Road network matching pedestrian navigation method and device based on orthogonal projection and similarity principle

Abstract

The invention provides a road network matching pedestrian navigation method and device based on an orthogonal projection and similarity principle. The method comprises the following steps: acquiring a matching road of a pedestrian navigation system in a road network map; performing orthogonal projection on the calculated position of the pedestrian navigation system on the matched road, determining a position error of the pedestrian navigation system in a direction vertical to the matched road, and correcting the calculated position of the pedestrian navigation system according to the position error; and performing similarity modeling on the position error based on a similarity principle to obtain a gyro course attitude angle error of an inertial measurement unit in the pedestrian navigation system, and correcting the calculated position of the pedestrian navigation system according to the gyro course attitude angle error. According to the method, only position errors in the vertical matching road direction are observed, so that introduction of additional errors can be avoided, and a road constraint function is realized; meanwhile, the method not only can be used for correcting the position, but also can be used for correcting the course error of the pedestrian navigation system, thereby further improving the positioning accuracy of the pedestrian navigation system.

Description

Technical Field

[0001] The present invention relates to the technical field of pedestrian navigation, and in particular to a road network matching pedestrian navigation method and device based on orthogonal projection and similarity principles. Background Art

[0002] Road network matching is the process of calculating the navigation system's position within the road network by comparing the location information collected by the navigation system with that in the road network database. Currently, road network matching algorithms can be categorized into geometric matching, topological matching, probabilistic statistical, and advanced matching algorithms, depending on the data processing method used.

[0003] Geometric-based road network matching algorithms prioritize information between roads, such as point-to-line distances and line-to-line angles. This approach is simple, computationally inefficient, and easy to implement. It performs well when positioning data quality is high and road complexity is low. However, it can easily lead to misjudgments on complex roads or when positioning results have large errors.

[0004] The topology-based road matching algorithm uses adjacency, association, and connectivity between points, lines, and polygons, focusing on road connectivity and trajectory similarity to constrain matching results. This method effectively considers the actual conditions of roads, thereby improving matching efficiency and accuracy to a certain extent.

[0005] Road network matching algorithms based on probabilistic statistical models assign a confidence interval to each collected location sample based on a probability criterion. The matching probability is calculated by considering the error between the GNSS (Global Navigation Satellite System) positioning point and the position within the confidence interval, and the optimal matching result is obtained based on this. This method does not consider historical information, so even if a point is matched incorrectly, it will not significantly affect subsequent matching. Furthermore, setting a confidence interval narrows the range of candidate road segments, thereby improving algorithm efficiency. However, this algorithm is relatively complex to implement and its matching results are less stable in complex road networks.

[0006] Advanced matching algorithms, such as those based on deep learning models, consider more comprehensive road network and observation information or employ more advanced models for map matching. These algorithms offer robustness and high matching accuracy, but they often suffer from complex models that lead to high computational complexity, are sensitive to road data quality, and require large amounts of labeled and training data.

[0007] Pedestrian navigation is an autonomous navigation technology that relies on inertial technology. It is typically implemented using zero-speed correction or pedestrian dead reckoning. It is completely independent of external information input and has the advantages of strong autonomy and anti-interference. Taking the zero-speed correction method as an example, its implementation principle is that when the carrier is detected to be in a stationary state, the speed calculated by the navigation system is used as the observed quantity of the system velocity error, and a Kalman filter estimation is performed. The updated state estimate is then used as feedback to correct the navigation parameter errors within the system, achieving static zero-speed correction. Although pedestrian navigation is highly autonomous, it suffers from the problem of navigation error increasing linearly with time, requiring the assistance of other positioning methods to correct this error.

[0008] Road-network matching pedestrian navigation combines road-network matching with pedestrian navigation technology. Using this information, the navigation system's position within the road network is determined, thereby correcting the system's accumulated errors. Existing pedestrian navigation methods based on road-network matching often use collected location data or trajectories to directly locate points on the road, or to first locate the road and then project the location onto a matching road, using this as the corresponding true position to correct the navigation system's positional errors.

[0009] However, the effectiveness of existing road network matching pedestrian navigation technology depends largely on the accuracy of the road network matching results. Specifically, the road network matching algorithm locates the position of the pedestrian navigation system at a specific location on the road and directly outputs this location as the navigation result. Furthermore, traditional road network matching methods only perform position corrections and are unable to correct the heading errors of the pedestrian navigation system.

[0010] Therefore, how to solve the problem that the correction effect of the existing road network matching pedestrian navigation method depends to a large extent on the accuracy of the road network matching results, and the traditional road network matching method only performs position correction but cannot correct the system heading error, is an important issue that needs to be urgently solved in the field of pedestrian navigation. Summary of the Invention

[0011] The present invention provides a road network matching pedestrian navigation method and device based on the principle of orthogonal projection and similarity, which is used to overcome the defects of the existing road network matching pedestrian navigation method, that is, the correction effect depends to a large extent on the accuracy of the road network matching result, and the traditional road network matching method only performs position correction but cannot correct the system heading error, thereby improving the positioning accuracy of the pedestrian navigation system and increasing the versatility and universality of the road network matching pedestrian navigation method.

[0012] On the one hand, the present invention provides a road network matching pedestrian navigation method based on orthogonal projection and similarity principles, including: obtaining a matching road of a pedestrian navigation system in a road network map; orthogonally projecting the estimated position of the pedestrian navigation system on the matching road, determining the position error of the pedestrian navigation system in the direction perpendicular to the matching road, and correcting the estimated position of the pedestrian navigation system according to the position error; performing similarity modeling on the position error based on the similarity principle, obtaining a gyro heading attitude angle error of an inertial measurement unit in the pedestrian navigation system, and correcting the estimated position of the pedestrian navigation system according to the gyro heading attitude angle error.

[0013] Furthermore, the obtaining of matching roads of the pedestrian navigation system in the road network map includes: calculating the projected distance between the position observation point of the pedestrian navigation system and the road based on the open street map; setting multiple distance threshold parameters and corresponding observation probability parameters according to the zero bias error of the pedestrian navigation system; determining the observation probability of a hidden Markov model according to the projected distance, the multiple distance threshold parameters and the corresponding observation probability parameters; determining the state transition probability of the hidden Markov model according to the road topological relationship; and solving the matching roads of the pedestrian navigation system in the road network map according to the observation probability and state transition probability of the hidden Markov model.

