint positioning is prone to being influenced by local magnetic interference, and GNSS positioning is poor in stability in an occlusion area are solved.

The invention discloses a DTW-based pedestrian navigation method and device and a storage medium, and relates to the technical field of pedestrian navigation, and the technical scheme is characterized in that the method comprises the following steps: S1, obtaining triaxial accelerometer data of an inertial sensor, carrying out secondary processing on the triaxial accelerometer data, and regenerating a new modulus value of a triaxial accelerometer by using an exponential function; s2, performing matching item construction on the regenerated new module value of the triaxial accelerometer by using a DTW algorithm, and performing zero-speed state detection in a gait cycle; and S3, performing position recursion of pedestrian navigation by using a Kalman filter according to a zero-speed state detection result in the gait cycle. According to the invention, the adaptability and flexibility of navigation are improved, the cost is reduced, and more accurate pedestrian navigation is realized.

The present invention claims protection for a three-dimensional indoor positioning method that uses ranging information to assist zero-velocity correction and altitude calculation, mainly including the following steps: (1) According to the distance between the foot and the ground output by the ranging information sensor, incorporate this distance into the data output by the inertial measurement unit to divide the gait intervals and perform zero-velocity correction; (2) When walking on a flat surface, the ranging data changes continuously in distance, while it exhibits jumps when going up and down stairs. Utilize this characteristic to identify the gait of going up and down stairs. The difference before and after the ranging jump is the height of one step; (3) Three types of information can be obtained from the ranging data: the height of the shoe from the ground, the vertical velocity of the shoe, and height information such as steps, stairs, and floors. Design a Kalman filter using these three states to perform altitude calculation, and finally obtain the pedestrian's height information to achieve high-precision three-dimensional indoor positioning of the pedestrian. The present invention not only assists zero-velocity correction to improve the horizontal positioning accuracy of pedestrian navigation, but also performs altitude calculation to improve the vertical positioning accuracy.

The invention provides a progressive optimization pedestrian enhanced autonomous navigation system based on sparse reference information. The navigation enhancement model construction module is used for carrying out frequency reduction representation on high-frequency navigation information output by the integrated navigation system to obtain navigation position representation based on two-dimensional vector mileage; according to error characteristics of direction and distance increment in the two-dimensional vector mileage, establishing a progressive pedestrian navigation enhancement model, and outputting enhanced autonomous navigation information; the abnormality diagnosis module performs abnormality diagnosis on the external position reference information; in the normal stage of the external position reference information, the enhanced navigation module carries out parameter identification and online updating on the pedestrian navigation enhanced model; and in the abnormal stage, a parameter identification process is not carried out, and enhanced navigation output is obtained by directly using the current pedestrian navigation enhancement model. According to the invention, the positioning precision can be improved.

The invention provides a traffic pedestrian navigation method and system based on multi-source information fusion, and relates to the technical field of navigation and positioning. Inputting the traffic scene multi-source information and the pedestrian historical movement data into a pedestrian intention prediction model, and estimating the potential destination or path selection tendency of the pedestrian; based on a pedestrian intention prediction result, dynamically distributing and adaptively adjusting a fusion weight of each piece of multi-source information through a fuzzy evaluation model, and carrying out fusion processing on the multi-source information by utilizing the adjusted fusion weight to generate comprehensive navigation data; and inputting the integrated navigation data and the pedestrian intention prediction result into a path planning model based on reinforcement learning to generate a personalized navigation path. According to the invention, by introducing a pedestrian intention prediction mechanism, the dynamic optimization of the multi-source information fusion weight is realized, and the accuracy and adaptability of pedestrian navigation in a complex urban environment are effectively improved.

