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CN109520494A - One kind is based on the micro- inertia autonomous navigation method of indoor walking - Google Patents

One kind is based on the micro- inertia autonomous navigation method of indoor walking Download PDF

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CN109520494A
CN109520494A CN201710847752.5A CN201710847752A CN109520494A CN 109520494 A CN109520494 A CN 109520494A CN 201710847752 A CN201710847752 A CN 201710847752A CN 109520494 A CN109520494 A CN 109520494A
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walking
inertial
micro
heading
deviation
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CN109520494B (en
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马涛
李永锋
朱红
王根
郭元江
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Beijing Automation Control Equipment Institute BACEI
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    • G—PHYSICS
    • 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
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G—PHYSICS
    • 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
    • G01C21/20—Instruments for performing navigational calculations
    • G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Automation & Control Theory (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Navigation (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)

Abstract

本发明属于惯性导航技术领域，特别涉及一种基于室内步行微惯性自主导航方法。一种基于室内步行微惯性自主导航方法，本方法包括七个步骤，步骤一初始对准；步骤二计算航向偏差及位置偏差；步骤三对偏差求平均值；步骤四获取当前位置并计算；步骤五判断是否满足直线行走；步骤六再判断(1)；步骤七再判断(2)；步骤八、更新。本方法利用室内地形相对规整的特点，通过判断行走的轨迹情况为微惯性导航提供虚拟的航向误差观测量，抑制航向误差累积，达到抑制定位误差发散的目的。步行微惯性导航设备无需与其他信息进行组合，不需要添加设备硬件，也无需基础设施的支撑，在保持微惯性导航自主性的同时，提高步行微惯性自主导航设备的定位精度。

The invention belongs to the technical field of inertial navigation, in particular to an autonomous navigation method based on indoor walking micro-inertial. A micro-inertial autonomous navigation method based on indoor walking, the method includes seven steps, the first step is initial alignment; the second step is to calculate the heading deviation and the position deviation; the third step is to average the deviations; the fourth step is to obtain the current position and calculate; Step 5: determine whether it satisfies the requirement of walking in a straight line; step 6 and then determine (1); step 7 and then determine (2); step 8, update. This method utilizes the relatively regular characteristics of indoor terrain, and provides virtual heading error observations for micro-inertial navigation by judging the walking trajectory, suppresses the accumulation of heading errors, and achieves the purpose of suppressing the divergence of positioning errors. The walking micro-inertial navigation device does not need to be combined with other information, does not need to add equipment hardware, and does not need the support of infrastructure. While maintaining the autonomy of the micro-inertial navigation, the positioning accuracy of the walking micro-inertial autonomous navigation device is improved.