[0014] Furthermore, the estimated position of the pedestrian navigation system is orthogonally projected on the matching road to determine the position error of the pedestrian navigation system in the direction perpendicular to the matching road, including: when the gait of the moving target is in a completely landed state, performing zero-speed correction on the pedestrian navigation system to obtain the estimated position of the pedestrian navigation system in the geodetic coordinate system; converting the estimated position of the pedestrian navigation system in the geodetic coordinate system to the road coordinate system; determining the position of the estimated position in the road coordinate system in the X-axis direction as the position error of the pedestrian navigation system in the direction perpendicular to the matching road; wherein, the road coordinate system takes one end point of the matching road as the origin, the direction perpendicular to the matching road as the X-axis, the direction parallel to the matching road as the Y-axis, and the normal direction of the reference ellipsoid as the Z-axis.

[0015] Furthermore, the step of converting the estimated position of the pedestrian navigation system in the geodetic coordinate system to the road coordinate system specifically includes: determining the starting endpoint coordinates and the ending endpoint coordinates of the matching road in the geodetic coordinate system, as well as the estimated position of the pedestrian navigation system; based on the conversion relationship between the geodetic coordinate system and the northeast celestial coordinate system, converting the starting endpoint coordinates, the ending endpoint coordinates and the estimated position in the geodetic coordinate system to the northeast celestial coordinate system; and calculating the estimated position of the pedestrian navigation system in the road coordinate system based on the starting endpoint coordinates, the ending endpoint coordinates and the estimated position in the northeast celestial coordinate system.

[0016] Furthermore, the step of correcting the estimated position of the pedestrian navigation system according to the position error specifically includes: inputting the position error as an observation vector into a Kalman filter to update the estimated position of the pedestrian navigation system.

[0017] Furthermore, the relationship between the position error and the gyro heading attitude angle error is as follows: ; in, represents the position error of the pedestrian navigation system's estimated trajectory, represents the actual trajectory of the pedestrian navigation system. Represents the gyro heading attitude angle error of the inertial measurement unit in the pedestrian navigation system.

[0018] In a second aspect, the present invention also provides a road network matching pedestrian navigation device based on the principle of orthogonal projection and similarity, comprising: a road network matching module for obtaining the matching roads of the pedestrian navigation system in the road network map; a first navigation position correction module for orthogonally projecting the estimated position of the pedestrian navigation system on the matching road, determining the position error of the pedestrian navigation system in the direction perpendicular to the matching road, and correcting the estimated position of the pedestrian navigation system according to the position error; a second navigation position correction module for performing similarity modeling on the position error based on the similarity principle, obtaining the gyro heading attitude angle error of the inertial measurement unit in the pedestrian navigation system, and correcting the estimated position of the pedestrian navigation system according to the gyro heading attitude angle error.

[0019] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, it implements any of the above-described road network matching pedestrian navigation methods based on orthogonal projection and similarity principles.

[0020] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for pedestrian navigation based on road network matching and orthogonal projection and similarity principle as described above is implemented.

[0021] The present invention also provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-described road network matching pedestrian navigation methods based on orthogonal projection and similarity principles.

[0022] The present invention provides a road network matching pedestrian navigation method based on orthogonal projection and similarity principles. This method obtains matching roads for a pedestrian navigation system in a road network map, orthogonally projects the pedestrian navigation system's estimated position onto the matching roads, determines the pedestrian navigation system's position error perpendicular to the matching roads, and corrects the pedestrian navigation system's estimated position based on the position error. Furthermore, similarity modeling is performed on the position error based on the similarity principle to obtain the gyroscopic heading attitude angle error of the pedestrian navigation system's inertial measurement unit. The estimated position of the pedestrian navigation system is corrected based on the gyroscopic heading attitude angle error. By observing only the position error perpendicular to the matching roads, this method avoids introducing additional errors and thus implements a road constraint function. Furthermore, this method not only corrects the position but also the heading error of the pedestrian navigation system, further improving the positioning accuracy of the pedestrian navigation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0024] Figure 1 It is a flowchart of a road network matching pedestrian navigation method based on orthogonal projection and similarity principle provided by an embodiment of the present invention.

[0025] Figure 2 Schematic diagram of road deviation of a pedestrian navigation system provided by an embodiment of the present invention.

[0026] Figure 3 Schematic diagram of a road coordinate system provided by an embodiment of the present invention.

[0027] Figure 4 This is a schematic diagram of the results of reading roads in a certain area in OSM using the Geopandas tool library provided by an embodiment of the present invention.

[0028] Figure 5 This is a flowchart of a pedestrian navigation system for road network matching based on orthogonal projection correction and estimated position, provided by an embodiment of the present invention.

[0029] Figure 6 It is a schematic diagram of the effect of correcting the position of a pedestrian navigation system based on orthogonal projection provided by an embodiment of the present invention.

[0030] Figure 7 1 is a schematic diagram of a trajectory of a pedestrian navigation system using only zero-speed correction provided by an embodiment of the present invention.

[0031] Figure 8 1 is a schematic diagram of a trajectory of a pedestrian navigation system based on road constraints of orthogonal projection provided by an embodiment of the present invention.

[0032] Figure 9 This is a result diagram of a pedestrian navigation experiment using only inertial navigation and zero-speed correction, as provided by an embodiment of the present invention.

[0033] Figure 10 This is a result diagram of a pedestrian navigation experiment trajectory based on orthogonal projection and similarity principles for road network matching provided by an embodiment of the present invention.

[0034] Figure 11 It is a structural diagram of a road network matching pedestrian navigation device based on orthogonal projection and similarity principles provided by an embodiment of the present invention.

[0035] Figure 12 It is a schematic diagram of the physical structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0036] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0037] It's important to note that the effectiveness of existing road network matching pedestrian navigation technology relies heavily on the accuracy of the road network matching results. This involves using a road network matching algorithm to pinpoint the position of the pedestrian navigation system to a specific location on the road and directly outputting that location as the navigation result. This method doesn't consider the mathematical relationship between inertial device errors and road network matching results, nor does it use the road network matching results to constrain inertial device errors. Consequently, positioning errors rapidly diverge after road network matching fails.

[0038] In addition, existing technologies rarely consider the relationship between the trajectory estimated by the pedestrian navigation system and the actual road when using zero-speed correction, and perform similarity modeling on the position error to estimate the attitude misalignment angle error in a short period of time.

[0039] In view of this, the present invention proposes a road network matching pedestrian navigation method based on orthogonal projection and similarity principle. Specifically, Figure 1 A flow chart of a road network matching pedestrian navigation method based on orthogonal projection and similarity principles provided by an embodiment of the present invention is shown.