This invention discloses a path navigation method, device, and medium for mobile robots in densely populated environments. The method includes identifying objects in the densely populated environment and constructing semantic map information based on these objects; performing semantic information analysis on the objects and using a unified interaction space method to describe the human-machine-object relationship to construct an interaction space model; constructing a sampling path tree by grafting several sampling paths to provide sampling paths with a reasonable density based on the needs of the densely populated environment; using different sampling path time allocations based on different navigation modes to simulate different pedestrian navigation behaviors; performing collision detection and comfort assessment on the sampling paths; setting evaluation indicators and comprehensively evaluating and filtering the sampling paths based on these indicators to obtain the sampling path corresponding to the best evaluation indicator as the optimal navigation path. This invention predicts pedestrian movement trajectories in real time and quickly updates path planning, ensuring safe navigation of the mobile robot in dynamic environments.

Systems, methods, and other embodiments described herein relate to providing navigational guidance to a pedestrian on infrastructure elements or vehicles in the vicinity of the pedestrian based on the pedestrian being identified as unfamiliar with an environment. In one embodiment, a method includes determining that a pedestrian is in an unfamiliar area based on sensor data that characterizes the pedestrian. The method also includes acquiring a navigational instruction presented on a user device of the pedestrian. The method further includes presenting navigational assistance on an electronic display in a vicinity of the pedestrian responsive to an indication that the pedestrian is in the unfamiliar area. The navigational assistance is based on the navigational instruction.

The present application relates to a kind of walking navigation method, based on the identification and positioning of user, guide equipment deployed in the environment around user is guided to user, in the navigation process, user can get rid of handheld terminal device;Further, there is no inherent defect of the technology itself of prior art, such as satellite navigation, navigation based on RSSI positioning, positioning based on the technical scheme of connected device, the performance influence of handheld terminal device related hardware, the precision and real-time problem caused by more environmental interference factors such as poor problem.The present application obtains the physical properties of user in passageway by the corresponding type of sensor deployed in passageway and covering passageway, identifies and locates user, can build pure intranet, can realize navigation, avoid the data security hidden danger existing in data interaction using public communication platform.The present application can be implemented in any scene where sensor can be deployed, and is highly versatile.

The application claims a kind of strapdown inertial navigation pedestrian three-dimensional position correction method, belongs to the field of inertial pedestrian navigation, it includes the following steps: pedestrian height correction, the height of fusion by barometer and accelerometer independently solved height, the initial correction of pedestrian height is carried out;Pedestrian pitch angle correction, the relationship between step height and pitch angle of pedestrian step length is used to calculate pitch angle, and the pitch angle is fused with attitude solution pitch angle, and the pitch angle is corrected;Pedestrian three-dimensional position correction, the relationship between pedestrian pitch angle, step length and heading angle is used to correct the three-dimensional position of pedestrian.The position information of pedestrian in three-dimensional space in strapdown inertial navigation system is corrected gradually by the above three sub-algorithms according to the application.The method disclosed by the application can realize the accurate positioning of pedestrian position in three-dimensional space, which can not only be applied to the existing city high-rise building such a more regular experimental environment pedestrian inertial navigation, especially suitable for more extensive irregular environment such as ramp, mountainous environment.

The application discloses an indoor pedestrian navigation method and system based on behavior probability analysis, wherein the method steps comprise the following steps: based on the measurement result of an IMU sensor, a Kalman filter is used to predict the current state of an indoor pedestrian, and a prediction result is obtained; a gait detection method is used to extract a maximum probability zero speed point of the indoor pedestrian; a stair step probability detection method is used to estimate the height of the pedestrian climbing stairs, and an estimation result is obtained; and the prediction result is corrected by using the maximum probability zero speed point and the estimation result, so that indoor pedestrian navigation is completed. The application extracts the zero speed point based on the maximum probability, is suitable for different gaits, and reduces the influence of unstable barometer output on height correction. Through experimental verification, the application can realize high positioning accuracy in an indoor scene.

