CN115406435A - Indoor electronic map construction method and device based on WLAN and MEMS and storage medium - Google Patents
Indoor electronic map construction method and device based on WLAN and MEMS and storage medium Download PDFInfo
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Abstract
The invention relates to a method, a device and a storage medium for constructing an indoor electronic map based on WLAN and MEMS, wherein the method comprises the following steps: step 1) in an off-line stage, based on a dynamic field mapping mode, utilizing an MEMS sensor of an intelligent mobile terminal to obtain motion auxiliary data, utilizing a WLAN sensor to obtain RSSI fingerprint measurement, and based on the motion auxiliary data, generating a motion track by adopting a pedestrian dead reckoning algorithm; step 2) calibrating the motion track based on the indoor landmarks, wherein the indoor landmarks comprise corridor corners and stairways between floors; and 3) matching the three-dimensional coordinates of the sampling points in the calibrated motion track and the RSSI fingerprints through the synchronous time stamps to obtain a complete indoor electronic map containing a plurality of floors. Compared with the prior art, the method has the advantages that the electronic map is built by utilizing the low-cost sensor, the built track precision is high, and the like.
Description
Technical Field
The invention relates to an electronic map construction method, in particular to an indoor electronic map construction method, device and storage medium based on WLAN and MEMS.
Background
Due to the wide application of mobile intelligent terminals and the rapid popularization and mass application of wireless networks, the application demand Based on Location Based Services (LBS) is on a rapid and greatly increased trend, and the LBS is rapidly developed and popularized to various fields of social life and production. Among them, reliable and efficient positioning technology is the premise and key point for implementing LBS.
In the outdoors, a Global Navigation Satellite System (GNSS) is used in various fields requiring information of positioning services. The GNSS generation and development basically solve the problem of positioning in outdoor open space, and is widely applied in the fields of military affairs, traffic, resource environment, agriculture, animal husbandry, fishery, mapping and the like and in daily life of people; although this technique works well in outdoor applications, the performance of GNSS based positioning systems is not satisfactory in indoor applications. Therefore, in an indoor environment, research of specialized positioning methods and techniques is an inevitable trend of the development of the current LBS applications.
In the 90's of the 20 th century, a high-speed wireless network communication technology, namely Wireless Local Area Network (WLAN), began to develop rapidly. The WLAN has the characteristics of high communication speed and convenience in deployment, so that the WLAN has wide application potential and prospect in the field of indoor positioning. Among the general WLAN-based indoor positioning methods, the Received Signal Strength Indication (RSSI) -based fingerprinting is the most interesting method with its high accuracy and low cost. Location based on RSSI fingerprints is typically performed in two phases. In the off-line phase, field mapping is performed, collecting Received Signal Strength Indicator (RSSI) vectors from different Access Points (APs) of known location, which are called Reference Points (RPs). These vectors of RSSI form a fingerprint for each station and are stored in a database, otherwise known as a Radio Map (RM). Thus, the offline phase is a database building process. In the online phase, the user or target measures the RSSI vector at its location and reports it to the server, which estimates and returns the user's location.
An important drawback that hinders large-scale deployment of existing indoor positioning systems is the time and labor consuming nature of the field survey. When the electronic map is constructed in an off-line stage, special field mapping needs to be carried out on the reference points, the positions and the signal intensity of the reference points need to be manually measured, and the positions and the signal intensity need to be matched to form position fingerprints. Furthermore, location dependent radio maps need to be updated regularly to adapt to changes in the environment, which significantly burdens the location system. Taking an airport as an example, for a survey area of 8000 square meters, if 2 meters are selected as the grid size for the site survey, there are still 2000 reference points to be surveyed on site. Therefore, reducing the in-situ measurement density or considering the trade-off between cost and accuracy is an important issue for current fingerprint localization.
Disclosure of Invention
The invention aims to provide an indoor electronic map construction method, device and storage medium based on WLAN and MEMS, which adopts a low-cost sensor to realize high-precision electronic map construction, thereby reducing cost and ensuring higher track precision.
