阅读说明：本技术 一种基于室内地图与虚拟路标辅助的行人室内导航方法 (Pedestrian indoor navigation method based on assistance of indoor map and virtual road sign ) 是由 梁源 任章 李清东 于 2019-10-23 设计创作，主要内容包括：本发明提供了一种基于室内地图与虚拟路标辅助的行人室内导航方法,所述导航方法引入了虚拟路标的概念,虚拟路标可以在不增加任何硬件的情况下实现室内行人位置的准确修正；另外,不再利用传统的PF架构进行行人信息与地图信息的融合,而是通过“行人航迹的可行性分析+霍夫变换+基于地图拓扑关系的加权投影”的方式,将不可行的行人航迹转换至可行区域,从而达到修正导航误差的目的。本发明在保证导航精度的同时,有效降低了算法的计算量。(The invention provides a pedestrian indoor navigation method based on indoor map and virtual road sign assistance, wherein the navigation method introduces the concept of virtual road signs, and the virtual road signs can realize accurate correction of indoor pedestrian positions under the condition of not adding any hardware; in addition, the traditional PF framework is not used for fusing the pedestrian information and the map information, and the infeasible pedestrian track is converted into the feasible region in a mode of feasibility analysis of the pedestrian track, Hough transformation and weighted projection based on the map topological relation, so that the aim of correcting the navigation error is fulfilled. The invention effectively reduces the calculation amount of the algorithm while ensuring the navigation precision.)

1. A pedestrian indoor navigation method based on assistance of an indoor map and a virtual road sign is characterized by comprising the following steps:

step 1: carrying out pedestrian gait calculation according to an MEMS gyroscope and an MEMS accelerometer to obtain pedestrian track information for a period of time, firstly judging whether the track contains navigation characteristics of a certain virtual landmark point, if so, correcting the track by using the corresponding virtual landmark point, and simultaneously setting a correction identification position to be 0;

step 2: inquiring whether the correction identification bit is 1, if so, directly executing the step 3; if the track information is 0, performing feasibility detection on the pedestrian track information, if the detection result is that the track is not feasible, setting the correction identification position to be 1, and then executing the step 3; if the detection result is that the track is feasible, ending the calculation of the current round, waiting for the next section of the pedestrian track, and returning to the step 1;

and step 3: carrying out Hough transformation on the pedestrian track information, judging whether the track is regular and correctable, if so, executing the step 4, otherwise, ending the current round of operation, and waiting for the next section of pedestrian track, namely returning to the step 1;

and 4, step 4: and (3) performing weighted projection based on the map topological relation on the pedestrian track information, projecting the pedestrian track which is infeasible at present to a feasible area/road section, finishing the correction of the current track, setting the correction identification position to be 0, finishing the calculation of the current round, waiting for the next section of pedestrian track, and returning to the step 1.

2. The navigation method according to claim 1, wherein the virtual waypoint in step 1 has a unique navigation feature in the full map range, and the pedestrian position can be determined to the virtual waypoint immediately upon detecting the occurrence of the navigation feature.

3. The navigation method according to claim 1 or 2, wherein in the step 1, when the navigation features are determined, it is determined whether the pedestrian walks along a straight line, and the determination is performed by using the track information of the pedestrian in combination with hough transform.

4. The navigation method according to claim 1, wherein the feasibility test in step 2 is based on: and if the pedestrian track passes through/passes through the impassable area, the current track is considered as an impassable track, and otherwise, the current track is considered as a feasible track.

5. The navigation method according to claim 1, wherein the correction of the current track in step 4 comprises the following two parts:

firstly, screening n feasible roads/areas for travelers to walk based on a map topological relation, wherein n is more than or equal to 1;

then, the correlation degree between the pedestrian track and the n feasible roads/areas is calculated respectively.

6. The navigation method according to claim 5, wherein the calculation method of the correlation degree between the pedestrian track and the feasible road/area is as follows:

defining the included angle between the direction of the feasible road/area a and the current heading of the pedestrian as theta_a(0°≤θ_aLess than or equal to 180 degrees), the projection distance from the current position of the pedestrian to the feasible road/area a is r_aThen the matching degree rho of the feasible road/area a and the pedestrian_aComprises the following steps:

ρ_a＝ω_rr_a+ω_θθ_a

wherein, ω is_rAnd ω_θThe weights of the distance and the direction included angle are respectively.

