CN109579832B - Personnel height autonomous positioning algorithm - Google Patents
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- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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Abstract
The invention requests to protect a personnel highly autonomous positioning algorithm, which comprises the following steps: 1. detecting the peak characteristics of the accelerometers of the X and Z axes, and judging the upstairs and downstairs or walking states of the personnel; 2. detecting the gait of a person, calculating the step height of the person going downstairs through an accelerometer instantaneous zero capture and gyroscope three-dimensional dynamic fusion algorithm, and then calculating the height; 3. detecting turning points in the upstairs and downstairs process through the attitude angles, correcting the calculated height to be integral multiple of the half-storey height at the turning points, and reducing height errors; the altitude algorithm does not depend on a barometer and other auxiliary equipment, is high in autonomy and not easily influenced by the external environment, and is suitable for the field with various complicated indoor environments.
Description
Technical Field
The invention belongs to a height algorithm, which is independent of a barometer and any auxiliary equipment and completely dependent on an inertial sensor, has good autonomy and high reliability, and is not easily influenced by environmental change. The method is particularly suitable for indoor environments with complex environmental changes, such as the personnel positioning field and the fire rescue field of nuclear power stations.
Background
With the increasing application demands such as context awareness and environment intelligence, applications based on indoor location services are more and more favored by people, such as fire rescue, nuclear power station personnel positioning, and the like. Since a satellite signal cannot be received or is weak in an indoor building, the GPS height measurement cannot be performed indoors.
The indoor height positioning mainly refers to accurately positioning a person to a floor where the person is located, and in the current indoor person height positioning technology, the height positioning technology based on the barometer and the height positioning technology based on the fusion of the barometer and auxiliary equipment (RFID, WLAN, UWB and the like) are widely applied. The height positioning technology based on the fusion of the barometer and the auxiliary equipment needs to install equipment in an indoor building in advance, is only suitable for certain specific environments, and has poor adaptability although the precision is high. Although the height positioning technology based on the barometer has good autonomy, the barometer is easily influenced by air factors such as wind speed, temperature, humidity and the like, in some environments with complex environmental changes, the height calculated by the barometer has a large error which is up to several meters or even ten meters, and people cannot be positioned to an accurate floor.
In the inertial positioning system, due to the limitation of the barometer, in some indoor environments with complicated environmental changes, the height calculated based on the barometer is not reliable. At present, algorithms for height estimation only by means of inertial sensors are few, for example, height algorithms based on dual integration of accelerometers in the vertical direction have high requirements on the precision of accelerometers and high cost. The current indoor height algorithm has the problems of poor autonomy, easy environmental influence and the like.
Based on the above, the invention provides a pure inertial navigation-based personnel autonomous indoor height algorithm, which only depends on an MEMS accelerometer and a gyroscope, has low cost and high reliability, and can be widely applied to civil fields with complex environmental changes, such as fire rescue and nuclear power station personnel positioning.
Disclosure of Invention
The present invention is directed to solving the above problems of the prior art. The indoor height positioning algorithm is good in autonomy, high in reliability and high in precision. The technical scheme of the invention is as follows:
an algorithm for highly autonomous positioning of persons, comprising the steps of:
1) The data processing is carried out on the MEMS accelerometer and the MEMS gyroscope of the inertial system, and the data processing method mainly comprises the following steps: error calibration such as installation error, zero offset, scale factor, temperature compensation, and low pass filtering. And the inertia system is arranged at the waist of the pedestrian to obtain the motion acceleration of the pedestrian.
2) And detecting the peak value of the accelerometer on the X axis and the Z axis, which are respectively expressed as Ax max 、Az min . The accelerometer has two peaks in one cycle due to the body swing of the person walking. The patent detects the interval time T by setting a peak value c I.e. after the first peak is detected, by a time T c And then carrying out next peak value detection, and filtering the interference of the secondary peak value of the accelerometer by the time threshold value method. And judging the upstairs and downstairs or flat walking states of the personnel according to the peak characteristics of the accelerometers on the X and Z axes.
3) Detecting the gait of a person, calculating the step height of the person going downstairs through an accelerometer instantaneous zero position capturing and gyroscope three-dimensional dynamic fusion algorithm, and then calculating the height;
4) And detecting turning points in the process of going upstairs and downstairs through the attitude angles, and calculating the integral multiple of the height corrected to the half-storey height at the turning points to obtain the personnel position height.