[0040] like Figure 1As shown, the method includes steps S110-S130, and steps S110-S130 and related steps will be described in detail below.

[0041] S110: Obtain matching roads in the road network map of the pedestrian navigation system.

[0042] A pedestrian navigation system (PNS) is a navigation solution designed specifically for pedestrians, helping them find the optimal path from one location to another in various environments. PNS can be applied in a variety of scenarios, including but not limited to urban navigation, indoor positioning and navigation, and tourist guides. A PNS includes at least one location acquisition module, such as an inertial measurement unit (IMU). The IMU includes an accelerometer and gyroscope, which are used to estimate the target's direction of movement and step count, especially when GPS signals are weak or unavailable.

[0043] A road network map is a thematic map in a Geographic Information System (GIS), which is specifically used to represent the layout, connectivity, and other related information of a road network.

[0044] In this embodiment, it is assumed that the actual trajectory of the moving target coincides with the road and that the target moves within the center of the road. A road network matching algorithm is then implemented using a hidden Markov model. In the road network matching problem, before the pedestrian navigation system's error diverges, its location information is typically only related to the current road, and the road it will be on at the next moment is also only related to the current road, meaning it will either remain on the current road or move to an adjacent road. Therefore, the road network matching problem can be assumed to satisfy the assumptions of the HMM (Hidden Markov Model). By modeling it as a decoding problem and solving it, the matching roads in the road network map for the pedestrian navigation system can be obtained.

[0045] Specifically, the open source OSM (OpenStreetMap) format road network map can be parsed to obtain an easy-to-process vector digital map. A road network matching algorithm is then implemented using a hidden Markov model. The observation probability of the hidden Markov model is set based on the heading angle of the moving target's footfall at full landing and the projected distance onto the road. The transition probability of the hidden Markov model is also set based on the topological relationships of the roads. This completes the mapping from the location data collected by the pedestrian navigation system to the corresponding roads, obtaining the matching roads in the pedestrian navigation system's road network map.

[0046] OSM is a free global map database whose goal is to create a world map that is freely editable and accessible to everyone. Road network maps in OSM format facilitate maintenance and updates of map content by mobile targets, enhancing scalability. OSM includes spatial data and attribute data. Spatial data primarily consists of three types: "points," "roads," and "relationships," which together make up the entire map. "Points" define the location of points in space; "roads" define lines or areas; and "relationships" define the relationships between elements.

[0047] It is worth mentioning that, after the target of road network matching is changed from location to road, the error tolerance of correction is improved in this embodiment, which can effectively solve the problem of poor stability of traditional road network matching constraint methods.

[0048] Based on obtaining the matching roads in the road network map of the pedestrian navigation system in step S110, step S120 is further executed.

[0049] S120 , orthogonally projecting the estimated position of the pedestrian navigation system onto the matching road, determining a position error of the pedestrian navigation system in a direction perpendicular to the matching road, and correcting the estimated position of the pedestrian navigation system according to the position error.

[0050] It is easy to understand that in the pedestrian navigation system, due to the zero bias error of the inertial devices (such as the accelerometer and gyroscope in the inertial measurement unit), the error will continue to accumulate when the strapdown inertial navigation algorithm is updated, resulting in the position calculated by the pedestrian navigation system not being completely on the road, but distributed on both sides, such as Figure 2 As shown, Figure 2 Schematic diagram of road deviation of the pedestrian navigation system provided by an embodiment of the present invention is shown. Figure 2 In the figure, the black five-pointed star represents the actual location, while the black dot represents the location estimated by the pedestrian navigation system. Over time, the estimated location of the pedestrian navigation system will deviate increasingly from the road, eventually rendering navigation ineffective. Therefore, it is necessary to use the results of road network matching (matching roads) to correct the estimated error of the pedestrian navigation system.

[0051] Specifically, by analyzing data from the accelerometer and gyroscope, it is possible to identify the stationary phase of a moving target's gait cycle, during which the target's foot is fully in contact with the ground and does not move relative to the ground. When the pedestrian navigation system detects that the target's gait is completely grounded, it performs a zero-speed correction on the system and obtains its estimated position in the road coordinate system at that moment. The pedestrian navigation system's position in the road coordinate system along the X-axis is then used as an observation of the lateral position error—that is, the position error of the pedestrian navigation system in the direction perpendicular to the road—and is corrected in the Kalman filter.

[0052] Among them, the estimated position of the pedestrian navigation system is the current position estimated based on previously known position and movement information (such as number of steps, step length, direction, etc.).

[0053] The road coordinate system is a custom coordinate system for this embodiment. Specifically, Figure 3 FIG. 1 shows a schematic diagram of a road coordinate system provided by an embodiment of the present invention. Figure 3 As shown, the origin of the road coordinate system is the endpoint of one side of the matching road, the Z axis is upward along the normal direction of the reference ellipsoid, the X axis is perpendicular to the matching road direction, and the Y axis is parallel to the matching road direction.

[0054] It's worth noting that this embodiment performs an orthogonal decomposition of the estimated position on the matching road, separating the target's motion into movement along the matching road and movement perpendicular to the matching road. Due to device errors, it's often impossible to precisely determine the target's exact position on the matching road. Therefore, this embodiment only observes the deviation from the road, avoiding the introduction of additional errors and further implementing the road constraint function.

[0055] S130, performing similarity modeling on the position error based on a similarity principle, obtaining a gyro heading attitude angle error of an inertial measurement unit in the pedestrian navigation system, and correcting the estimated position of the pedestrian navigation system according to the gyro heading attitude angle error.

[0056] It is easy to understand that the position error of the pedestrian navigation system satisfies the similarity principle. This embodiment considers the relationship between the trajectory calculated by the pedestrian navigation system and the actual road when only the zero-speed correction method is used, and performs similarity modeling on the position error, thereby estimating the gyro heading attitude misalignment angle error of the pedestrian navigation system in a short period of time and deducting it to achieve heading correction, so as to improve the positioning accuracy of the pedestrian navigation system.

[0057] In this embodiment, by obtaining the matching roads of a pedestrian navigation system in a road network map, orthogonally projecting the pedestrian navigation system's estimated position onto the matching roads, determining the pedestrian navigation system's position error in a direction perpendicular to the matching roads, and correcting the pedestrian navigation system's estimated position based on the position error, similarity modeling is then performed on the position error based on the similarity principle. The gyroscopic heading attitude angle error of the pedestrian navigation system's inertial measurement unit is obtained, and the pedestrian navigation system's estimated position is corrected based on the gyroscopic heading attitude angle error. By only observing the position error in a direction perpendicular to the matching roads, this method avoids the introduction of additional errors, thereby achieving a road constraint function. Furthermore, this method not only corrects the position but also the heading error of the pedestrian navigation system, further improving the positioning accuracy of the pedestrian navigation system.

