CN107345813B - Indoor plane graph construction method based on MT-PDR and light intensity information - Google Patents
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Abstract
The invention discloses an indoor plane graph construction method based on MT-PDR and light intensity information, which is an indoor plane graph construction method for fusing multi-line man-made dead reckoning trajectory (MT-PDR) and area division based on light intensity information and is oriented to an intelligent mobile terminal inertial sensor and a light intensity sensor, wherein for the problem of course drift of MT-PDR, course estimation is carried out by combining a quaternion of four-subsample equivalent rotation vectors and a gyroscope inflection point detection algorithm. Compared with the traditional method for constructing the indoor plane graph by adopting multiple wireless access nodes and sensor nodes and combining an indoor positioning and distance measurement algorithm, the method has the advantages of lower time complexity and algorithm complexity, and realizes the construction of the indoor plane graph in a low-cost and simple mode.
Description
Technical Field
The invention relates to an indoor plane graph construction method based on MT-PDR and light intensity information.
Background
For indoor complex layout environments, such as shopping malls, parking lots, hospitals and the like, when people buy a commodity, search for a parking space or a treatment department, the destination cannot be quickly and accurately positioned to find out the shortest path, so that blind search can be performed, and a large amount of time and energy are consumed. In order to solve the problem of the last kilometer of human beings, the indoor navigation technology is developed.
An important support tool for indoor navigation, namely an indoor plane map, has attracted attention and research in recent years, wherein how to construct the indoor plane map is still a problem to be solved. The traditional method for constructing the indoor floor plan is mostly obtained by manual measurement or building floor plan provided by developers and builders. If Google adopts a manual measurement mode in 2011 to construct an indoor plan; apple company introduced the iBeacon technology based on near field bluetooth communication in 2014, and utilized the Beacon equipment to send information such as building layout and the like to construct an indoor floor plan. Although the construction work of the indoor plane map can be realized by the above mode, the time complexity is high, the cost is high, and the indoor plane map constructed by the traditional mode is not easy to update due to the characteristic that the indoor environment layout is easy to change.
According to the advantages, in 2012, Shin and other people face the intelligent mobile terminal, a SmartS L AM method is provided, a wifi fingerprint positioning technology, a step-counting algorithm in the PDR and an electronic compass heading estimation are fused to construct an indoor plane map, in 2016, Renaudin and other people face the intelligent mobile terminal, a multi-wifi reference node is utilized to correct PDR path tracks in real time to construct the indoor plane map, in the method for constructing the indoor plane map, the infrastructure such as wifi is added to enable the layout mode to be complicated, multiple algorithms such as the PDR and the fingerprint algorithm are fused to enable the algorithm complexity to be high, and most of the construction work of the indoor corridor plane map is completed.
In the MT-PDR, the typical 'course drift' problem of the PDR exists, and in order to solve the problem, a quaternion Kalman filtering algorithm is proposed to solve the 'course drift' problem of the PDR, but the algorithm has high calculation complexity and has a strong nonlinear problem. Aiming at the problems, the invention adopts the quaternion of the equivalent rotation vector of the four subsamples with high linearity, small calculation error and non-singularity characteristics to carry out course estimation, and combines a gyroscope inflection point detection algorithm to further improve the course precision. In addition, the MT-PDR includes layout information of a room and a corridor area, but cannot obtain a layout state of each. The building lighting design standard stipulates the lighting standard value, lighting quality and lighting function rate density of residential, public and industrial buildings, and indicates that different indoor functional spaces need to be matched with different lighting intensities. Based on the characteristics of the difference of the light intensity distribution of the indoor areas, the invention provides the method for carrying out area division on the MT-PDR by using the light intensity information.
Disclosure of Invention
The invention aims to solve the technical problem of providing an indoor plane graph construction method based on MT-PDR and light intensity information so as to solve the problems of high cost, high time complexity and algorithm complexity in the conventional indoor plane graph construction method.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
an indoor plane map construction method based on MT-PDR and light intensity information is characterized by comprising the following steps:
the method comprises the following steps: the indoor data acquisition work is completed by using an inertial sensor and a light intensity sensor of the intelligent mobile terminal in a crowdsourcing-based mode;
step two: calculating three elements of step length, step frequency and course in a pedestrian dead reckoning algorithm by using the inertial sensor data obtained in the step one, and generating an MT-PDR;
step three: for the problem of 'course drift' existing in the MT-PDR, course estimation is carried out by combining a quaternion of an equivalent rotation vector of a quaternary sample and a gyro inflection point detection algorithm;
step four: according to the characteristics of indoor light intensity distribution difference, the MT-PDR is divided into regions by using the light intensity sensor data obtained in the step one, and the regions are divided into indoor rooms and corridor regions;
step five: and overlapping the MT-PDRs after the area division to construct an indoor plan.