Technical Field

The invention relates to the field of indoor navigation and positioning of pedestrians, in particular to an indoor navigation method of pedestrians based on assistance of an indoor map and a virtual road sign.

Background

With the development of the urbanization process, the indoor positioning and navigation have the potential of hiding huge social functions and economic benefits, and in view of the weakness of the GNSS navigation in the indoor environment, a new solution needs to be provided for the indoor positioning and navigation. For civil use, the positioning and navigation of indoor objects and individuals, especially for the working personnel related to personal safety and life, such as policemen, firemen, doctors and the like, have very important functions in emergency response and treatment of various critical events. Military can develop a positioning system which can be worn by soldiers and is used for establishing a ground combat training system in an urban area. In the business field, there are currently indoor shopping guide systems for large shopping malls, and future applications are believed to be more. Similarly, the indoor positioning technology can be applied to service industries such as hospitals and nursing homes to provide better services for users, and can also be applied to prisons to realize monitoring and positioning of important persons.

With the development of wireless network technology, wireless network positioning technologies based on WiFi, Ultra Wide Band (UWB), bluetooth and the like are rapidly popularized and applied in indoor environments. However, pedestrian activities and surrounding environments easily affect indoor environments, the indoor environments have complexity, the indoor positioning technology based on the wireless network cannot guarantee stable and reliable positioning effects in all scenes at present, and the wireless network positioning mode needs to be deployed indoors in advance and is not suitable for emergency places. Meanwhile, such schemes require corresponding acquisition tool software and measurement equipment to complete the acquisition of indoor electromagnetic information. The scheme has high cost and complex operation flow. Therefore, other positioning systems are needed, accurate position information can be provided in a short time, base stations do not need to be arranged in advance, and the indoor pedestrian navigation technology based on a Micro Electro Mechanical System (MEMS) sensor (MEMS gyroscope, MEMS accelerometer) has the characteristics, but the indoor pedestrian navigation technology based on the MEMS sensor has a poor positioning effect for a long time, and errors are gradually accumulated. The reason that the long-time precision of the technical scheme is poor is limited by the manufacturing process, and the low-cost MEMS sensor has the defects of low precision, large random error and the like. Therefore, much research by researchers at home and abroad focuses on how to fully mine potential information in indoor environments to improve the navigation accuracy of the indoor pedestrian navigation technology based on the MEMS sensor.

The indoor map information is an indoor auxiliary information with extremely high cost performance, and the indoor map information has been widely applied to indoor navigation positioning schemes as an auxiliary information source. The currently mainstream scheme is to modify pedestrian navigation information of an indoor map by using a Particle Filter (PF) method, and the core idea is to set the weight of particles passing through an impassable area to 0 to suppress the impassable particles, but this method is relatively complex, the number of particles is positively correlated with the navigation accuracy, it is difficult to obtain good navigation positioning accuracy with a small number of particles, and even there is a possibility of convergence to an incorrect navigation positioning result, and a large number of particles significantly increases the calculation amount and the calculation time. In addition, another main problem of the PF scheme is that the particles diverge when the pedestrian turns, and after divergence, the particles can be converged again after a long time, and in some cases, the particles cannot be converged to a real position, which seriously reduces the accuracy of pedestrian navigation positioning for a long time.

Disclosure of Invention

Aiming at the existing problems, the invention aims to provide a pedestrian indoor navigation method based on the assistance of an indoor map and a virtual road sign, which improves the navigation precision of an indoor pedestrian navigation technology based on an MEMS sensor by realizing the navigation pose correction on line in a mode of introducing the information of the indoor map and the virtual road sign, and effectively reduces the calculated amount of an algorithm while ensuring the navigation precision.