[0058] On the basis of the above embodiments, the following will further describe in detail the process of obtaining matching roads in the road network map by the pedestrian navigation system.

[0059] Obtaining matching roads for a pedestrian navigation system in a road network map includes: calculating a projection distance between a position observation point of the pedestrian navigation system and the road based on an open street map; setting multiple distance threshold parameters and corresponding observation probability parameters according to a zero bias error of the pedestrian navigation system; determining an observation probability of a hidden Markov model based on the projection distance, the multiple distance threshold parameters and the corresponding observation probability parameters; determining a state transition probability of the hidden Markov model based on a road topological relationship; and solving for matching roads for the pedestrian navigation system in the road network map based on the observation probability and the state transition probability of the hidden Markov model.

[0060] As described above, Open Street Map (OSM) is a free global map database whose goal is to create a world map that is freely editable and accessible to everyone. OSM-formatted road network maps facilitate maintenance and updates by mobile users, enhancing scalability. OSM road network maps contain both spatial and attribute data. Spatial data primarily consists of three types: "points," "roads," and "relationships," which together comprise the entire map. "Points" define the location of points in space; "roads" define lines or areas; and "relationships" define the relationships between elements.

[0061] It is easy to understand that the original format of OSM is usually .XML and .PBF. After obtaining OSM, this embodiment converts the original OSM file into Shapefile format and then performs subsequent parsing. Among them, Shapefile file refers to a file storage method, and this file format is composed of multiple files. To form a Shapefile file, three files are indispensable, namely ".shp" (graphic format, used to save the geometric entity of the element), ".shx" (graphic index format, which can speed up the efficiency of searching forward or backward for a geometric body) and ".dbf" (attribute data format, storing attribute data of each geometric shape) file. In addition, there are optional files that can be used to enhance the expressiveness of data in space.

[0062] In this embodiment, the Geopandas tool library can be used to read, index, and debug OSM, which facilitates subsequent interaction with the data output by the pedestrian navigation system.

[0063] Figure 4 The following is a schematic diagram showing the result of reading the roads in a certain area of OSM using the Geopandas tool library provided by an embodiment of the present invention. Figure 4The displayed attributes include "osm_id," "name," "type," and "geometry." The most interesting attribute for the mobile target is "geometry," which represents each road as a series of polyline segments and provides the latitude and longitude coordinates of their endpoints. This allows for parsing OSM road information, which translates to easily processable vector digital maps.

[0064] It is worth mentioning that road network maps represented by OSM have the advantages of open source, easy access, and easy maintenance, which can increase the versatility and universality of road network matching pedestrian navigation methods.

[0065] Assuming that the pedestrian's actual trajectory coincides with the road and that the pedestrian moves within the center of the road, this embodiment can utilize a hidden Markov model to implement a road network matching algorithm. In the road network matching problem, before the pedestrian navigation system's errors diverge, its location information is typically only related to the current road, and the road it will be on at the next moment is also only related to the current road, meaning it will either remain on the current road or move to an adjacent road. Therefore, the road network matching problem can be considered to satisfy the assumptions of the HMM model and can be modeled as a decoding problem.

[0066] Specifically, firstly, based on the vector digital map obtained by analysis, the pedestrian navigation system is calculated. Position observation point at the moment To the road Projection distance between If the location observation point The projection point of the straight line on the road is on the road On, the projection distance Directly take the actual projection distance; if the position observation point The projection point of the straight line on the road is not on the road On, the projection distance The value is taken as the pedestrian navigation system in Position observation point at the moment To the road The closer endpoint is twice the distance.

[0067] Then, according to the zero bias error of the pedestrian navigation system, set A suitable distance threshold parameter and the corresponding observation probability parameter . Among them, the zero bias error of the pedestrian navigation system refers to the error caused by the inherent bias or drift of the inertial measurement unit (including accelerometers, gyroscopes, etc.) in the pedestrian navigation system. The distance threshold is used to divide different error intervals. These thresholds can be determined based on the standard deviation or other statistical characteristics of the zero bias error. For example, if the zero bias error is 0.5m, the distance threshold can be 0.5m, 1.0m, and 1.5m. Each distance threshold corresponds to an observation probability parameter, which represents the credibility or weight of the observation data within the error interval. The observation probability parameter can be determined based on the probability density function of the error distribution.

[0068] Next, the projection distance Compare with each distance threshold to determine the observation probability of the hidden Markov model. Specifically, if the projection distance , then the observation probability If the projection distance , then the observation probability ;like , then the observation probability . From this, the observation probability of the hidden Markov model can be determined , that is, if the moving target is The road at the time is , then the observed position is Among them, the moving target is The road at the time for The implicit state of the moment.

[0069] It should be noted that in addition to determining the observation probability of the hidden Markov model through the above steps In addition, the observation probability of the hidden Markov model can also be directly calculated by the following formula (1).

[0070] (1).

[0071] In formula (1), represents the standard deviation of the position observation points of the pedestrian navigation system, Represents the location observation point of the pedestrian navigation system To the road The closest great circle distance.

[0072] While calculating the observation probability of the hidden Markov model, the road topology relationship can be obtained based on the vector digital map obtained by analysis, and then the state transition probability of the hidden Markov model can be determined based on the road topology relationship. For details, please refer to the following formula (2).

[0073] (2).

[0074] In formula (2), If the moving target is The road at the time is , then at the next moment transfer to the road probability.

[0075] After modeling the road network matching problem as a decoding problem and determining the observation and state transition probabilities of the hidden Markov model, the Viterbi algorithm is used to determine the most likely corresponding road state / implicit state of the pedestrian navigation system, that is, the matching road of the pedestrian navigation system in the road network map, thereby integrating road network matching with pedestrian navigation. The Viterbi algorithm is a dynamic programming algorithm that is primarily used to find the most likely hidden state sequence in a hidden Markov model, especially when given a series of observations.