The purpose of the invention can be realized by the following technical scheme:
an indoor electronic map construction method based on WLAN and MEMS comprises the following steps:
step 1) in an off-line stage, based on a dynamic field mapping mode, utilizing an MEMS sensor of an intelligent mobile terminal to obtain motion auxiliary data, utilizing a WLAN sensor to obtain RSSI fingerprint measurement, and based on the motion auxiliary data, generating a motion track by adopting a pedestrian dead reckoning algorithm;
step 2) calibrating the motion track based on the indoor landmarks, wherein the indoor landmarks comprise corridor corners and stairways between floors;
and 3) matching the three-dimensional coordinates of the sampling points in the calibrated motion track and the RSSI fingerprints through the synchronous time stamps to obtain a complete indoor electronic map containing a plurality of floors.
The step 1) comprises the following steps:
step 1-1) obtaining a starting signal for a pedestrian to start walking, starting an MEMS sensor and a WLAN sensor of an intelligent mobile terminal based on the starting signal, and entering a step counting state, wherein the MEMS sensor comprises an acceleration sensor, a gyroscope and a magnetic sensor;
step 1-2) dividing an indoor space into a plurality of areas based on indoor landmarks, wherein the areas include a floor level and a stair area of each floor;
step 1-3) obtaining RSSI fingerprint measurement output by a WLAN sensor;
step 1-4) acquiring acceleration data output by an acceleration sensor, wherein the acceleration data are periodic data;
step 1-5) estimating the number of steps of walking based on the acceleration data and a pre-configured threshold condition;
step 1-6) estimating the step length of walking based on the acceleration data and the dynamic model;
step 1-7) acquiring the outputs of a gyroscope and a magnetic sensor and estimating a walking direction value;
step 1-8) determining the position coordinate of the next moment by combining the step length, the direction value and the step number of each step based on the initial position coordinate of walking for each area;
step 1-9) repeating the steps 1-3) -1-8) until a stop signal of stopping walking of the pedestrian is received, turning off the MEMS sensor and the WLAN sensor, and ending the step counting state;
and 1-10) connecting the position coordinates of each time in each area to generate a walking track of each area.
The step 1-5) comprises the following steps:
step 1-5-1) carrying out low-pass filtering on the triaxial data of the acceleration data to separate out the components of gravity on each axis;
step 1-5-2) eliminating gravity interference based on high-pass filtering to obtain actual acceleration data;
step 1-5-3) determining the overall acceleration based on the actual acceleration data:
wherein, (a (t) x ,a(t) y ,a(t) z ) A (t) is the overall acceleration, which is the triaxial data of the actual acceleration;
and 1-5-4) judging whether the integral acceleration of the current step meets a preset threshold condition, and if so, accumulating the step to count the number of steps.
The threshold condition comprises that the acceleration of the wave crest and the wave trough of the integral acceleration is larger than a first threshold, the time interval of two adjacent wave crests is larger than a second threshold, and the time of a complete walking cycle is larger than a third threshold, wherein the complete walking cycle comprises foot lifting, striding and landing.
And 1-6) obtaining the relation between the step length and the acceleration data by establishing the relation between the analysis step length of the human body walking dynamic model and the body displacement in each walking cycle, thereby calculating the dynamic change of the step length based on the acceleration data.
The steps 1-7) comprise the following steps:
step 1-7-1) obtaining a first direction value output by magnetometer calculation;
step 1-7-2) acquiring an angular velocity value output by a gyroscope, and integrating the angular velocity value to obtain a second direction value;
and 1-7-3) fusing the first direction value and the second direction value to obtain a direction value.
The calculation method for determining the position coordinate at the next moment in the steps 1-8) comprises the following steps:
wherein (x) t ,y t ,z t ) (x) is the current coordinate of the user t+Δt ,y t+Δt ,z t+Δt ) Is the coordinate of the next moment, n represents the number of steps in the time of delta t, l is the step size, theta is the direction value, z k Is the coordinate height of the initial floor k of the stair area, z k+1 Is the coordinate height of the next floor k + 1.