Description

One kind is based on the micro- inertia autonomous navigation method of indoor walking
Technical field
It is the invention belongs to technical field of inertial, in particular to a kind of based on the micro- inertia independent navigation side of indoor walking Method.
Background technique
In walking micro-inertial navigation equipment, since the error of Mierotubule-associated proteins is larger, it will usually be led using some other Boat system or sensor composition integrated navigation system application, such as satellite navigation, WLAN, ultra-wide band positioning etc., but this A little ancillary techniques are perhaps difficult to work normally the additional hardware infrastructure layout of perhaps needs indoors or increase itself Hardware facility is also unfavorable for keeping the independence of equipment.In the present invention, using the relatively regular feature of indoor landform, by sentencing The track situation that line-break is walked provides virtual course error observed quantity for micro-inertial navigation, without being combined with other information, Addition device hardware is not needed, without the support of infrastructure, while keeping micro-inertial navigation independence, improves walking The positioning accuracy of micro- inertia independent navigation equipment.
Summary of the invention
Presently, there are aiming at the problem that, propose a kind of based on the micro- inertia autonomous navigation method of indoor walking, lead to and do not increasing Device hardware and infrastructure, and while keep micro-inertial navigation independence, improve indoor walking micro-inertial navigation equipment Precision.
In order to realize the purpose, the technical solution adopted by the present invention is that:
One kind includes seven steps based on the micro- inertia autonomous navigation method of indoor walking, this method, and step 1 is initially aligned, It is initially aligned using paper feeding method, obtains the higher initial navigation state of precision；Step 2 calculates course deviation and position Deviation；Step 3 averages to deviation；Step 4 obtains current location and calculates；Step 5 judges whether to meet linear rows It walks；Step 6 judges (1) again；Step 7 judges (2) again；Step 8: updating.
One kind calculating course deviation and position deviation based on the micro- inertia autonomous navigation method of indoor walking, the step 2, It is after initial alignment, along straight line walking n1Step, the inertial navigation during which carried out under zero-velocity curve auxiliary calculates, sliding simultaneously It is dynamic to calculate nearest n2The course deviation δ ψ of stepi(i=1,2 ... a) and lateral position deviation δ P relative to straight line pathi(i=1, 2 ... a), wherein n1=n2+ a-1, n2>=3, a >=3 are sliding window quantity.
For one kind based on the micro- inertia autonomous navigation method of indoor walking, it is to preceding n that the step 3, which averages to deviation,1 Course deviation δ ψ obtained in stepi(i=1,2 ... a) and lateral position deviation δ P relative to straight line pathi(i=1,2 ... a) is asked Average value obtains course deviation mean valueWith lateral position deviation mean value
One kind is obtained current location and is simultaneously calculated based on the indoor micro- inertia autonomous navigation method of walking, the step 4, be In normal use, pedestrian indoors arbitrarily walk by environment, and walking micro-inertial navigation equipment still carries out used under zero-velocity curve auxiliary Property navigation, externally export positioning result, i.e. current location P, and sliding calculates nearest n at the same time2Navigation boat in the time of step To deviation δ ψkWith the lateral position deviation δ P relative to straight line pathk, save nearest n2The inertance element sampled value of step and zero The state estimation and error co-variance matrix of fast correction wave filter.
Based on the micro- inertia autonomous navigation method of indoor walking, the step 5 judges whether to meet straight line walking one kind, is Judge nearest n2In the time of step, navigational course deviation δ ψkWith the lateral position deviation δ P relative to straight line pathkWhether meet Straight line walking criterion, i.e.,
It is whether true.
Wherein α and β is respectively precedence constraints coefficient, α < 1 and β > 1.
One kind is based on the micro- inertia autonomous navigation method of indoor walking, and the step 6 judges (1) again, if the criterion of step 5 It sets up, and previous step also meets straight line walking criterion, then is increased in zero-velocity curve using the course of previous step as observation Air Canada re-starts filtering to observed quantity and calculates, and obtains new present orientation and current location.
One kind is based on the micro- inertia autonomous navigation method of indoor walking, and the step 7 judges (2) again, if the criterion of step 5 It sets up, but previous step is unsatisfactory for straight line walking criterion, then needs to judge current course and when meeting straight line criterion of the last time The relationship in the course at quarter, due to the indoor environment largely built all be it is upright, it is believed that the course phase of each straight line path Poor 90 ° or 90 ° of multiple, i.e.,