Further, the step 1) of processing data including error calibration, temperature compensation, and low-pass filtering on the inertial system specifically includes:
101. selecting an inertial system integrating a three-axis MEMS accelerometer and a gyroscope;
102. selecting a rotation control system with two or more degrees of freedom;
103. performing zeroing operation on the main shaft and the pitching shaft of the rotation control system in the step 102;
104. vertically placing an inertial system on a rotary control system, and calibrating an accelerometer and a gyroscope, wherein calibration parameters comprise: scale factor error, zero offset;
105. selecting an incubator, and performing temperature compensation on the data subjected to the calibration processing in the step 104;
106. and (5) performing low-pass filtering processing on the data after temperature compensation in the step 105.
Further, step 1) installs inertial system on the personnel under test, obtains pedestrian's motion acceleration, specifically includes:
201. placing an inertial system at the waist by a person, collecting accelerometer data after error calibration, temperature compensation and low-pass filtering, standing the person for 1 second, collecting the local gravity acceleration components of accelerometers of X, Y and Z axes, and calculating the average value which is recorded as g _ X, g _ Y and g _ Z;
202. subtracting the mean value of the gravity acceleration in a static state from the accelerometer data of X and Z axes in the walking process of the personnel to obtain the motion acceleration data of the personnel, respectively recording the motion acceleration data as Ax and Az, and taking the motion acceleration data as a research reference quantity.
Further, step 2) detects X, Z axle accelerometer peak value characteristic, according to the peak value characteristic of accelerometer, judges personnel's the state of going upstairs or going flat, specifically includes:
301. carrying out peak value detection on the acceleration data, and recording the wave peak value of the X-axis accelerometer as Ax max The trough value of the Z-axis accelerometer, denoted as Az min ；
302. The personnel walks flatly for 2s, peak detection is carried out on the data of the X-axis accelerometer and the Z-axis accelerometer, and the peak average values of the X-axis accelerometer and the Z-axis accelerometer are obtained and are respectively marked as av _ X and av _ Z;
303. by setting a time threshold T c After the first wave peak is detected, the time T is separated c Then carrying out next peak value detection;
304. setting up the judgment threshold values of going upstairs and downstairs, and respectively recording the judgment threshold values as D 1 ,D 2 ,D 3 ；
305. The conditions for judging whether a person goes upstairs or downstairs and walks smoothly are shown as formula 1:
further, the step 3) of detecting the gait of the person, calculating the step height of the person going downstairs through an accelerometer instantaneous zero position capturing and gyroscope three-dimensional dynamic fusion algorithm, and then calculating the height specifically comprises the following steps:
401. capturing the instantaneous zero position of the accelerometer, reading the triaxial accelerometer values, which are respectively marked as Acc _ x, acc _ y and Acc _ z, and calculating a modulus value Acc _ norm, which is specifically shown as the following formula:
402. carrying out peak value detection on the Acc _ norm, wherein the wave peak value is recorded as Acc _ min, and the wave trough value is recorded as Acc _ max;
403. the personnel step size DS _ L is calculated as follows:
404. obtaining the three-dimensional attitude angle of the person according to a gyroscope three-dimensional dynamic fusion algorithm, reading the course angle and the pitch angle of the step i, and recording as yaw i ，pitch i (ii) a Obtaining the height value h of each step according to the step length, the course angle and the pitch angle 0 Specifically, the formula (4) is as follows:
h 0 ＝DS_L*sin(yaw i )*tan(pitch i ) (4)
405. detecting the gait of the person;
406. after steps 404 and 405 are finished, detecting the upstairs and downstairs states of the personnel, and if the personnel are in the upstairs state, performing height accumulation; in the downstairs state, the height is reduced; in the level walking state, the height is unchanged, specifically as shown in formula (5):
wherein H 2 Height value, H, resolved for the current time 1 Is the height value of the previous step.
Further, step 4) detects a turning point in the process of going upstairs and downstairs through the attitude angle, and specifically includes: when people go upstairs and downstairs, the stairs can turn once, the turning degree is usually finished by 4-5 steps, and the turning point between floors is judged by utilizing the course angle difference, which is specifically shown as formula (6):
wherein, raw i I =1, 2,3, 4, 5 for the heading angle of step i; alpha is a set turning judgment threshold value, and the threshold value is 100 degrees.