The purpose of the invention is realized by the following technical scheme:

a pedestrian indoor navigation method based on assistance of an indoor map and a virtual road sign comprises the following steps:

step 1: carrying out pedestrian gait calculation according to an MEMS gyroscope and an MEMS accelerometer to obtain pedestrian track information for a period of time, firstly judging whether the track contains navigation characteristics of a certain virtual landmark point, if so, correcting the track by using the corresponding virtual landmark point, and simultaneously setting a correction identification position to be 0;

step 2: inquiring whether the correction identification bit is 1, if so, directly executing the step 3; if the track information is 0, performing feasibility detection on the pedestrian track information, if the detection result is that the track is not feasible, setting the correction identification position to be 1, and then executing the step 3; if the detection result is that the track is feasible, ending the calculation of the current round, waiting for the next section of the pedestrian track, and returning to the step 1;

and step 3: carrying out Hough transformation on the pedestrian track information, judging whether the track is regular and correctable, if so, executing the step 4, otherwise, ending the current round of operation, and waiting for the next section of pedestrian track, namely returning to the step 1;

and 4, step 4: and (3) performing weighted projection based on the map topological relation on the pedestrian track information, projecting the pedestrian track which is infeasible at present to a feasible area/road section, finishing the correction of the current track, setting the correction identification position to be 0, finishing the calculation of the current round, waiting for the next section of pedestrian track, and returning to the step 1.

Further, the virtual landmark point in the step 1 has a unique navigation feature in the full map range, and when the navigation feature is detected to appear, the position of the pedestrian can be determined to the virtual landmark point immediately.

Further, in the step 1, when the navigation features are judged, it is required to judge whether the pedestrian walks along a straight line, and the judgment is performed by using the track information of the pedestrian in combination with hough transform.

Further, the feasibility test in step 2 is based on: and if the pedestrian track passes through/passes through the impassable area, the current track is considered as an impassable track, and otherwise, the current track is considered as a feasible track.

Further, the correction of the current track in step 4 includes the following two parts:

firstly, screening n feasible roads/areas for travelers to walk based on a map topological relation, wherein n is more than or equal to 1;

then, the correlation degree between the pedestrian track and the n feasible roads/areas is calculated respectively.

Further, the method for calculating the degree of correlation between the pedestrian track and the feasible road/area comprises the following steps:

defining the included angle between the direction of the feasible road/area a and the current heading of the pedestrian as theta_a(0°≤θ_aLess than or equal to 180 degrees), the projection distance from the current position of the pedestrian to the feasible road/area a is r_aThen the matching degree rho of the feasible road/area a and the pedestrian_aComprises the following steps:

ρ_a＝ω_rr_a+ω_θθ_a

wherein, ω is_rAnd ω_θThe weights of the distance and the direction included angle are respectively.

The principle of the invention comprises two parts, which are respectively: 1. the principle of correcting the position of the pedestrian by the virtual road sign is as follows: the virtual landmark point refers to a position point with unique navigation characteristics in the whole map range, and accurate position correction can be performed by utilizing the characteristic that the navigation characteristics of the position point have uniqueness (when the unique navigation characteristics are detected, a pedestrian is necessarily located at the virtual landmark point); 2. the principle of map online correction of pedestrian tracks is as follows: judging whether the current track needs to be corrected or not by judging the feasibility of the current track of the pedestrian (whether the pedestrian passes through or passes through an incommunicable area on a map) on line, introducing Hough transform to detect the regularity of the track of the pedestrian when the correction is needed, and correcting the track when the track of the pedestrian is relatively regular to avoid the condition of error correction. On the basis, the correction mode of the pedestrian track adopts a weighted projection mode based on the topological relation of the map, and the road section/area with the highest matching degree is used as the correct track/road section of the pedestrian by calculating the matching degree of the current pedestrian track and each road section/area in the map, so that the current pedestrian track is accurately corrected according to the correct track.

Compared with the prior art, the invention has the beneficial effects that:

(1) the method and the system do not adopt the traditional PF framework any more, but realize the effective fusion of the pedestrian navigation information and the map information through 'feasibility analysis of the pedestrian track, Hough transformation and weighted projection based on the map topological relation', and compared with the traditional PF framework scheme, the scheme effectively reduces the calculated amount of the algorithm while ensuring the navigation precision. In addition, the method also effectively solves the problem of particle divergence when the pedestrian turns under the traditional PF framework, and ensures the navigation precision of the pedestrian when the pedestrian turns;

(2) the invention introduces the concept of virtual road signs, and the explanation of the virtual road signs in the patent is as follows: the landmark has a unique navigation feature within the full map range, and when the unique navigation feature is detected, the position of the pedestrian can be determined to the position of the virtual landmark at once. The virtual road signs can be accurately obtained on the basis of fully mining the indoor map information, essentially, the virtual road signs are obtained by secondary mining of the map information, and a batch of position points with unique navigation features on the map are defined by using the properties of connectivity, non-leability and the like of the indoor map. When the pedestrian detects the unique navigation feature during walking, the pedestrian can be considered to be necessarily located at the corresponding virtual road sign, and therefore the position of the pedestrian is corrected. The virtual road signs fully excavate valuable information of an indoor map, realize accurate correction of indoor pedestrian positions under the condition of not increasing any hardware, effectively improve the pedestrian navigation positioning precision and inhibit the divergence of long-time pedestrian navigation errors;