One of set up.
WhereinFor current course observation, ψk-For the last course for meeting the straight line criterion moment.
UsingAs observation, increases course observed quantity in zero-velocity curve and re-start filtering calculating, obtain new Present orientation and current location.
One kind is updated based on the micro- inertia autonomous navigation method of indoor walking, the step 8；If logical for current navigation state It crosses to recalculate and be updated, need the state of the corresponding inertance element sampled value of synchronized update and zero-velocity curve filter Estimated value and error co-variance matrix.
The invention has the benefit that
In the indoor conditions application of walking micro-inertial navigation equipment, using the relatively regular feature of indoor landform, pass through Judge that the track situation of walking provides virtual course error observed quantity for micro-inertial navigation, inhibits course error accumulation, reach Inhibit the purpose of position error diverging.Walking micro-inertial navigation equipment does not need addition and sets without being combined with other information While keeping micro-inertial navigation independence, it is certainly leading to improve the micro- inertia of walking without the support of infrastructure for standby hardware The positioning accuracy for equipment of navigating.
Detailed description of the invention
Fig. 1 is the flow chart of the method for the present invention.
Specific embodiment
The specific embodiment of the invention is as follows:
Further to illustrate technological means and its effect adopted by the present invention, preferred implementation of the invention is once combined And its attached drawing is described in detail.
One kind is specific as follows based on the micro- inertia autonomous navigation method of indoor walking, including seven steps:
Step 1: initial alignment；
It is initially aligned using known method, obtains the higher initial navigation state of precision.
Step 2: calculating course deviation and position deviation；
After initial alignment, along straight line walking n1Step, the inertial navigation during which carried out under zero-velocity curve auxiliary calculate, together When sliding calculate nearest n2The course deviation δ ψ of stepi(i=1,2 ... a) and lateral position deviation δ P relative to straight line pathi(i =1,2 ... a), wherein n1=n2+ a-1, n2>=3, a >=3 are sliding window quantity.
Step 3: averaging to deviation；
To preceding n1Course deviation δ ψ obtained in stepi(i=1,2 ... a) and lateral position deviation δ relative to straight line path Pi(i=1,2 ... a) average, and obtain course deviation mean valueWith lateral position deviation mean value
Step 4: obtaining current location and calculating；
During normal use, pedestrian indoors arbitrarily walk by environment, and walking micro-inertial navigation equipment still carries out zero-velocity curve Inertial navigation under auxiliary, externally exports positioning result, i.e. current location P, and sliding calculates nearest n at the same time2The time of step Interior navigational course deviation δ ψkWith the lateral position deviation δ P relative to straight line pathk, save nearest n2The inertance element of step is adopted The state estimation and error co-variance matrix of sample value and zero-velocity curve filter.
Step 5: judging whether to meet straight line walking；
Judge nearest n2In the time of step, navigational course deviation δ ψkWith the lateral position deviation δ P relative to straight line pathk Whether straight line walking criterion is met, i.e.,
It is whether true.
Wherein α and β is respectively precedence constraints coefficient, α < 1 and β > 1.
Step 6: judging (1) again；
If the criterion of step 5 is set up, and previous step also meets straight line walking criterion, then is made using the course of previous step For observation, increases course observed quantity in zero-velocity curve and re-start filtering calculating, obtain new present orientation and present bit It sets.
Step 7: judging (2) again；
If the criterion of step 5 is set up, but previous step be unsatisfactory for straight line walking criterion, then need to judge current course with most The relationship in the nearly primary course for meeting the straight line criterion moment, since the indoor environment largely built all is upright, so Think that the course of each straight line path differs 90 ° or 90 ° of multiple, i.e.,
One of set up.
WhereinFor current course observation, ψk-For the last course for meeting the straight line criterion moment.
UsingAs observation, increases course observed quantity in zero-velocity curve and re-start filtering calculating, obtain new Present orientation and current location.
Step 8: updating；
If current navigation state is updated by recalculating, the corresponding inertance element sampled value of synchronized update is needed And the state estimation and error co-variance matrix of zero-velocity curve filter.