Further, in the step 4), the calculation height is corrected to be an integral multiple of the half-floor height at the turning point, and the correction algorithm is as shown in the formula (7):
k = -3, -2, -1,0,1,2,3 \8230; 8230; etc. H 2 The height value resolved at the current moment, beta is the height correction error threshold value, h c Building half-storey height.
The invention has the following advantages and beneficial effects:
the invention can effectively identify the upstairs and downstairs states of personnel in real time, can carry out height calculation in real time, and positions the personnel to the correct floor, and can realize the following beneficial effects in the process:
(1) The autonomy is good: the algorithm utilizes an accelerometer and a gyroscope of an inertial unit to carry out identification and height calculation on going upstairs and downstairs, does not depend on any auxiliary equipment, and is good in autonomy.
(2) The reliability is high: the algorithm does not use a barometer, is not easily influenced by the external environment, overcomes the defect that the barometer is easily influenced by the environment, and is particularly suitable for application fields with complex environmental changes, such as fire rescue and nuclear power station positioning.
(3) The precision is high: the algorithm adds a special correction algorithm, height correction is carried out at the corner of each floor, the accumulation of height errors can be effectively reduced, and personnel can be accurately positioned to the correct floor.
Drawings
FIG. 1 is a flow chart of the algorithm of the preferred embodiment of the present invention
FIG. 2 is a view showing how the inertial unit is worn
FIG. 3 shows the interference of X-axis accelerometer secondary peaks on the results of going upstairs and downstairs
FIG. 4 shows filtering sub-peak interference after setting a time threshold
FIG. 5 is a diagram of the results of the X-axis accelerometer and the Z-axis accelerometer going upstairs and downstairs
FIG. 6 is a diagram of an indoor structure of a test scenario
FIG. 7 is a graph of height resolution results
FIG. 8 is a highly resolved graph of changing environmental factors
Detailed Description
The technical solutions in the embodiments of the present invention will be described in detail and clearly in the following with reference to the accompanying drawings. The described embodiments are only some of the embodiments of the present invention.
The technical scheme for solving the technical problems is as follows:
the invention discloses a personnel height autonomous positioning algorithm only depending on an inertial sensor, and a flow chart of the technical scheme is shown as a figure 1 and specifically comprises the following steps:
firstly, data processing is firstly carried out on an accelerometer and a gyroscope of an inertial unit:
1. an inertial system integrating a three-axis MEMS accelerometer and gyroscope is selected.
2. And selecting a double-circle electric turntable of the rotation control system, and carrying out zero resetting operation on a main shaft and a pitching shaft of the electric turntable to enable the turntable to be horizontal.
3. Vertically placing the inertial system in a state, and calibrating the accelerometer and the gyroscope, wherein the calibration parameters comprise: scale factor error, zero offset.
4. And selecting a high-temperature box, placing the inertia unit in the high-temperature box, setting the temperature to be-40-80 ℃, collecting the experimental data of the accelerometer, and performing temperature compensation on the accelerometer.
5. And performing low-pass filtering processing on the data after calibration and temperature compensation.
Secondly, identifying the upstairs and downstairs states of the personnel:
1. the person places the inertial system in a lumbar position, fig. 2. And collecting the accelerometer data after calibration, compensation and filtering. And (3) standing for 1 second, collecting the gravity acceleration components of the accelerometers at the X, Y and Z axes in the local area, and solving the average values which are recorded as g _ X, g _ Y and g _ Z.
2. And subtracting the gravity acceleration mean value in a static state from the acceleration data of the X and Z axes in the walking process of the person to obtain the motion acceleration data of the person, recording the motion acceleration data as Ax and Az respectively, and taking the motion acceleration data as a study reference quantity.
3. Carrying out peak value detection on accelerometer data, recording wave peak value of X-axis accelerometerIs Ax max The trough value of the Z-axis accelerometer, denoted as Az min 。
4. And (4) the person walks flatly for 2s, and peak average values of the accelerometers of the X axis and the Z axis are collected and are respectively marked as av _ X and av _ Z.