The invention provides an indoor plane graph construction method fusing MT-PDR and carrying out region division based on light intensity information. The MT-PDR adopts a detection algorithm combining four-subsample equivalent rotation vector quaternion and gyroscope inflection points, effectively solves the typical course drift problem in the PDR, and utilizes the characteristic of indoor light intensity distribution difference to realize region division on the MT-PDR by adopting light intensity information. The construction work of the indoor plane graph is completed by adopting a simple and effective method, and compared with the existing method, the method is low in cost and has lower time complexity and algorithm complexity.
Drawings
FIG. 1 is a flow chart of an indoor floor plan construction method based on MT-PDR and light intensity information according to the present invention.
FIG. 2 is a graph of data distribution for a gyroscope of the present invention with cornering behaviour.
FIG. 3 is a flow chart of MT-PDR heading estimation of the present invention.
Fig. 4 is a schematic diagram of an indoor plan view obtained by an embodiment of the present invention and a comparison with an actual indoor layout.
Detailed Description
The present invention will be described in further detail below by way of examples with reference to the accompanying drawings, which are illustrative of the present invention and are not to be construed as limiting the present invention.
As shown in fig. 1, a method for constructing an indoor plane map based on MT-PDR and light intensity information according to the present invention comprises the following steps:
the method comprises the following steps: based on a crowdsourcing mode, the synchronous acquisition of data is realized by using an inertial sensor and a light intensity sensor of an intelligent mobile terminal, and the specific realization process is as follows,
1.1, installing data acquisition software on an intelligent mobile terminal to realize synchronous data acquisition of a three-axis acceleration sensor, a three-axis gyroscope sensor, a three-axis Orientation sensor and a light intensity sensor;
1.2 the user holds the intelligent mobile terminal to walk indoors, and the data acquisition work of the sensor is completed through data acquisition software.
Step two: calculating three elements of step length, step frequency and course in a pedestrian dead reckoning algorithm by using the inertial sensor data obtained in the step one, and generating an MT-PDR;
in the walking process of a human body, walking gait is a periodic process, according to the characteristics of statistical characteristics of an inertial sensor of an intelligent mobile terminal and the correlation of the walking gait, step frequency, step length and course direction can be obtained, the step frequency and the step length can be obtained by analyzing data of an acceleration sensor, smooth denoising processing needs to be carried out on the data of the sensor in advance in order to eliminate unpredictable errors, in order to avoid the influence of the change of the numerical value of the acceleration sensor caused by the orientation of the intelligent mobile terminal, three-axis combined acceleration data with gravity components removed is adopted in the calculation process, so that the combined acceleration data are approximately and symmetrically distributed along a coordinate y axis, the waveform fluctuates in a fixed numerical value range, and the specific implementation process of three-element calculation in MT-PDR,
2.1 for the estimation of the step length, utilizing the three-axis resultant acceleration data without the gravity component and adopting a nonlinear model to dynamically estimate the step length, wherein the formula is as follows:
wherein K is the dynamic step coefficient, S L is the calculated step value, Amax、AminRespectively representing the maximum value and the minimum value of the acceleration sensor in each step period;
representing the average value of the sequence formed by the maximum values of the acceleration in all the step frequency periods in the whole movement process; a. b and c are constant parameters obtained by training;
2.2 estimating the step frequency, utilizing the three-axis resultant acceleration data without the gravity component, adopting a peak detection algorithm to calculate the walking step frequency, and in the peak detection algorithm, defining a peak threshold value V by carrying out numerical analysis on a waveform of the resultant accelerationthresholdHas a value interval of [0.8,3 ]]Defining a step cycle T according to the frequency range of normal walking of the pedestrian as 1-2.5 HzthresholdHas a value interval of [0.4s,1s ]]When the acceleration waveform is combined, the peak values of two adjacent wave crests are at VthresholdMiddle and time interval is at TthresholdRecording as one step to finish the calculation of the step frequency;
2.3 the course of calculation of the heading, as shown in step three.