(3) the fusion scheme of indoor map information and pedestrian navigation information designed by the invention does not depend on a PF (particle filter) framework any more, but is a combined mode of feasibility analysis of pedestrian tracks, Hough transformation and weighted projection based on map topological relations, wherein the feasibility analysis of the pedestrian tracks mainly judges whether the current tracks of pedestrians are reasonable or not and are matched with a map; the application of Hough transform mainly judges the regularity of the track of the pedestrian, and avoids the problem of mismatching possibly caused by correcting the track under the condition that the track of the pedestrian is irregular; the weighted projection based on the map topological relation is a correction method for the track which is not feasible for the pedestrian, the track/area with the highest matching degree is taken as the correct track/section of the pedestrian by calculating the matching degree of the track of the current pedestrian and each section/area in the map, and the track of the current pedestrian is corrected accurately according to the correct track. The framework can effectively reduce the calculation amount of the algorithm on the premise of ensuring the navigation precision, and simultaneously, the problem that the traditional PF framework diverges when turning is avoided.

Drawings

Fig. 1 is a flow chart of a pedestrian indoor navigation method based on indoor map and virtual road sign assistance according to the invention;

FIG. 2 is an indoor map containing virtual waypoints;

FIG. 3 is a result diagram of an example of a straight line detection performed using Hough transform;

FIG. 4 is a flow chart of a pedestrian track correction scheme based on virtual waypoint detection;

FIG. 5 is a schematic diagram of a feasible track and an impossible track of a pedestrian;

FIG. 6 is a schematic view of a pedestrian track to be corrected (infeasible);

FIG. 7 is a schematic diagram of a track correction result obtained by a weighted projection method based on a map topology relationship;

FIG. 8 is a diagram illustrating results of an example of a weighted projection method based on map topology.

Detailed Description

The invention is described in detail below by way of example with reference to the accompanying drawings.

As shown in the overall flow chart of the algorithm in fig. 1, the present embodiment provides a pedestrian indoor navigation method based on assistance of an indoor map and a virtual road sign, which includes the following steps:

step 1: and carrying out Pedestrian gait Reckoning (PDR) according to the MEMS gyroscope and the MEMS accelerometer to obtain Pedestrian track information for a period of time, firstly judging whether the track contains navigation characteristics of a certain virtual road mark point, if so, correcting the track by using the corresponding virtual road mark point, and simultaneously setting the correction identification position to be 0.

It should be noted that, the PDR algorithm in the present invention is not within the scope of the present invention, and the present invention only uses the result provided by the PDR algorithm and corrects the PDR result to achieve the purpose of improving the accuracy of the PDR algorithm, and the PDR algorithm is equivalent to providing the original data for the specific implementation of the present invention. The core function of the PDR is to convert the MEMS gyroscope and MEMS accelerometer into the heading information of the pedestrian and the step length information of the pedestrian, and the PDR algorithm will not be described in detail here.