The application discloses a pedestrian trajectory data rectification method and device, equipment and medium, and relates to the technical field of navigation. The method comprises the following steps: obtaining pedestrian trajectory data, and obtaining trajectory structured data based on the pedestrian trajectory data; obtaining a trajectory mean value of the trajectory structured data within a preset time, determining whether the trajectory structured data is offset based on the trajectory mean value, and if it is determined that the trajectory structured data is offset, performing position rectification on the trajectory structured data based on a preset error value. The application does not depend on any third-party map data, can adapt to the requirements of any third-party map manufacturer and business system on pedestrian navigation data, does not need to develop a system again, and can be connected in a module form at any time.

This invention discloses a gait detection method based on edge-side LSTM, belonging to the field of inertial pedestrian navigation technology. The method first fixes a device equipped with inertial sensors to the feet of the test subject to collect gyroscope and accelerometer data in real time. Then, the collected data is divided into training and validation sets, and converted into several samples suitable for deep learning training. An LSTM neural network is constructed, the LSTM model is trained using the training set, and the model's classification ability is verified using the validation set. Model parameters and training parameters are adjusted until the model's classification results meet experimental expectations, and the LSTM model is saved. Finally, the LSTM model is appropriately pruned and converted into a general neural network model, which is then deployed in a wearable device to classify the real-time foot inertial data to obtain gait detection results. This invention can provide pedestrians with real-time edge-side gait detection capabilities, providing a basis for edge-side navigation calculation and pedestrian state judgment.

The application provides a method for determining the waiting time of a pedestrian crossing a road, a method for prompting the waiting time of the pedestrian crossing the road, a method for generating the travel duration of pedestrian navigation, a device, an electronic device and a storage medium. According to the embodiment of the application, the position information of the pedestrian and the target road to be crossed by the pedestrian are determined first, then the target vehicle traveling to the travel path of the target road to be crossed by the pedestrian is determined, and the travel time required for the target vehicle to travel to the travel path is calculated. Finally, the waiting time of the pedestrian crossing the target road is determined based on the travel time required for the target vehicle to travel to the travel path and the crossing time required for the pedestrian to cross the target road. Through the above scheme, the waiting time of the pedestrian crossing the road can be calculated more accurately, and the user experience of using the electronic map is improved.

The present invention belongs to the field of pedestrian navigation and positioning technology, and relates to a pedestrian inertial navigation method based on motion recognition. The method relies on wearable devices to collect IMU data and muscle electrical data; in a stationary state, the Euler angle is calculated based on the IMU data and the coordinate rotation matrix is ​​obtained, the acceleration in the carrier coordinate system is converted to the navigation coordinate system, and the speed information is obtained after integration; after preprocessing and feature extraction of the muscle electrical data, an SVM model based on statistics and motion correction algorithms is used to identify the motion state of the human body in a mixed motion mode, and a motion recognition result is obtained; the motion recognition result is used to adaptively adjust the threshold detection of the zero-speed interval, and finally, the zero speed is corrected in the zero-speed interval using an EKF-based speed correction method and the corrected speed is integrated to obtain position information. The method can achieve higher positioning accuracy, smaller positioning error, better robustness and more accurate trajectory drawing in a mixed motion mode.

This invention discloses a pedestrian navigation method based on the RANSAC algorithm, relating to the field of pedestrian navigation. The method collects acceleration and angular velocity data during pedestrian movement using an inertial measurement unit (IMU). First, it calculates the sensor variance within a sliding window and dynamically adjusts the adaptive threshold for determining a stationary state. Then, it uses the RANSAC algorithm for multiple rounds of random sampling and model validation. By constructing a stationary state model, it filters interior points, retaining the model with the most interior points and determining the stationary moment based on the proportion of interior points. When stationary is determined, it triggers Zero Velocity Correction (ZUPT) and combines it with Kalman filtering to achieve navigation and positioning. The process noise covariance matrix and observation noise covariance matrix of the Kalman filter can be adaptively adjusted during operation. This invention solves the problem of traditional threshold methods being susceptible to noise and outlier interference, significantly improving the robustness of stationary state detection and the positioning accuracy of pedestrian navigation.