[0076] In this embodiment, the matching roads of a pedestrian navigation system in a road network map are determined based on the observation probabilities and state transition probabilities of a hidden Markov model. The estimated position of the pedestrian navigation system is then orthogonally projected onto the matching roads to determine the position error of the pedestrian navigation system in a direction perpendicular to the matching road. The estimated position of the pedestrian navigation system is then corrected based on the position error. This similarity modeling of the position error is then performed based on the similarity principle. The gyroscopic heading attitude angle error of the inertial measurement unit in the pedestrian navigation system is then obtained, and the estimated position of the pedestrian navigation system is corrected based on the gyroscopic heading attitude angle error. This method avoids introducing additional errors by observing only the position error in a direction perpendicular to the matching road, thereby achieving a road constraint function. Furthermore, this method not only corrects the position but also the heading error of the pedestrian navigation system, further improving the positioning accuracy of the pedestrian navigation system.

[0077] On the basis of the above embodiment, the orthogonal projection and correction process of the estimated position of the pedestrian navigation system on the matching road will be described in detail below.

[0078] Orthogonally projecting the estimated position of the pedestrian navigation system onto the matching road to determine the position error of the pedestrian navigation system in the direction perpendicular to the matching road, including: performing zero-speed correction on the pedestrian navigation system when the gait of the moving target is in a completely grounded state to obtain the estimated position of the pedestrian navigation system in the geodetic coordinate system; converting the estimated position of the pedestrian navigation system in the geodetic coordinate system to the road coordinate system; determining the position of the estimated position in the road coordinate system in the X-axis direction as the position error of the pedestrian navigation system in the direction perpendicular to the matching road; wherein the road coordinate system has an endpoint on one side of the matching road as its origin, a direction perpendicular to the matching road as its X-axis, a direction parallel to the matching road as its Y-axis, and a normal direction of the reference ellipsoid as its Z-axis.

[0079] It's easy to understand that when observing the estimated position of a pedestrian navigation system, the specific errors of the pedestrian navigation system are unknown, so the exact location of the moving target on the matching road is generally unknown. Only the distance error of the moving target's trajectory perpendicular to the matching road can be determined. Therefore, this embodiment orthogonally projects the estimated position of the pedestrian navigation system onto the matching road to obtain the position error perpendicular to the matching road when the moving target completely lands, and then corrects it.

[0080] Specifically, by analyzing data from the accelerometer and gyroscope, it is possible to identify the stationary phase of a moving target's gait cycle—the phase in which the target's foot is fully in contact with the ground and does not move relative to it. When the pedestrian navigation system detects that the target's gait is completely grounded, a zero-speed correction is applied to the system, resulting in the estimated position of the target in the geodetic coordinate system.

[0081] Then, the estimated position of the pedestrian navigation system in the geodetic coordinate system is converted to the road coordinate system, specifically including: determining the starting endpoint coordinates and the ending endpoint coordinates of the matching road in the geodetic coordinate system, as well as the estimated position of the pedestrian navigation system; based on the conversion relationship between the geodetic coordinate system and the northeast celestial coordinate system, converting the starting endpoint coordinates, the ending endpoint coordinates and the estimated position in the geodetic coordinate system to the northeast celestial coordinate system; and calculating the estimated position in the road coordinate system based on the starting endpoint coordinates, the ending endpoint coordinates and the estimated position in the northeast celestial coordinate system.

[0082] Specifically, the coordinates of the two endpoints of the matching road are given in the geodetic coordinate system, namely the starting endpoint coordinates and the ending endpoint coordinates. The position of the matching road in the northeast celestial coordinate system can be obtained through the conversion relationship from the geodetic coordinate system to the northeast celestial coordinate system (such as the geodetic2enu function). Assume that the starting and ending coordinates of the matching road in the geodetic coordinate system are The coordinates of the end point are , the estimated position (coordinates) of the pedestrian navigation system is Using the starting endpoint coordinates of the matching road as the reference point, the end point coordinates of the matching road in the northeast celestial coordinate system can be calculated as , the estimated position (coordinates) of the pedestrian navigation system in the northeast sky coordinate system is Finally, based on the basic geometric relationship, the estimated position of the pedestrian navigation system in the road coordinate system can be obtained, as shown in the following equations (3)-(5).

[0083] (3).

[0084] (4).

[0085] (5).

[0086] Among them, the origin of the northeast celestial coordinate system is located at the center of mass of the carrier, its X axis points to the east along the meridian of the reference ellipsoid, its Y axis points to the north along the meridian of the reference ellipsoid, and its Z axis points to the sky along the normal of the reference ellipsoid.

[0087] After obtaining the estimated position of the pedestrian navigation system in the road coordinate system, the position of the estimated position in the road coordinate system in the X-axis direction is determined as the position error of the pedestrian navigation system in the direction perpendicular to the matching road.

[0088] Finally, the estimated position of the pedestrian navigation system is corrected using a Kalman filter based on the position error of the pedestrian navigation system in the direction perpendicular to the road. The step of correcting the estimated position using the Kalman filter includes a prediction step and an update step.

[0089] In the prediction step, a state vector and a state transition matrix are defined. These are used to predict the state at the next time point and estimate the uncertainty of the predicted state at that point. In the update step, an observation vector and an observation matrix are used. When a new observation vector is received, the observation matrix is used to convert the predicted state into an expected observation value. The difference between the expected and actual observation values is calculated to adjust the state prediction based on this difference, producing a more accurate navigation result, which is the estimated position of the pedestrian navigation system.

[0090] In this embodiment, the 15-dimensional state vector of the Kalman filter is ,in, Indicates the attitude misalignment angle of the calculated navigation coordinate system relative to the ideal navigation coordinate system, Indicates the velocity error of the calculated navigation coordinate system relative to the ideal navigation coordinate system. Similarly, Indicates the position error of the calculated navigation coordinate system relative to the ideal navigation coordinate system, Indicates the zero bias measured by the gyroscope, Indicates the zero bias measured by the accelerometer.

[0091] State transition matrix Specifically expressed as ,in, Represent the transfer matrices of the system's attitude error, velocity error, and position error to the attitude error change (differential), which are given by the classic strapdown inertial navigation attitude error equation. Represents the attitude transformation matrix from the carrier coordinate system to the navigation coordinate system, Represent the transfer matrices of the system's attitude error, velocity error, and position error to the velocity error change (differential), which are given by the classic strapdown inertial navigation velocity error equation. They represent the transfer matrices of the system's velocity error and position error to the change (differential) of the position error, respectively, and are given by the classic strapdown inertial navigation attitude error equation. and The inverse of the correlation time of the process (random walk, first-order Gauss-Markov process, or autoregressive process) representing the gyroscope and accelerometer, respectively.

[0092] Observation vector ,in, Indicates the eastward speed of the moving target in the navigation coordinate system, and They represent the north velocity and celestial velocity of the moving target in the navigation coordinate system respectively. Indicates the lateral (X-axis) position in the road coordinate system of the matching road. Indicates the longitudinal position in the road coordinate system of the matching road, Indicates the altitude of the matched road.