Step three: in order to solve the problem of course drift existing in MT-PDR, the invention adopts a combination of quaternion of equivalent rotation vectors of four subsamples and a gyroscope inflection point detection algorithm to carry out course estimation; for the gyro inflection point detection algorithm, fig. 2 shows the characteristics of the gyro sensor associated with the human motion state: when the data of the gyroscope sensor fluctuates in a small numerical range and changes stably, the pedestrian is in the straight walking process; when the gyroscope data has high value mutation, the gyroscope data shows that a turning image occurs in the walking process, and when the gyroscope data has negative value mutation, the gyroscope data shows that a forward turning behavior occurs in the walking process, the course value is increased, and when the gyroscope data has positive value mutation, the gyroscope data shows that a reverse turning behavior occurs in the walking process, and the course value is reduced; fig. 3 shows the calculation process of MT-PDR course track, which is implemented as,
3.1 initial attitude angle (Φ) obtained from Orientation sensor0θ0Ψ0)TFinding the initial quaternion Q0；
3.2 when detecting that the walking is one step, detecting whether the data of the Z axis of the gyroscope is mutated or not, if the data is mutated, detecting the size of a mutation numerical value, if the mutation numerical value is greater than 0, reducing the current course value by 90 degrees, otherwise, if the mutation numerical value is less than 0, increasing the current course value by 90 degrees;
3.3 if the gyroscope Z-axis data does not have a sudden change, updating the attitude quaternion Q according to the four-subsample equivalent rotation vector, wherein the formula is Q (t + h) ═ Q (t) × Δ Q, h is a time updating interval, Δ Q is an equivalent rotation vector quaternion, Q (t) is a quaternion at the time t, Q (t + h) is a quaternion updated at the time t + h, and the course updating is realized by using the updated quaternion Q (t + h);
wherein T is a quaternion matrix (phi theta psi) obtained from quaternion Q (T + h)TIs the new attitude angle obtained by the quaternion matrix T calculation.
Step four: in order to realize the regional division of rooms and corridors in the MT-PDR, the invention provides a regional division method based on light intensity information; under the influence of factors such as weather, light and the like, the light intensity values of indoor office and study areas fluctuate in a large range, the light intensity value of a corridor area changes stably, but the general trend shows that the light intensity values of the indoor office and study room areas are generally higher than the light intensity values of the corridor area, the light intensity distribution difference is obvious, based on the characteristics, the area division of MT-PDR is realized by utilizing indoor light intensity information, the specific realization process is that,
4.1 all the light intensity data in MT-PDR are processed by min-max standard normalization, and a light intensity threshold L is setthreshold；
4.2 when the light intensity is at [0, L ]threshold]When the interval is within the range of [ L ], dividing the corresponding MT-PDR track into corridor areas and expressing the corridor areas by straight lines, otherwise, when the light intensity is within the range of [ L ]threshold，1]When the interval is within (2), the track is divided into room areas and indicated by dotted lines, thereby completing the area division of the MT-PDR.
Step five: the MT-PDRs for realizing the area division are mutually superposed to construct an indoor plan, and fig. 4 shows the indoor plan constructed by the present invention and a comparison result with an actual indoor layout, so that it can be seen that the matching rate is higher by comparing the indoor plan constructed by the MT-PDRs for completing the area division with the actual indoor layout, and the area division of the room and the corridor in the constructed plan is realized, thereby effectively completing the construction work of the indoor plan.
The above description of the present invention is intended to be illustrative. Various modifications, additions and substitutions for the specific embodiments described may be made by those skilled in the art without departing from the scope of the invention as defined in the accompanying claims.
Claims (3)
1. An indoor plane map construction method based on MT-PDR and light intensity information is characterized by comprising the following steps:
the method comprises the following steps: the indoor data acquisition work is completed by using an inertial sensor and a light intensity sensor of the intelligent mobile terminal in a crowdsourcing-based mode;
step two: calculating three elements of step length, step frequency and course in a pedestrian dead reckoning algorithm by using the inertial sensor data obtained in the step one, and generating an MT-PDR;
step three: for the problem of 'course drift' existing in the MT-PDR, course estimation is carried out by combining a quaternion of an equivalent rotation vector of a quaternary sample and a gyro inflection point detection algorithm;
in the third step, in order to solve the problem of 'course drift' existing in MT-PDR, course estimation is realized by combining quaternion of four-subsample equivalent rotation vector and gyroscope inflection point detection algorithm,
3.1 obtaining an initial attitude angle (Φ) from an Orientation sensor0θ0Ψ0)TAnd find the initial quaternion Q0；
3.2 when detecting that walking is carried out by one step, detecting whether data of a Z axis of the gyroscope is mutated or not, if the data is mutated, detecting the size of a mutation numerical value, and when the mutation numerical value is greater than 0, reducing the current course value by 90 degrees, otherwise, when the mutation numerical value is less than 0, increasing the current course value by 90 degrees;
3.3 if the Z-axis data of the gyroscope does not have sudden change, updating the quaternion Q according to the equivalent rotation vector of the quaternion sample, and realizing course updating by using the updated quaternion;
step four: according to the characteristics of indoor light intensity distribution difference, the MT-PDR is divided into regions by using the light intensity sensor data obtained in the step one, and the regions are divided into indoor rooms and corridor regions;
step five: and overlapping the MT-PDRs after the area division to construct an indoor plan.