The step 2) comprises the following steps:
step 2-1) presumes that the motion track in one area consists of n sampling points (p) 1 ,p 2 ,…,p n ) Wherein the position of each sampling point corresponds to a three-dimensional coordinate, the first sampling point p 1 And the last sampling point p n Is known, let p be 1 And p n Setting as a calibration point;
step 2-2) obtaining p calculated based on pedestrian dead reckoning n Observing coordinates of points by comparing p n The observed coordinates and the real coordinates of the points are used for obtaining the closing difference f of the x, y and z coordinates x ,f y ,f z ：
Wherein (x) n ,y n ,z n ) Is p n The observed coordinates of the point or points are,
is p n The true coordinates of the points;
step 2-3) utilizing the closure difference f x ,f y ,f z Determining a coordinate correction value:
wherein D is selected from p 1 Point to p n Full length of the locus of points, D i-1 Is the length of the side of the track before the ith point, v xi ，v yi ，v zi Is a coordinate correction value;
step 2-4) coordinates are corrected based on the coordinate correction value, and calibration is completed:
x i ′＝x i +v xi
y i ′＝y i +v yi
z i ′＝z i +v zi
wherein, 9x i ,y i ,z i ) To correct the three-dimensional coordinates of the ith point, (x) i ′,y i ′,z i ') is the three-dimensional coordinates of the corrected ith point.
An indoor electronic mapping device based on WLAN and MEMS comprises a memory, a processor and a program stored in the memory, wherein the processor executes the program to realize the method.
A storage medium having a program stored thereon, which when executed performs the method as described above.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides an indoor electronic map construction method based on information fusion of a WLAN sensor and an MEMS sensor, which is different from the conventional heavy-duty static field measurement mode.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a floor map of an indoor environment in an embodiment where (a) is a first floor, (b) is a second floor, and (c) is a third floor;
FIG. 3 is a schematic view of acceleration triaxial data;
FIG. 4 is a graphical depiction of the trajectory of multiple zones prior to calibration in an embodiment wherein (a) is a first floor, (b) is a second floor, and (c) is a third floor;
FIG. 5 is a plot of calibrated multiple zones for an embodiment where (a) is a first floor, (b) is a second floor, and (c) is a third floor;
FIG. 6 is a diagram illustrating merged tracks for multiple regions, in accordance with an embodiment;
FIG. 7 is a schematic electronic map of various areas in an embodiment where (a) is a first floor, (b) is a second floor, and (c) is a third floor;
fig. 8 is a schematic diagram of a complete indoor electronic map according to an embodiment.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
This example was tested using a multi-story building at the university of Jaume I, with experimental data provided by the IPIN2019 indoor location competition collected at the CNR (pizza, italy) facility, with an indoor environment floor map of the building as shown in FIG. 2.
An indoor electronic map construction method based on WLAN and MEMS, as shown in fig. 1, includes the following steps:
step 1) in an off-line stage, based on a dynamic field mapping mode, an MEMS sensor of an intelligent mobile terminal is used for obtaining motion auxiliary data, an RSSI fingerprint measurement is obtained through a WLAN sensor, and based on the motion auxiliary data, a Pedestrian Dead Reckoning (PDR) is adopted for generating a motion track.
Step 1-1) obtaining a starting signal for a pedestrian to start walking, starting an MEMS sensor and a WLAN sensor of an intelligent mobile terminal based on the starting signal, and entering a step counting state, wherein the MEMS sensor comprises an acceleration sensor, a gyroscope and a magnetic sensor;
step 1-2) dividing an indoor space into a plurality of areas based on indoor landmarks, wherein the areas comprise floor planes and stair areas of each floor;
in this embodiment, data acquisition of a motion trajectory starts at one calibration point and ends at another calibration point, which is determined based on indoor landmarks.
Step 1-3) obtaining RSSI fingerprint measurement output by a WLAN sensor;
step 1-4) acquiring acceleration data output by an acceleration sensor, wherein the acceleration data are periodic data, and are shown in FIG. 3;
step 1-5) estimating the number of steps of walking based on the acceleration data and a pre-configured threshold condition;
the walking gait of the pedestrian comprises lifting, striding and landing, the acceleration in each direction is represented as the alternate transformation of wave crest and wave trough curves, and the acceleration has certain regularity and periodicity. The acceleration data is shown in fig. 3. Therefore, the detection of the number of steps can be realized by using an acceleration sensor. The original output data of the mobile phone MEMS acceleration sensor contains the gravity component of the earth, and the gravity acceleration component caused by different mobile phone placing posture differences is not fixed, so that the acceleration on each axis loses the original regularity, and therefore the gravity component needs to be removed from each axis of the acceleration data before step detection. The gravity changes only when the equipment rotates, and belongs to a low-frequency signal. Therefore, the gravity interference can be eliminated through the following steps, and the actual acceleration value is obtained.