[0093] When the foot of the moving target is detected to have completely landed, the speed calculated by the pedestrian navigation system at that moment is input as the error value into the observation state. and altitude There is also an observation, but due to the existence of sensor error, the longitudinal position Usually it is impossible to obtain accurate observation in real time, so this embodiment will Setting it to 0 means that there is no error, which can avoid introducing additional observation errors and thus better correct the status of the pedestrian navigation system.

[0094] Observation Matrix , ,in, Represents the length of the matching road in meters. Represents the eastward distance from the end point of the road to the starting point of the road, Represents the north distance from the end point of the road to the starting point of the road. Represents the change in distance (meters) corresponding to the change in unit longitude of the road location. Represents the change in distance (meters) corresponding to a change in unit longitude of the road location.

[0095] Accordingly, Figure 5 A flowchart of a pedestrian navigation process for road network matching based on an orthogonal projection correction pedestrian navigation system estimated position provided by an embodiment of the present invention is shown.

[0096] like Figure 5As shown, the initial position and attitude of the pedestrian navigation system are initialized, along with the system noise and measurement noise of the Kalman filter. The inertial measurement unit (IMU) in the pedestrian navigation system is then left stationary for ten seconds, and a preliminary zero-bias error is deducted. Strapdown dead reckoning is then performed on the pedestrian navigation system to detect whether the moving target (e.g., a pedestrian) has completely landed. If so, a zero-speed correction is performed on the pedestrian navigation system, and a road network matching process is performed to determine the matching road for the pedestrian navigation system in the road network map. A coordinate system transformation is then performed, and road constraints are applied to the estimated position of the pedestrian navigation system. If not, strapdown dead reckoning continues.

[0097] also, Figure 6 The schematic diagram shows the effect of the position estimation based on the orthogonal projection correction provided by the pedestrian navigation system according to the embodiment of the present invention. Figure 6 In the figure, the black dot is the estimated position of the pedestrian navigation system, and the five-pointed star is the position estimation result after road constraints (based on orthogonal projection correction). Figure 6 It can be seen that after orthogonal decomposition, the error in the X-axis direction of the matched road will be gradually corrected after each road network match, and the part that exceeds the road at the turn will not be forced to be pulled back to the matched road. This can effectively improve the success rate of road network matching in a pure inertial navigation system.

[0098] In this embodiment, a zero-speed correction is performed on the pedestrian navigation system when the moving target's gait is completely grounded, obtaining the estimated position of the pedestrian navigation system in the geodetic coordinate system. This estimated position is then converted to a road coordinate system. The X-axis position of the estimated position in the road coordinate system is then determined as the position error of the pedestrian navigation system in the direction perpendicular to the road. The estimated position of the pedestrian navigation system is then corrected based on the position error. Consequently, similarity modeling of the position error is performed based on the similarity principle, and the gyroscopic heading attitude angle error of the inertial measurement unit in the pedestrian navigation system is obtained. The estimated position of the pedestrian navigation system is then corrected based on the gyroscopic heading attitude angle error. This method avoids the introduction of additional errors by observing only the position error in the direction perpendicular to the road, thereby achieving a road constraint function. Furthermore, this method not only corrects the position but also the heading error of the pedestrian navigation system, further improving the positioning accuracy of the pedestrian navigation system.

[0099] On the basis of the above embodiment, the process of correcting the estimated position of the pedestrian navigation system based on the similarity principle will be described in detail below.

[0100] In this embodiment, the basic assumptions of the similarity principle are as follows: First, assuming that the gyroscope drift is very small or in a short time, the misalignment angular error of the system's attitude during movement is considered to be a constant small amount; second, assuming that the high-frequency periodic zero-speed correction method can effectively constrain the system's velocity error and horizontal attitude angular error, it is considered that the main source of position error is the system's heading error; third, assuming that during a short period of movement, the influence of extremely small errors such as the angular velocity of the navigation coordinate system, the angular velocity of the earth's rotation, and the earth's gravity error can be ignored.

[0101] The position error differential equation of the strapdown inertial navigation system in the navigation coordinate system is as follows (6). In this embodiment, the navigation coordinate system adopts the northeast celestial coordinate system, that is, the origin is located at the center of mass of the carrier, the X-axis points to the east along the reference ellipsoid meridian, the Y-axis points to the north along the reference ellipsoid meridian, and the Z-axis points to the sky along the reference ellipsoid normal.

[0102] (6).

[0103] In formula (6), is the angular velocity of the navigation coordinate system, is the position error of the estimated trajectory of the pedestrian navigation system in the navigation coordinate system, The misalignment angle error between the navigation coordinate system and the Earth-centered Earth-fixed coordinate system is caused by the longitude and latitude errors. is the speed of the pedestrian navigation system in the navigation coordinate system, is the velocity error in the navigation coordinate system. If only the position error of the moving target within one step is considered, the angular velocity of the navigation coordinate system can be ignored. and misalignment angle Therefore, the position error differential equation (6) can be simplified to the following equation (7).

[0104] (7).

[0105] Next, consider the velocity differential equation of the strapdown inertial navigation system in the navigation coordinate system, as shown in the following equation (8).

[0106] (8).

[0107] In formula (8), Represents the attitude transformation matrix from the carrier coordinate system to the navigation coordinate system, It represents the true value of the specific force sensed by the accelerometer in the carrier coordinate system, represents the Earth's rotation angular velocity vector, is the local gravitational acceleration. If the effects of the angular velocity of the navigation coordinate system and the angular velocity of the Earth's rotation are ignored, the above velocity differential equation (8) can be simplified to the following equation (9).

[0108] (9).

[0109] In formula (9), Represents the projection of the specific force vector perceived by the system in the east direction of the navigation coordinate system, Represents the projection of the specific force vector in the north direction of the navigation coordinate system, Represents the projection of the specific force vector in the celestial direction of the navigation coordinate system.

[0110] Considering only the situation within the horizontal plane, integrating the above equation (9) with respect to time and expanding it yields the following equations (10) and (11).

[0111] (10).

[0112] (11).

[0113] Then, the velocity error differential equation of the strapdown inertial navigation system in the navigation coordinate system is as follows (12).

[0114] (12).

[0115] Similarly, ignoring the effects of navigation coordinate system rotation, earth rotation angular velocity, and earth gravity error, and only considering the situation within the horizontal plane, the above velocity error differential equation (12) can be simplified and expanded into the following equations (13)-(14).