On the basis of acquiring track information of a pedestrian for a period of time, firstly, detecting whether the walking characteristics of the pedestrian in the track are consistent with the navigation characteristics of a virtual landmark point in a map, and if so, correcting the track by using the virtual landmark point. The selection and correction of the virtual waypoints are described by taking the indoor map shown in fig. 2 as an example. As can be seen from fig. 2, if the pedestrian continues to walk along the road a according to the arrow shown in the figure, the pedestrian must turn to the road b according to the left direction shown in the figure or turn around to walk along the road a in the reverse direction, otherwise the pedestrian cannot continue to walk. That is to say, the walking mode at the intersection of the road a and the road b has strong characteristics, and the pedestrian can only make two choices: turning to 90 degrees to the road b to continue walking or turning to 180 degrees to walk along the road a in the reverse direction, and in the two options, due to the existence of the road constraint, only the walking option of turning to 90 degrees to continue walking on the road b can be completed at the intersection point of the road a and the road b, and by combining the analysis, the following conclusion can be reached: if the pedestrian is on the road a, walks straight down along the road a, turns 90 degrees, and continues to walk straight for a period of time, the position where the pedestrian turns 90 degrees is definitely the intersection point of the road a and the road b. That is, if the above navigation features occur, at the time when the pedestrian turns 90 degrees, the pedestrian must be located at the intersection of the road a and the road b, that is, the intersection of the road a and the road b is a virtual landmark point, and the unique navigation features of the virtual landmark point are: the pedestrian firstly lies on the road a and walks straight down along the road a, then turns 90 degrees and continues to walk straight for a period of time. If the walking mode of the pedestrian meets the navigation characteristics, the position of the pedestrian is supposed to be at the virtual road sign at the intersection point of the road a and the road b at the moment that the pedestrian turns 90 degrees, at this moment, the position information of the virtual road sign point can be used for correcting the pedestrian track information, and meanwhile, the correction identification position is set to be 0 (the pedestrian track is supposed not to need to be corrected any more).

In the determination of the navigation feature, it is necessary to determine whether the pedestrian walks along a straight line, and considering that the pedestrian walks with a certain arbitrariness, for example: although sometimes the pedestrian turns (the gyroscope senses the angle change information), the pedestrian does not walk, but turns on the spot. Therefore, it is no longer reliable to use only the information of the gyroscope to judge whether the pedestrian walks in a straight line. The method utilizes the track information of the pedestrian and Hough transformation to detect the straight walking of the pedestrian. Hough transform is a feature detection method widely used in image analysis, computer vision, and digital image processing. The hough transform is used to identify features in the found object, such as: a line. The specific algorithm flow is not described in detail here. The main function of the hough transform is to detect straight lines from the image. In the present invention, the property of the straight line can be detected by using hough transform, that is: for an input set of two-dimensional position variables (track information of a pedestrian), hough transform can identify whether the set of data is a straight line or not. Fig. 3 shows a result diagram of an example of operation of detecting straight lines by hough transform. In fig. 3, the black dots are the track positions of the pedestrians at each step, and as can be seen from the figure, the pedestrian walking track in the left walking track 1 is recognized as a straight line (black line segment) by hough transform, while the pedestrian walking track in the right walking track 2 is not recognized as a straight line by hough transform, and the hough transform recognition result coincides with the pedestrian track.

By combining the above analysis, a pedestrian track correction scheme based on virtual landmark point detection can be obtained, and a flow chart of the scheme is shown in fig. 4.

Step 2: inquiring whether the correction identification bit is 1, if so, directly executing the step 3; if the track information is 0, performing feasibility detection on the pedestrian track information, if the detection result is that the track is not feasible, setting the correction identification position to be 1, and then executing the step 3; if the detection result is that the track is feasible, the calculation of the current round is ended, and the next section of pedestrian track is waited (returning to the step 1).

In the step, firstly, whether the pedestrian track needs to be corrected or not is inquired (realized by inquiring a correction identification position), and if the pedestrian track needs to be corrected, the step 3 is directly executed; if the correction is not needed, the feasibility of the current pedestrian track is detected according to the following steps: and if the pedestrian track passes through/passes through the impassable area, the current track is considered as an impassable track, and otherwise, the current track is considered as a feasible track. The schematic diagram of the feasible track and the non-feasible track is shown in fig. 5. If the detection result is that the track is not available, setting the correction identification position to be 1 (indicating that the track of the pedestrian needs to be corrected), and then executing the step 3; if the detection result is a feasible track, the current track is correct (the track information does not conflict with the map information), no further operation is needed, the operation of the current round can be finished, and the next section of pedestrian track is waited (the step 1 is returned).

And step 3: and (3) carrying out Hough transformation on the pedestrian track information, judging whether the track is regular and correctable (can be approximate to a straight line), if so, executing the step 4, otherwise, ending the operation of the current round, and waiting for the next section of pedestrian track (returning to the step 1).