Step 1-5-1) carrying out low-pass filtering on the triaxial data of the acceleration data to separate out the components of gravity on each axis;
step 1-5-2) eliminating gravity interference based on high-pass filtering to obtain actual acceleration data;
step 1-5-3) it can be seen from fig. 3 that the waveforms output by the acceleration sensor in three axes all have a certain periodicity, but none are very obvious, so that the overall acceleration is determined based on the actual acceleration data, and the analysis is performed with the overall acceleration as a sample:
wherein, (a (t) x ,a(t) y ,a(t) z ) A (t) is the three-axis data of the actual acceleration, and a (t) is the integral acceleration;
and 1-5-4) judging whether the integral acceleration of the current step meets all threshold conditions, and if so, accumulating the step to count the number of steps.
The threshold condition comprises that the acceleration of the wave crest and the wave trough of the integral acceleration is larger than a first threshold, the time interval of two adjacent wave crests is larger than a second threshold, and the time of a complete walking cycle is larger than a third threshold, wherein the complete walking cycle comprises foot lifting, striding and landing.
Step 1-6) estimating the step length of walking based on the acceleration data and the dynamic model;
the step size is typically estimated using a static model or a dynamic model as the pedestrian progresses. The static model fixes the step length, divides the step length into a plurality of grades according to the sex, the height and the walking state of the human body, and obtains the corresponding step length value according to the actual condition of the pedestrian, or directly takes the step length obtained in the training mode as the step length of the pedestrian in actual walking. The step length estimation method is simple and reliable under the condition that the pedestrian walks at a constant speed, but the step length estimation method is more accurate by using a dynamic model in consideration of the gait complexity and the variable speed movement condition of the human body during walking. The invention obtains the relation between the step length and the acceleration data by establishing the relation between the human body walking dynamic model analysis step length and the body displacement in each walking period, thereby calculating the dynamic change of the step length based on the acceleration data.
Step 1-7) acquiring the outputs of a gyroscope and a magnetic sensor and estimating a walking direction value;
after the pedestrian is judged to finish the walking step and the step length of the step is estimated, the PDR algorithm can be finished only by knowing the motion direction of the pedestrian. In view of the sensor characteristics described above, the orientation data calculated by the magnetometer remains stable over time. However, when there is strong magnetic interference in the indoor environment, the magnetometer data will be severely distorted, resulting in a certain degree of azimuth deviation. The gyroscope is not affected by magnetic interference, and accurate azimuth data can be obtained by integrating the angular velocity of the output in a short time. However, errors due to self-data drift accumulate over time, and the resulting orientation is the position relative to the initial orientation. Therefore, the invention integrates the magnetometer and the gyroscope to jointly estimate the direction of the pedestrian, thereby reducing the influence caused by magnetic interference signals in the room and drift errors of the gyroscope and obtaining more reliable direction angle information.
Step 1-7-1) acquiring a first direction value calculated and output by a magnetometer;
step 1-7-2) acquiring an angular velocity value output by a gyroscope, and integrating the angular velocity value to obtain a second direction value;
and 1-7-3) fusing the first direction value and the second direction value to obtain a direction value.
Step 1-8) determining the position coordinate of the next moment by combining the step length, the direction value and the step number of each step based on the initial position coordinate of walking for each area:
wherein (x) t ,y t ,z t ) As the current coordinates of the user, (x) t+Δt ,y t+Δt ,z t+Δt ) Is the coordinate of the next time, n represents the number of steps in the time of delta t, l is the step size, theta is the direction value, z k Is the coordinate height of the starting floor k of the stair area, z k+1 Is the coordinate height of the next floor k + 1.
Step 1-9) repeating the steps 1-3) -1-8) until a stop signal of stopping walking of the pedestrian is received, turning off the MEMS sensor and the WLAN sensor, and ending the step counting state;
and 1-10) connecting the position coordinates of each time in each area to generate a walking track of each area.
The motion trajectories of the plurality of regions obtained based on the above steps are shown in fig. 4.
And 2) calibrating the motion track based on the indoor landmarks, wherein the indoor landmarks comprise corridor corners and stairways between floors.