[0116] (13).

[0117] (14).

[0118] In formulas (13) and (14), represents the projection of the velocity error vector of the pedestrian navigation system in the east direction of the navigation coordinate system, represents the projection of the velocity error vector in the north direction of the navigation coordinate system, represents the misalignment angle error vector between the navigation coordinate system calculated by the pedestrian navigation system and the real navigation coordinate system (expressed by an equivalent rotation vector), represents the misalignment angle error vector The projection in the north direction of the navigation coordinate system, represents the misalignment angle error vector The projection in the celestial direction, represents the misalignment angle error vector The projection in the easting direction.

[0119] Since the zero-speed correction algorithm is used every time the moving target lands during the movement process, the horizontal attitude angle error can be effectively suppressed. and It can be ignored. Meanwhile, only the influence of the gyro attitude error angle is considered, and the above equations (13)-(14) are integrated to obtain the following equations (15)-(16).

[0120] (15).

[0121] (16).

[0122] If you remember , then we can get the following formula (17).

[0123] (17).

[0124] Integrating the above formula (17) with respect to time yields the following formula (18).

[0125] (18).

[0126] In formulas (17) and (18), represents the velocity error of the pedestrian navigation system’s estimated trajectory in the direction perpendicular to the road, represents the velocity vector of the pedestrian navigation system, represents the gyro heading attitude angle error of the inertial measurement unit in the pedestrian navigation system, surface Shows the actual trajectory of the pedestrian navigation system. Represents the position error of the pedestrian navigation system's dead reckoning trajectory, which is formed based on the pedestrian navigation system's dead reckoning position at multiple consecutive moments.

[0127] The above formula (18) shows that the position error of the pedestrian navigation system is caused by the misalignment angle of the gyroscope's heading attitude over a period of time, that is, the trajectory error of the pedestrian navigation system satisfies the similarity principle.

[0128] Based on the above formula (18), the gyroscopic heading attitude angle error of the inertial measurement unit in the pedestrian navigation system can be solved according to the position error of the pedestrian navigation system in the direction of vertical matching road. Then, when the pedestrian navigation system estimates the position, the gyroscopic heading attitude angle error of the inertial measurement unit is deducted, and the estimated position of the pedestrian navigation system can be further corrected to achieve heading correction, thereby ultimately improving the positioning accuracy of the pedestrian navigation system.

[0129] also, Figure 7The figure shows the trajectory diagram of the pedestrian navigation system provided by the embodiment of the present invention using only zero-speed correction, where the dotted line is the positioning trajectory of the pedestrian navigation system and the solid line is the real road. Figure 8 A schematic diagram of the trajectory of a pedestrian navigation system based on orthogonal projection road constraints, provided by an embodiment of the present invention, shows that the positioning trajectory closely matches the actual road. Therefore, at each end of the road, the gyro heading attitude angle error can be calculated by comparing the length of the pure inertial navigation trajectory with the actual road length and deducted, further improving the error estimation of the pedestrian navigation IMU and the positioning accuracy of the pedestrian navigation system.

[0130] at the same time, Figure 9 The result diagram of the pedestrian navigation experiment trajectory provided by the embodiment of the present invention using only inertial navigation and zero-speed correction is shown. It can be seen that the positioning error continues to grow over time. Figure 10 The results of the pedestrian navigation experiment trajectory based on road network matching and orthogonal projection and similarity principle provided by the embodiment of the present invention are shown, and it can be seen that the positioning accuracy is significantly improved.

[0131] It is worth mentioning that the road network matching pedestrian navigation method based on orthogonal projection and similarity principle provided by the embodiment of the present invention can be applied to smart devices such as mobile phones and bracelets, and has great economic benefits.

[0132] In addition to the road network matching pedestrian navigation method based on orthogonal projection and similarity principle described in the above embodiments, the present invention also provides a road network matching pedestrian navigation device based on orthogonal projection and similarity principle.

[0133] Specifically, Figure 11 A schematic structural diagram of a road network matching pedestrian navigation device based on orthogonal projection and similarity principles provided by an embodiment of the present invention is shown.

[0134] like Figure 11 As shown, the device includes: a road network matching module 1110, which is used to obtain the matching road of the pedestrian navigation system in the road network map; a first navigation position correction module 1120, which is used to orthogonally project the estimated position of the pedestrian navigation system on the matching road, determine the position error of the pedestrian navigation system in the direction perpendicular to the matching road, and correct the estimated position of the pedestrian navigation system according to the position error; a second navigation position correction module 1130, which is used to perform similarity modeling on the position error based on the similarity principle, obtain the gyro heading attitude angle error of the inertial measurement unit in the pedestrian navigation system, and correct the estimated position of the pedestrian navigation system according to the gyro heading attitude angle error.

[0135] In this embodiment, a road network matching module 1110 obtains a matching road in a road network map for a pedestrian navigation system. A first navigation position correction module 1120 orthogonally projects the pedestrian navigation system's estimated position onto the matching road, determines the pedestrian navigation system's position error in a direction perpendicular to the matching road, and corrects the pedestrian navigation system's estimated position based on the position error. A second navigation position correction module 1130 then performs similarity modeling on the position error based on the similarity principle, obtains the gyroscopic heading attitude angle error of the pedestrian navigation system's inertial measurement unit, and corrects the pedestrian navigation system's estimated position based on the gyroscopic heading attitude angle error. By observing only the position error in a direction perpendicular to the matching road, this device avoids introducing additional errors, thereby achieving a road constraint function. Furthermore, this method not only corrects the position but also the heading error of the pedestrian navigation system, further improving the positioning accuracy of the pedestrian navigation system.

[0136] It should be noted that the road network matching pedestrian navigation device based on orthogonal projection and similarity principle provided in the embodiment of the present invention can correspond to the road network matching pedestrian navigation method based on orthogonal projection and similarity principle described in the above embodiments, and will not be repeated here.

[0137] Figure 12 An example of a physical structure diagram of an electronic device is shown below. Figure 12 As shown, the electronic device may include: a processor 1210, a communications interface 1220, a memory 1230, and a communications bus 1240, wherein the processor 1210, the communications interface 1220, and the memory 1230 communicate with each other via the communications bus 1240. The processor 1210 may invoke logic instructions in the memory 1230 to execute a road network matching pedestrian navigation method based on orthogonal projection and similarity principles. The method includes: obtaining matching roads of a pedestrian navigation system in a road network map; orthogonally projecting the estimated position of the pedestrian navigation system onto the matching roads to determine a position error of the pedestrian navigation system in a direction perpendicular to the matching roads, and correcting the estimated position of the pedestrian navigation system based on the position error; performing similarity modeling on the position error based on the similarity principle to obtain a gyroscopic heading attitude angle error of an inertial measurement unit in the pedestrian navigation system, and correcting the estimated position of the pedestrian navigation system based on the gyroscopic heading attitude angle error.