5. Because personnel are in the walking process, the body can swing a little, therefore 2 wave crests can appear in X axle accelerometer in a cycle, and a pseudo-wave crest is followed to the main wave crest promptly, and this pseudo-wave crest can make the mistake judgement take place for going upstairs and downstairs. As shown in fig. 3, during the leveling walking process, the leveling walking is erroneously determined as downstairs due to the interference of the secondary peak. The algorithm is realized by setting a time threshold value T c To filter out interference by sub-peaks, i.e. after the first peak is detected, a time interval T is provided c And then the next peak value detection is carried out. The interference of the sub-peak can be obviously filtered by the time interval method, and the accuracy of the upstairs and downstairs judgment is improved, as shown in fig. 4.
6. Setting up judgment threshold values for going upstairs and downstairs, and respectively recording as D 1 ,D 2 ,D 3 。
7. The conditions for judging whether a person goes upstairs or downstairs indoors and walks smoothly are shown as formula 1:
the results of the upstairs and downstairs determination are shown in fig. 5, and when going upstairs, the wave peak value of the X-axis accelerometer is larger than the average value of the wave peak values when going straight; when the vehicle goes downstairs, the wave peak value of the X-axis accelerometer is smaller than the average value of the wave peak value during flat walking, and the wave valley value of the Z-axis accelerometer is smaller than the average value of the wave valley value during flat walking. The up-down and horizontal walking states of the personnel can be effectively identified through the peak characteristics of the X-axis accelerometer and the Z-axis accelerometer, and the up-down judgment accuracy is more reliable.
Thirdly, height calculation and height correction are carried out when people walk:
1. capturing the instantaneous zero position of the accelerometer, reading the triaxial accelerometer values at this time, respectively recording as Acc _ x, acc _ y and Acc _ z, and calculating a modulus value Acc _ norm, which is specifically shown as the following formula:
2. and performing peak detection on the Acc _ norm, wherein the wave peak value is recorded as Acc _ min, and the wave trough value is recorded as Acc _ max. The calculation of the staff step length DS _ L is shown in equation 3:
3. calculating the three-dimensional attitude angle of the personnel according to the three-dimensional dynamic fusion algorithm of the gyroscope, reading the course angle and the pitch angle of the step i, and recording as yaw i ，pitch i (ii) a Obtaining the height value h of each step according to the step length, the course angle and the pitch angle 0 Specifically, the formula is shown in formula 4:
h 0 ＝DS_L*cos(yaw i -yaw i-1 )*tan(pitch i -pitch i-1 ) (4)
4. and carrying out gait detection on the person.
5. Identifying the upstairs and downstairs states of the personnel, and if the personnel go upstairs, performing height accumulation; if the vehicle goes downstairs, the height is reduced; if the height is unchanged after the walking is leveled, the height is specifically shown as formula 6:
wherein H 2 For the height value of the subsequent step, H 1 Is the height value of the previous step.
6. Usually, when people go upstairs and downstairs, a turn is made in a staircase, and the turn can be finished by 4-5 steps. Therefore, the turning point between floors can be judged by utilizing the difference value of the heading angle, and the turning point is specifically shown as the formula 3:
wherein, raw i I =1, 2,3 for the heading angle of step i4, 5; alpha is a set turning judgment threshold value, the threshold value is 100 degrees, and in order to avoid repeatedly detecting one turning point, the algorithm detects the turning degree after 4 seconds after detecting one turning point. As shown in fig. 7 and 8, 98% of the turning points are detected during the upstairs and downstairs.
5. In a conventional square building, a staircase is generally in a zigzag shape, 1-2 turning points are detected in the process of going upstairs and downstairs by one floor, and the turning points are generally at the height of a half floor or the height of the whole floor. When the turning point is detected, the current height value is corrected, and meanwhile, when the person is detected to be in a flat walking state, the height is also corrected. The experiment group investigates the buildings such as nearby teaching buildings, office buildings and the like, and the height of each floor is about 4 meters, so the correction height h is set c =2 m. Setting an error threshold value beta =1.3 m for height correction, wherein a specific correction algorithm is shown as formula 4:
k = -3, -2, -1,0,1,2,3 \8230; 8230; etc. The calculation result of the height of the upstairs and downstairs is shown in fig. 7, fig. 7 shows that a person enters a teaching building from the outside and walks to the outside (breeze), and the process that the person walks to the second floor is carried out twice, and it can be seen from fig. 7 that the barometer is easily influenced by the wind speed, the height value has a great error, and the height value calculated by the algorithm is subjected to height correction at the turning position of each floor, and the height error generated when the person goes to the upstairs and downstairs and is misjudged can be effectively compensated through the correction algorithm, so that the person can be correctly positioned to the floor.