Step 2-1) supposing that the motion track in one area consists of n sampling points (p) 1 ,p 2 ,…,p n ) Wherein the position of each sampling point corresponds to a three-dimensional coordinate, the first sampling point p 1 And the last sampling point p n Is known, let p be 1 And p n Setting as a calibration point;
step 2-2) obtaining p calculated based on pedestrian dead reckoning n Observing coordinates of points by comparing p n The observed coordinates and the real coordinates of the points are used for obtaining the closing difference f of the x, y and z coordinates x ,f y ,f z ：
Wherein, 9x n ,y n ,z n ) Is p n The observed coordinates of the point or points are,
is p n The true coordinates of the points;
step 2-3) utilizing the closure difference f x ,f y ,f z Determining a coordinate correction value:
wherein D is selected from p 1 Point to p n Full length of the locus of points, D i-1 Is the length of the side of the track before the ith point, v xi ，v yi ，v zi Is a coordinate correction value;
step 2-4) coordinates are corrected based on the coordinate correction value, and calibration is completed:
x i ′＝x i +v xi
y i ′＝y i +v yi
z i ′＝z i +v zi
wherein (x) i ,y i ,z i ) Is composed ofThree-dimensional coordinates of the ith point before correction, (x) i ′,y i ′,z i ') is the three-dimensional coordinates of the corrected ith point.
The calibrated trajectory diagram is shown in fig. 5, and compared with fig. 4, the invention can greatly improve the accuracy of the trajectory based on the constraint of the indoor landmark.
The schematic diagram of the overall trajectory obtained by drawing the area trajectories for all the data in the training set and combining the area trajectories is shown in fig. 6, where in fig. 6, the dashed line represents the trajectory of the stair area, and the solid line represents the trajectory of the floor plane.
And 3) matching the three-dimensional coordinates of the sampling points in the calibrated motion track and the RSSI fingerprints through the synchronous time stamps to obtain a complete indoor electronic map containing a plurality of floors.
Each received WiFi fingerprint is bound with a position coordinate in the track according to a timestamp to serve as a Reference Point (RP), an electronic map corresponding to each floor is obtained as shown in fig. 7, and a complete indoor electronic map containing multiple floors is shown in fig. 8. All reference points in the figure are evenly distributed in the corridor of the building.
The above functions, if implemented in the form of software functional units and sold or used as a separate product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions that can be obtained by a person skilled in the art through logic analysis, reasoning or limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (10)
1. An indoor electronic map construction method based on WLAN and MEMS is characterized by comprising the following steps:
step 1) in an off-line stage, based on a dynamic field mapping mode, utilizing an MEMS sensor of an intelligent mobile terminal to obtain motion auxiliary data, utilizing a WLAN sensor to obtain RSSI fingerprint measurement, and based on the motion auxiliary data, generating a motion track by adopting a pedestrian dead reckoning algorithm;
step 2) calibrating the motion track based on the indoor landmarks, wherein the indoor landmarks comprise corridor corners and stairways between floors;
and 3) matching the three-dimensional coordinates of the sampling points in the calibrated motion track and the RSSI fingerprints through the synchronous time stamps to obtain a complete indoor electronic map containing a plurality of floors.
2. An indoor electronic mapping method based on WLAN and MEMS according to claim 1, wherein said step 1) comprises the following steps:
step 1-1) obtaining a starting signal for a pedestrian to start walking, starting an MEMS sensor and a WLAN sensor of an intelligent mobile terminal based on the starting signal, and entering a step counting state, wherein the MEMS sensor comprises an acceleration sensor, a gyroscope and a magnetic sensor;
step 1-2) dividing an indoor space into a plurality of areas based on indoor landmarks, wherein the areas comprise floor planes and stair areas of each floor;
step 1-3) obtaining RSSI fingerprint measurement output by a WLAN sensor;
step 1-4) acquiring acceleration data output by an acceleration sensor, wherein the acceleration data are periodic data;
step 1-5) estimating the number of steps of walking based on the acceleration data and a preconfigured threshold condition;
step 1-6) estimating the step length of walking based on the acceleration data and the dynamic model;
step 1-7) acquiring the outputs of a gyroscope and a magnetic sensor and estimating a walking direction value;
step 1-8) determining the position coordinate of the next moment based on the initial position coordinate of walking and the step length, the direction value and the step number of each step aiming at each area;
step 1-9) repeating the steps 1-3) -1-8) until a stop signal of stopping walking of the pedestrian is received, turning off the MEMS sensor and the WLAN sensor, and ending the step counting state;
and 1-10) connecting the position coordinates of each moment in each area to generate a walking track of each area.