2. The method of claim 1, wherein the MT-PDR and the light intensity information are based on an indoor plane map, comprising: the specific process of the step one is as follows,
1.1, installing data acquisition software on an intelligent mobile terminal to realize synchronous data acquisition of a three-axis acceleration sensor, a three-axis gyroscope sensor, a three-axis Orientation sensor and a light intensity sensor;
1.2 the user holds the intelligent mobile terminal to walk indoors, and the data acquisition work of the sensor is completed through data acquisition software.
3. The method of claim 1, wherein the MT-PDR and the light intensity information are based on an indoor plane map, comprising: in the fourth step, the MT-PDR is divided into areas by using the data of the light intensity sensor,
4.1 all the light intensity data in MT-PDR are processed by min-max standard normalization, and a light intensity threshold L is setthreshold；
4.2 when the light intensity is at [0, L ]threshold]When the interval is within (2), the corresponding MT-PDR track is divided into corridor areas and is represented by straight lines, otherwise, the light intensity is (L)threshold，1]When the interval is within the range of (2), the corresponding MT-PDR track is divided into room areas and is indicated by a dotted line, thereby completing the area division of the MT-PDR.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012049492A1 (en) * | 2010-10-13 | 2012-04-19 | University Of Nottingham | Positioning system |
| CN104567931A (en) * | 2015-01-14 | 2015-04-29 | 华侨大学 | Course-drifting-error elimination method of indoor inertial navigation positioning |
| CN104977001A (en) * | 2014-04-02 | 2015-10-14 | 北京自动化控制设备研究所 | Relative navigation method applied to individual indoor navigation system |
| CN105974360A (en) * | 2016-04-27 | 2016-09-28 | 沈阳云飞科技有限公司 | Monitoring analysis method based on ADL and apparatus thereof |
| CN106441292A (en) * | 2016-09-28 | 2017-02-22 | 哈尔滨工业大学 | Building indoor planar graph establishing method based on crowdsourcing IMU inertial navigation data |
| CN106443745A (en) * | 2016-10-27 | 2017-02-22 | 广州日滨科技发展有限公司 | Course angle correction method and device |
| CN106500706A (en) * | 2015-09-06 | 2017-03-15 | 中兴通讯股份有限公司 | A kind of method of navigation and terminal |
-
2017
- 2017-07-07 CN CN201710550185.7A patent/CN107345813B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012049492A1 (en) * | 2010-10-13 | 2012-04-19 | University Of Nottingham | Positioning system |
| CN104977001A (en) * | 2014-04-02 | 2015-10-14 | 北京自动化控制设备研究所 | Relative navigation method applied to individual indoor navigation system |
| CN104567931A (en) * | 2015-01-14 | 2015-04-29 | 华侨大学 | Course-drifting-error elimination method of indoor inertial navigation positioning |
| CN106500706A (en) * | 2015-09-06 | 2017-03-15 | 中兴通讯股份有限公司 | A kind of method of navigation and terminal |
| CN105974360A (en) * | 2016-04-27 | 2016-09-28 | 沈阳云飞科技有限公司 | Monitoring analysis method based on ADL and apparatus thereof |
| CN106441292A (en) * | 2016-09-28 | 2017-02-22 | 哈尔滨工业大学 | Building indoor planar graph establishing method based on crowdsourcing IMU inertial navigation data |
| CN106443745A (en) * | 2016-10-27 | 2017-02-22 | 广州日滨科技发展有限公司 | Course angle correction method and device |
Non-Patent Citations (1)
| Title |
|---|
| 基于GMR传感器的姿态检测系统涉及;林亚坤 等;《仪表技术与传感器》;20140831(第8期);第53-55页 * |
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