6. A set of comparative experiments is also carried out, namely, the place is selected as the first teaching building of a school, the height of each floor is 4 meters, people walk randomly, enter a primary air-conditioning (refrigerating) classroom when the third floor is reached, the simulated air environment is lowered, and when the fourth floor is reached, a hot water bag is placed beside an inertia unit to simulate the rising of the air environment. Specific results as shown in fig. 8, it can be seen that the data of the barometer has a large error due to the change of the environment, and the change of the external environment has a small influence on the algorithm.
The above examples are to be construed as merely illustrative and not limitative of the remainder of the disclosure in any way whatsoever. After reading the description of the present invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.
Claims (3)
1. A human height autonomous positioning algorithm is characterized in that positioning calculation is carried out only by means of a MEMS accelerometer and a gyroscope, and the method comprises the following steps:
1) And carrying out data processing on the MEMS accelerometer and the MEMS gyroscope of the inertial system, wherein the data processing comprises the following steps: the method comprises the steps of correcting installation errors, zero offset and scale factor errors, compensating temperature and filtering in a low-pass mode; the inertia system is arranged at the waist of the pedestrian to obtain the motion acceleration of the pedestrian;
2) And detecting the peak value of the accelerometer on the X axis and the Z axis, which are respectively expressed as Ax max 、Az min The accelerometer has two wave peaks in one period due to the body swing of the person walking, and the interval time T is detected by setting one peak value c I.e. after the first peak is detected, by a time T c Then, next peak value detection is carried out, the interference of the secondary peak of the accelerometer is filtered by the time threshold value method, and the upstairs and downstairs or flat walking state of the personnel is judged according to the peak value characteristics of the accelerometer on the X axis and the Z axis;
3) Detecting the gait of a person, calculating the step height of the person going downstairs through an accelerometer instantaneous zero position capturing and gyroscope three-dimensional dynamic fusion algorithm, and then calculating the height;
4) Detecting turning points in the process of going upstairs and downstairs through the attitude angles, and correcting the calculated height to be integral multiple of the half-floor height at the turning points to obtain the personnel position height;
step 3) detecting the gait of the person, calculating the step height of the person going downstairs through an accelerometer instantaneous zero position capturing and gyroscope three-dimensional dynamic fusion algorithm, and then calculating the height, wherein the step specifically comprises the following steps:
501. capturing instantaneous zero positions of the accelerometers, collecting the local gravity acceleration components of the accelerometers on X, Y and Z axes, calculating the average value of the components, recording the average value as g _ X, g _ Y and g _ Z, reading the triaxial acceleration value as Acc _ X, acc _ Y and Acc _ Z, and calculating the modulus value Acc _ norm, wherein the formula is shown as follows:
collecting the local gravity acceleration components of the X-axis accelerometer, the Y-axis accelerometer and the Z-axis accelerometer, and calculating the average value which is recorded as g _ X, g _ Y and g _ Z
502. Carrying out peak value detection on the Acc _ norm, wherein the trough value is recorded as Acc _ min, and the wave peak value is recorded as Acc _ max;
503. the personnel step size DS _ L is calculated as follows:
504. obtaining the three-dimensional attitude angle of the person according to a gyroscope three-dimensional dynamic fusion algorithm, reading the course angle and the pitch angle of the step i, and recording as yaw i ，pitch i (ii) a Obtaining the height value h of each step according to the step length, the course angle and the pitch angle 0 Specifically, as shown in formula (4):
h 0 ＝DS_L*sin(yaw i )*tan(pitch i ) (4)
505. detecting the gait of the person;
506. after the steps 504 and 505 are finished, detecting the upstairs and downstairs states of the personnel, and if the personnel are in the upstairs state, performing height accumulation; in the downstairs state, the height is decreased gradually; in the level walking state, the height is unchanged, specifically as shown in formula (5):
wherein H 2 Height value, H, resolved for the current time 1 For the previous step of solutionCalculating a height value;