And 3, mainly carrying out track straight line detection (regularity detection) on the pedestrian. At this time, the track of the pedestrian is already the wrong track (the correction identification position is 1), the track of the pedestrian needs to be corrected, but in order to ensure the accuracy and the success rate of the correction, the track correction is performed when the track of the pedestrian is relatively regular. The regularity detection mode adopted here is to detect whether the pedestrian track is a straight line, the adopted method is to detect whether the pedestrian track is a straight line by using Hough transform, the introduction of Hough transform is the same as step 1, and the detailed description is omitted here. If the current detection result is that the pedestrian track is irregular (not straight), the track correction is not carried out, but the correction identification position is maintained to be 1, meanwhile, the current round of operation is finished, and the pedestrian track correction is carried out when the pedestrian track meeting the requirements appears (the step 1 is returned); if the current detection result is the pedestrian track rule (is a straight line), the step 4 is further executed.

And 4, step 4: and performing weighted projection based on the map topological relation on the pedestrian track information, projecting the pedestrian track needing to be corrected to a feasible area/road section, finishing the correction of the current track, and setting the correction identification position to be 0. And finishing the operation of the current round, and waiting for the next section of pedestrian track (returning to the step 1).

At the moment, the track state of the pedestrian needs to be corrected (the correction identification position is 1), and the track is more regular (the track regularity inspection is passed), the current pedestrian track is corrected by utilizing a weighted projection mode based on the map topological relation. The specific operation method is as follows, and for convenience of description, it is assumed that the track to be corrected and the indoor map of the pedestrian are as shown in fig. 6.

The correction algorithm comprises the following two parts: 1. road/area screening based on map topological relation; 2. and (4) a weighted projection correction algorithm. First, the first part is introduced:

consider an indoor map as shown in fig. 6. The pedestrian is currently located on the road a, and then the pedestrian only appears on the roads a, b or c, but not on the other roads in the next period of time, considering that the walking speed of the pedestrian is limited, and the road topology structure can be expressed by the following road topology matrix:

in the matrix G, G (1,2) ═ 1 represents that the road a and the road b are directly connected, G (1,4) ═ 0 represents that the road a and the road d cannot be directly reached, and by using the road topological relation, the correction range of the pedestrian track can be screened, as shown in fig. 6, at this time, the pedestrian enters an infeasible area and needs to be corrected, and the pedestrian walking route is relatively regular and can be corrected, and the road topological relation can show that the feasible area where the pedestrian walks can only be the road a, the road b and the road c, and other roads are excluded due to the limitation of the road topology.

On the basis of a road topology screening result, a weighted projection algorithm is further introduced:

for the pedestrian path situation shown in fig. 6, it is only possible to project the pedestrian path to one of the roads a, b or c through the analysis of the previous step. The correlation between the pedestrian track and the roads a, b, c is calculated as follows, taking the road a as an example:

defining the direction of the road a and the current course of the pedestrian (the current course of the pedestrian is the track of the pedestrian)Best fit angle obtained by hough transform) is theta_a(0°≤θ_aNot more than 180 degrees, the projection distance from the current position of the pedestrian to the road a is r_aThe degree of matching rho between the road a and the pedestrian_aIs (ρ)_aInversely correlated with degree of match):

ρ_a＝ω_rr_a+ω_θθ_a

wherein, ω is_rAnd ω_θThe weights of the distance and the direction included angle are respectively.

After the correlation degrees between the current track of the pedestrian and the roads a, b and c are sequentially calculated, the minimum rho is selected_aFor the corresponding road, the calculation result indicates that the matching degree of the road a is the highest for the pedestrian track situation shown in fig. 6, and then the current track of the pedestrian is projected onto the road a, so that the pedestrian track correction is realized. Fig. 7 is a schematic diagram of a track correction result obtained by the above-described weighted projection method based on the map topological relation for the pedestrian track situation shown in fig. 6. The result of the example of the operation of the weighted projection method based on the topological relation of the map is shown in fig. 8. In the figure, the black part is a wall (impassable area); the white part is a passable area; the circle points represent pedestrian PDR tracks; the square frame represents the pedestrian track corrected by projection; the dashed arrow represents the true path of the pedestrian. The pedestrian track represented by the circle enters the impassable area (black part) in the navigation process, and the correction algorithm based on the indoor map corrects the wrong pedestrian track to the correct track position in a projection mode (the block point represents that the projection correction is carried out at the point), so that the operation example effectively shows that the algorithm can accurately correct the wrong track.

And when the pedestrian track correction is finished, resetting the correction identification position to be 0, representing that the pedestrian track does not need to be corrected, ending the calculation of the current round, and waiting for the next section of pedestrian track (returning to the step 1).