Claims (8)

1.一种基于室内步行微惯性自主导航方法，其特征在于：本方法包括七个步骤，步骤一初始对准，采用供纸方法进行初始对准，得到精度较高的初始导航状态；步骤二计算航向偏差及位置偏差；步骤三对偏差求平均值；步骤四获取当前位置并计算；步骤五判断是否满足直线行走；步骤六再判断(1)；步骤七再判断(2)；步骤八、更新。1. a micro-inertial autonomous navigation method based on indoor walking, is characterized in that: this method comprises seven steps, step 1 initial alignment, adopts paper feeding method to carry out initial alignment, obtains the initial navigation state with higher precision; Step 2 Calculate the heading deviation and position deviation; step 3 average the deviations; step 4 obtain the current position and calculate; step 5 judge whether it satisfies straight line walking; step 6 and then judge (1); step 7 and then judge (2); step 8, renew. 2.如权利要求1所述的一种基于室内步行微惯性自主导航方法，其特征在于：所述步骤二计算航向偏差及位置偏差，是在初始对准完毕后，沿直线行走n1步，期间进行零速修正辅助下的惯性导航计算，同时滑动计算最近n2步的航向偏差δψi(i＝1,2…a)和相对于直线路径的侧向位置偏差δPi(i＝1,2…a)，其中n1＝n2+a-1，n2≥3，a≥3为滑动窗口数量。2. a kind of autonomous navigation method based on indoor walking micro-inertial as claimed in claim 1, it is characterized in that: described step 2 calculates heading deviation and position deviation, is after initial alignment is completed, walk n 1 steps along a straight line, During the period, the inertial navigation calculation with the aid of zero-speed correction is carried out, and the heading deviation δψ i (i=1,2...a) of the last n 2 steps and the lateral position deviation δP i (i=1, 2...a), where n 1 =n 2 +a-1, n 2 ≥3, a≥3 is the number of sliding windows. 3.如权利要求1所述的一种基于室内步行微惯性自主导航方法，其特征在于：所述步骤三对偏差求平均值，是对前n1步中得到的航向偏差δψi(i＝1,2…a)和相对于直线路径的侧向位置偏差δPi(i＝1,2…a)求平均值，得到航向偏差均值和侧向位置偏差均值 3. a kind of autonomous navigation method based on indoor walking micro-inertial as claimed in claim 1 , it is characterized in that: described step 3 averages the deviation, is to the heading deviation δψ i (i= 1,2...a) and the lateral position deviation δP i (i=1,2...a) relative to the straight path are averaged to obtain the mean heading deviation and the mean lateral position deviation 4.如权利要求1所述的一种基于室内步行微惯性自主导航方法，其特征在于：所述步骤四获取当前位置并计算，是在正常使用中，行人在室内环境任意行走，步行微惯性导航设备仍进行零速修正辅助下的惯性导航，对外输出定位结果，即当前位置P，并在同时滑动计算最近n2步的时间内的导航航向偏差δψk和相对于直线路径的侧向位置偏差δPk，保存最近n2步的惯性元件采样值以及零速修正滤波器的状态估计值和误差协方差矩阵。4. A kind of autonomous navigation method based on indoor walking micro-inertial as claimed in claim 1, it is characterized in that: described step 4 obtains current position and calculates, is in normal use, pedestrian walks arbitrarily in indoor environment, walking micro-inertial The navigation device still performs inertial navigation with the aid of zero-speed correction, outputs the positioning result, that is, the current position P, and simultaneously slides to calculate the navigation heading deviation δψ k and the lateral position relative to the straight path within the last n 2 steps. Deviation δP k , saves the sampling values of the inertial elements of the last n 2 steps and the state estimation value and error covariance matrix of the zero-speed correction filter. 5.如权利要求1所述的一种基于室内步行微惯性自主导航方法，其特征在于：所述步骤五判断是否满足直线行走，是判断最近n2步的时间内，导航航向偏差δψk和相对于直线路径的侧向位置偏差δPk是否满足直线行走判据，即5. a kind of autonomous navigation method based on indoor walking micro-inertial as claimed in claim 1, it is characterized in that: described step 5 judges whether to satisfy straight walking, is to judge within the time of recent n 2 steps, navigation course deviation δψ k and Whether the lateral position deviation δP k relative to the straight path satisfies the straight-line walking criterion, namely 是否成立。is established. 其中α和β分别为前后约束系数，α＜1且β＞1。where α and β are the front and rear constraint coefficients, respectively, α<1 and β>1. 6.如权利要求1所述的一种基于室内步行微惯性自主导航方法，其特征在于：所述步骤六再判断(1)，若步骤五的判据成立，并且上一步也满足直线行走判据，则采用上一步的航向作为观测值，在零速修正中增加航向观测量重新进行滤波计算，得到新的当前方位和当前位置。6. a kind of autonomous navigation method based on indoor walking micro-inertial as claimed in claim 1, it is characterized in that: described step 6 judges (1) again, if the criterion of step 5 is established, and the last step also satisfies the criterion of walking in a straight line. According to the data, the heading of the previous step is used as the observation value, and the heading observation value is added in the zero-speed correction to perform the filtering calculation again to obtain the new current orientation and current position. 7.如权利要求1所述的一种基于室内步行微惯性自主导航方法，其特征在于：所述步骤七再判断(2)，若步骤五的判据成立，但上一步不满足直线行走判据，则需要判断当前航向与最近一次的满足直线判据时刻的航向的关系，由于大部分建筑的室内环境都是方正的，所以认为各直线路径的航向相差90°或者90°的倍数，即7. a kind of autonomous navigation method based on indoor walking micro-inertial as claimed in claim 1, is characterized in that: described step 7 judges (2) again, if the criterion of step 5 is established, but the last step does not satisfy the criterion of walking in a straight line. According to the data, it is necessary to judge the relationship between the current heading and the heading at the most recent time when the straight line criterion is met. Since the indoor environment of most buildings is square, it is considered that the heading of each straight path differs by 90° or a multiple of 90°, that is, 之一成立。one was established. 其中为当前航向观测值，ψk-为最近一次的满足直线判据时刻的航向。in is the current heading observation value, and ψ k- is the last heading at the moment when the straight line criterion is satisfied. 采用作为观测值，在零速修正中增加航向观测量重新进行滤波计算，得到新的当前方位和当前位置。use As the observation value, add the heading observation value in the zero-speed correction and perform the filtering calculation again to obtain the new current bearing and current position. 8.如权利要求1所述的一种基于室内步行微惯性自主导航方法，其特征在于：所述步骤八更新；为若当前导航状态通过重新计算进行了更新，需要同步更新相应的惯性元件采样值以及零速修正滤波器的状态估计值和误差协方差矩阵。8. A micro-inertial autonomous navigation method based on indoor walking as claimed in claim 1, characterized in that: the step 8 is updated; for if the current navigation state is updated through recalculation, the corresponding inertial element sampling needs to be updated synchronously value and the state estimate and error covariance matrix of the zero-speed correction filter.
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