step 4) detects the turning point in the process of going upstairs and downstairs through the attitude angle, and the method specifically comprises the following steps: when people go upstairs and downstairs, the stairs can turn once, the turning degree is usually finished by 4-5 steps, and the turning point between floors is judged by utilizing the course angle difference, which is specifically shown as formula (6):
wherein, raw i I =1, 2,3, 4, 5 for the course angle of the ith step; alpha is a set turning judgment threshold value, and the value of the threshold value is 100 degrees;
and 4) correcting the calculation height to be integral multiple of half-floor height at a turning point, wherein the correction algorithm is shown as a formula (7):
k＝-3，-2，-1，0，1，2,3……，H 2 the height value resolved at the current moment, beta is the height correction error threshold value, h c Building half-storey height;
step 2) detects the peak characteristics of the accelerometers of the X and Z axes, and judges the upstairs and downstairs or horizontal walking states of the personnel according to the peak characteristics of the accelerometers, and the method specifically comprises the following steps:
401. carrying out peak value detection on the acceleration data, and recording the wave peak value of the X-axis accelerometer as Ax max The trough value of the Z-axis accelerometer is noted as Az min ；
402. The personnel horizontally walk for 2s, peak value detection is carried out on the data of the X-axis accelerometer and the Z-axis accelerometer, and peak value average values of the X-axis accelerometer and the Z-axis accelerometer are obtained and are respectively marked as av _ X and av _ Z;
403. two wave peaks appear in the accelerometer in one period due to the body swing of a person walking, and the interval time T is detected by setting one peak value c I.e. after the first peak is detected, by a time T c Then the next timeThe interference of the secondary wave peak of the accelerometer is filtered by the time threshold method;
404. setting up judgment threshold values for going upstairs and downstairs, and respectively recording as D 1 ,D 2 ,D 3 ；
405. The conditions for judging whether people go upstairs or downstairs and walk smoothly are shown as formula 1:
2. the algorithm for locating the person at high degree of autonomy as claimed in claim 1, wherein the step 1) of processing the data of the MEMS accelerometer and the MEMS gyroscope of the inertial system includes error calibration, temperature compensation and low-pass filtering, and specifically includes:
201. selecting an inertial system integrating a three-axis MEMS accelerometer and a gyroscope;
202. selecting a rotation control system with two or more degrees of freedom;
203. performing zeroing operation on the main shaft and the pitching shaft of the rotation control system in the step 102;
204. vertically placing an inertial system on a rotary control system, and calibrating an accelerometer and a gyroscope, wherein calibration parameters comprise: installation error, scale factor error, zero offset;
205. selecting an incubator, and performing temperature compensation on the data subjected to the calibration processing in the step 104;
206. and (5) performing low-pass filtering processing on the data after temperature compensation in the step 105.
3. The people high autonomous positioning algorithm according to claim 2, wherein the step 2) of installing an inertial system on the tested person to obtain the acceleration of the pedestrian comprises:
301. placing an inertial system at the waist of a person, collecting accelerometer data after error calibration, temperature compensation and low-pass filtering, standing the person for 1 second, collecting the local gravity acceleration components of accelerometers on X, Y and Z axes, and calculating the mean value of the components, and marking the mean value as g _ X, g _ Y and g _ Z;
302. and subtracting the gravity acceleration mean value in a static state from the acceleration data of the X axis and the Z axis in the walking process of the person to obtain the motion acceleration data of the person, and recording the motion acceleration data as Ax and Az respectively, and taking the motion acceleration data as a study reference quantity.
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| CN112568899A (en) * | 2019-09-30 | 2021-03-30 | 深圳市荣盛智能装备有限公司 | Personnel posture judgment method and system |
| CN111197974B (en) * | 2020-01-15 | 2021-12-17 | 重庆邮电大学 | Barometer height measuring and calculating method based on Android inertial platform |
| CN112747747B (en) * | 2021-01-20 | 2022-10-11 | 重庆邮电大学 | Improved UWB/IMU fusion indoor pedestrian positioning method |
| CN113720332B (en) * | 2021-06-30 | 2022-06-07 | 北京航空航天大学 | Floor autonomous identification method based on floor height model |
| CN114459460A (en) * | 2022-01-07 | 2022-05-10 | 山东云海国创云计算装备产业创新中心有限公司 | Indoor staircase pedestrian positioning device and method |
| CN114993304A (en) * | 2022-06-29 | 2022-09-02 | 北京石头创新科技有限公司 | Method, device, medium and electronic equipment for determining position of robot |
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