[0138] Furthermore, the logic instructions in the aforementioned memory 1230 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0139] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the road network matching pedestrian navigation method based on orthogonal projection and similarity principles provided by the above methods. The method includes: obtaining the matching roads of the pedestrian navigation system in the road network map; orthogonally projecting the estimated position of the pedestrian navigation system on the matching road, determining the position error of the pedestrian navigation system in the direction perpendicular to the matching road, and correcting the estimated position of the pedestrian navigation system according to the position error; performing similarity modeling on the position error based on the similarity principle, obtaining the gyro heading attitude angle error of the inertial measurement unit in the pedestrian navigation system, and correcting the estimated position of the pedestrian navigation system according to the gyro heading attitude angle error.

[0140] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the road network matching pedestrian navigation method based on orthogonal projection and similarity principles provided by the above-mentioned methods, the method comprising: obtaining the matching roads of the pedestrian navigation system in the road network map; orthogonally projecting the estimated position of the pedestrian navigation system on the matching roads, determining the position error of the pedestrian navigation system in the direction perpendicular to the matching road, and correcting the estimated position of the pedestrian navigation system according to the position error; performing similarity modeling on the position error based on the similarity principle, obtaining the gyro heading attitude angle error of the inertial measurement unit in the pedestrian navigation system, and correcting the estimated position of the pedestrian navigation system according to the gyro heading attitude angle error.

[0141] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0142] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0143] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A road network matching pedestrian navigation method based on orthogonal projection and similarity principle, characterized in that: include: Obtain matching roads from the pedestrian navigation system in the road network map; orthogonally projecting the estimated position of the pedestrian navigation system onto the matching road, determining a position error of the pedestrian navigation system in a direction perpendicular to the matching road, and correcting the estimated position of the pedestrian navigation system according to the position error; Based on the similarity principle, similarity modeling is performed on the position error to obtain the gyro heading attitude angle error of the inertial measurement unit in the pedestrian navigation system, and the estimated position of the pedestrian navigation system is corrected according to the gyro heading attitude angle error.

2. The road network matching pedestrian navigation method based on orthogonal projection and similarity principle according to claim 1 is characterized in that: The obtaining of matching roads of the pedestrian navigation system in the road network map includes: Calculating the projected distance between the position observation point of the pedestrian navigation system and the road based on the open street map; Setting a plurality of distance threshold parameters and corresponding observation probability parameters according to the zero bias error of the pedestrian navigation system; Determining an observation probability of a hidden Markov model according to the projection distance, the multiple distance threshold parameters and corresponding observation probability parameters; According to the road topology relationship, the state transition probability of the hidden Markov model is determined; According to the observation probability and state transition probability of the hidden Markov model, the matching roads of the pedestrian navigation system in the road network map are solved.

3. The road network matching pedestrian navigation method based on orthogonal projection and similarity principle according to claim 1 is characterized in that: The orthogonally projecting the estimated position of the pedestrian navigation system onto the matching road to determine a position error of the pedestrian navigation system in a direction perpendicular to the matching road includes: When the gait of the moving target is in a completely landed state, performing zero-speed correction on the pedestrian navigation system to obtain a calculated position of the pedestrian navigation system in a geodetic coordinate system; Converting the estimated position of the pedestrian navigation system in the geodetic coordinate system to the road coordinate system; Determining the position of the estimated position in the road coordinate system in the X-axis direction as the position error of the pedestrian navigation system in the direction perpendicular to the matching road; The road coordinate system has an endpoint on one side of the matching road as its origin, a direction perpendicular to the matching road as its X-axis, a direction parallel to the matching road as its Y-axis, and a normal direction of the reference ellipsoid as its Z-axis.

4. The road network matching pedestrian navigation method based on orthogonal projection and similarity principle according to claim 3 is characterized in that: The step of converting the estimated position of the pedestrian navigation system in the geodetic coordinate system to the road coordinate system specifically includes: Determining the starting endpoint coordinates and the ending endpoint coordinates of the matching road in a geodetic coordinate system, and the estimated position of the pedestrian navigation system; Based on the conversion relationship between the geodetic coordinate system and the northeast celestial coordinate system, the starting endpoint coordinates, the ending endpoint coordinates and the estimated position in the geodetic coordinate system are converted to the northeast celestial coordinate system; According to the starting endpoint coordinates, the ending endpoint coordinates and the estimated position in the northeast celestial coordinate system, the estimated position of the pedestrian navigation system in the road coordinate system is calculated.

5. The road network matching pedestrian navigation method based on orthogonal projection and similarity principle according to claim 1 is characterized in that: The step of correcting the estimated position of the pedestrian navigation system according to the position error specifically includes: The position error is input into a Kalman filter as an observation vector to update the estimated position of the pedestrian navigation system.

6. The road network matching pedestrian navigation method based on orthogonal projection and similarity principle according to any one of claims 1 to 5, characterized in that: The relationship between the position error and the gyro heading attitude angle error is as follows: ； in, represents the position error of the pedestrian navigation system's estimated trajectory, represents the actual trajectory of the pedestrian navigation system. Represents the gyro heading attitude angle error of the inertial measurement unit in the pedestrian navigation system.

7. A road network matching pedestrian navigation device based on orthogonal projection and similarity principle, characterized in that: include: The road network matching module is used to obtain matching roads in the road network map for the pedestrian navigation system; a first navigation position correction module, configured to perform an orthogonal projection of the estimated position of the pedestrian navigation system on the matching road, determine a position error of the pedestrian navigation system in a direction perpendicular to the matching road, and correct the estimated position of the pedestrian navigation system according to the position error; The second navigation position correction module is used to perform similarity modeling on the position error based on the similarity principle, obtain the gyro heading attitude angle error of the inertial measurement unit in the pedestrian navigation system, and correct the estimated position of the pedestrian navigation system according to the gyro heading attitude angle error.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the road network matching pedestrian navigation method based on orthogonal projection and similarity principle as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the road network matching pedestrian navigation method based on orthogonal projection and similarity principle as described in any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the road network matching pedestrian navigation method based on orthogonal projection and similarity principle as described in any one of claims 1 to 6 is implemented.

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