3. A WLAN and MEMS based indoor electronic mapping method according to claim 2, wherein the steps 1-5) include the steps of:
step 1-5-1) carrying out low-pass filtering on the triaxial data of the acceleration data to separate out the components of gravity on each axis;
step 1-5-2) eliminating gravity interference based on high-pass filtering to obtain actual acceleration data;
step 1-5-3) determining the overall acceleration based on the actual acceleration data:
wherein, (a (t) x ,a(t) y ,a(t) z ) A (t) is the overall acceleration, which is the triaxial data of the actual acceleration;
and 1-5-4) judging whether the integral acceleration of the current step meets a preset threshold condition, and if so, accumulating the step to count the number of steps.
4. The WLAN and MEMS based indoor electronic map building method of claim 3, wherein the threshold condition includes that the acceleration magnitude of the whole acceleration peak and the valley is larger than a first threshold, the time interval between two adjacent peaks is larger than a second threshold, and the time of one complete walking cycle is larger than a third threshold, wherein the complete walking cycle includes lifting foot, striding and landing.
5. The method as claimed in claim 2, wherein the step 1-6) obtains the relationship between the step size and the acceleration data by establishing the relationship between the step size and the body displacement in each walking cycle through dynamic model analysis of human walking, so as to estimate the dynamic change of the step size based on the acceleration data.
6. A WLAN and MEMS based indoor electronic mapping method according to claim 2, wherein the steps 1-7) include the steps of:
step 1-7-1) acquiring a first direction value calculated and output by a magnetometer;
step 1-7-2) obtaining an angular velocity value output by a gyroscope, and integrating the angular velocity value to obtain a second direction value;
and 1-7-3) fusing the first direction value and the second direction value to obtain a direction value.
7. An indoor electronic map construction method based on WLAN and MEMS according to claim 2, characterized in that the calculation method of determining the position coordinates of the next time in the steps 1-8) is:
wherein (x) t ,y t ,z t ) Is the current coordinates of the user, (z) t+Δt ,y t+Δt ,z t+Δt ) Is the coordinate of the next moment, n represents the number of steps in the time of delta t, l is the step size, theta is the direction value, z k Is the coordinate height of the starting floor k of the stair area, z k+1 Is the coordinate height of the next floor k + 1.
8. The WLAN and MEMS based indoor electronic mapping method of claim 1, wherein the step 2) comprises the steps of:
step 2-1) presumes that the motion track in one area consists of n sampling points (p) 1 ,p 2 ,…,p n ) The position of each sampling point corresponds to a three-dimensional coordinate, and the first sampling point p 1 And the last sampling point p n Is known, let p be 1 And p n Setting as a calibration point;
step 2-2) obtaining p calculated based on pedestrian dead reckoning n Observing coordinates of points by comparing p n The observed coordinates and the real coordinates of the points are used for obtaining the closing difference f of the x, y and z coordinates x ,f y ,f z ：
Wherein (x) n ,y n ,z n ) Is p n Observed coordinates of points，
Is p n The true coordinates of the points;
step 2-3) utilizing the closure difference f x ,f y ,f z Determining a coordinate correction value:
wherein D is selected from p 1 Point to p n Full length of locus of points, D i-1 Is the length of the side of the track before the ith point, v xi ，v yi ，v zi Is a coordinate correction value;
step 2-4) coordinates are corrected based on the coordinate correction value, and calibration is completed:
x i ′＝x i +v xi
y i ′＝y i +v yi
z i ′＝z i +v zi
wherein (x) i ,y i ,z i ) To correct the three-dimensional coordinates of the ith point, (x) i ′,y i ′,z i ') is the three-dimensional coordinates of the corrected ith point.
9. An indoor electronic mapping device based on WLAN and MEMS, comprising a memory, a processor, and a program stored in the memory, wherein the processor when executing the program implements the method according to any of claims 1-8.
10. A storage medium having a program stored thereon, wherein the program, when executed, implements the method of any of claims 1-8.
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