CN106525053A - Indoor positioning method for mobile robot based on multi-sensor fusion - Google Patents
Indoor positioning method for mobile robot based on multi-sensor fusion Download PDFInfo
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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
- G01C21/20—Instruments for performing navigational calculations
- G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
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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
- G01C21/14—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 recording the course traversed by the object
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Abstract
The invention provides an indoor positioning method for a mobile robot based on multi-sensor fusion. The milemeter position data based on track plotting is adopted for compensating for an unrecognized similar environment in laser matching positioning. According to the invention, the sensor positioning data based on an inertia unit and a milemeter is fused, and the laser radar matching positioning is also considered as a reference index, so that the accumulative error of the milemeter is reduced, and the defect that a short-distance laser radar cannot be matched and positioned under a single characteristic environment is overcome, so that more accurate positioning data can be supplied for map drawing and navigation of the robot.
Description
Technical field
The invention belongs to wheeled mobile robot indoor positioning field, more particularly, to a kind of based on Multi-sensor Fusion
Mobile robot indoor orientation method.
Background technology
When mobile robot is moved in environment indoors, first it is to be understood that where laying oneself open to, this is that robot enters
Row environmental map is drawn, the important prerequisite of independent navigation is exactly accurate position and pose.Therefore, indoor mobile robot positioning
Technology is always the study hotspot and difficult point of robot field.
With the progress of sensing technology, the sensor for indoor mobile robot positioning is also being constantly updated, overall next
Say, current robot localization method is broadly divided into two classes：Relative localization method and absolute fix method.Relative localization method refers to robot
According to self-sensor device, such as speedometer, identification loop etc. obtains the relative displacement and steering in the short time, with reference to during upper one sampling
The cumulative pose carved draws pose of the robot at current time, mainly has dead reckoning and inertial navigation method.
Dead reckoning is mainly used in short distance positioning, and during long range, the cumulative error of encoder is fairly obvious；Acceleration
Meter and gyroscope there are problems that systematic error and drift, will also result in cumulative error.
Absolute fix Fa Zhi robots directly determine its pose in world coordinate system by external sensor, often use
Road sign method, GPS, map match etc..Road sign method is difficult in maintenance, and needs to be changed environment；GPS is used for outdoor, no
It is adapted to environment indoors to use；The conventional laser radar of map match obtains environmental information, data will be adopted in front and back to be matched with
Robot global pose is obtained, but the laser radar of wide range involves great expense, the laser radar of small-range cannot be processed as corridor
General environment feature similarity, changes unconspicuous scene.
In conventional research, it is thus proposed that at set intervals using laser scanning matching calibration speedometer positioning.But
This is generally required using expensive large range laser radar, and in the single indoor environment of some features, laser
With displacement cannot be inferred according to matching result, error in data can be caused hence with its location data calibration speedometer, therefore, this
Invention proposes a kind of mobile robot indoor orientation method based on Multi-sensor Fusion, using the speedometer based on reckoning
The situation of None- identified similar environments in position data compensation laser matching positioning, on the basis of indoor position accuracy is improved,
Its environmental suitability is higher.
The content of the invention
In view of this, it is contemplated that proposing a kind of mobile robot indoor orientation method based on Multi-sensor Fusion,
Mapping and navigation for robot provides more accurate location data.
To reach above-mentioned purpose, the technical scheme is that what is be achieved in that：
A kind of mobile robot indoor orientation method based on Multi-sensor Fusion, is calculated using the ICP that laser data is matched
Method carries out positioning estimation, and the pose change obtained using dead reckoning carries out positioning result compensation.
Further, the ICP algorithm of described utilization laser data matching carries out positioning estimation to be included：
If the sampling period is Δ t, before the ith sample cycle, the accurate pose of robot is
P=(x, y, θ)T
In the ith sample cycle, the pose change for obtaining robot movement by laser scanning matching is turned to：
Δpicp_i=[Δ xipc_i Δyipc_i Δθipc_i]T；
If now the translational speed of robot is vicp_i=[vicp_xi,vicp_yi,wicp_i]T, therefore there is following relation：
vicp_xi=Δ xicp_i/ Δ t or vicp_xi=Δ yicp_i/ Δ t, and Δ Sicp_i=Δ xicp_iOr Δ Sicp_i=Δ
yicp_i,
Wherein Δ Sicp_iIt is displacement of the robot obtained by matching algorithm within this sampling period.
Further, the ICP algorithm for being matched using laser data is carried out positioning estimation and is specifically included：
(a1) laser scanning of this moment is designated as into Current Scan D, upper moment scanning is designated as reference scan M；
(b1) obtain D is matched the optimal transformation (R, T) of M by the matching process of point to line, wherein R is rotation transformation
Matrix, T are translation vector：
(c1) pose changes delta p of current robot is calculated according to (R, T)k=(Δ xk,Δyk,Δθk)T, it is assumed that the k moment
Robot pose is pk=(xk,yk,θk)T, then k+1 moment robots pose be：
(d1) Current Scan D is designated as into new reference scan M, continues sampling laser data, by step (a1) again
Iteration.
Further, the pose change that described use dead reckoning is obtained carries out positioning result compensation to be included：
Positioning estimation is carried out using dead reckoning, the pose change for obtaining ith sample cycle inner machine people movement is turned to：
Δptrack_i=[Δ xtrack_i,Δytrack_i,Δθtrack_i]T；
If now the translational speed of robot is vtrack_i=[vtrack_xi,vtrack_yi,wtrack_i]T, then：
vtrack_xi=Δ Strack_i/ Δ t, vtrack_yi=0.0, wi=Δ θtrack_i/Δt；
Wherein Δ Strack_iIt is displacement of the robot obtained by dead reckoning within this sampling period.
Further, the pose change that described use dead reckoning is obtained carries out positioning result compensation and specifically includes：
(a2) assume, within a sampling period, to receive motor encoder feedback, unit conversion obtains the shifting of revolver, right wheel
Dynamic distance respectively Δ SlWith Δ Sr, the angle for turning over is Δ θ, then can calculate and obtain robot at this using in the cycle
Displacement and anglec of rotation angle：
Wherein, 2R be robot two-wheeled spacing, Δ S be robot movement distance, the angle that Δ θ is turned over for robot；
(b2) motion of the robot under world coordinate system can be shown below：
Wherein, the cumulative angle that robot has been rotated through before θ is this moment；
(c2) it follows that k moment robots pose is pk=(xk,yk,θk), unit sampling time inner machine people pose
Change turns to Δ pk=(Δ xk,Δyk,Δθk), then according to dead reckoning, pose of the robot at the k+1 moment is：
Further, also include the ICP algorithm matched using laser data carry out pose change that positioning estimation obtains with
And the pose change obtained using dead reckoning is made the difference, pose difference Δ p is obtained.
Further, if meeting
Δ p≤ξ, wherein ξ>0,
Then think that the pose change that laser scanning matching now is obtained is correct
Further, if being unsatisfactory for
Δ p≤ξ, wherein ξ>0,
Then enabling the pose change that dead reckoning obtains carries out positioning result compensation.
Relative to prior art, a kind of mobile robot indoor positioning side based on Multi-sensor Fusion of the present invention
Method has the advantage that：
(1) the environments match location algorithm based on laser radar is tied by the present invention with the dead reckoning based on speedometer
Close, complete the indoor positioning of mobile robot；
(2) present invention solves use using the speedometer compensation data laser radar location data based on reckoning
Short range laser radar cannot be distinguished by that feature is single and the unconverted environment of long-time.
Description of the drawings
The accompanying drawing for constituting the part of the present invention is used for providing a further understanding of the present invention, the schematic reality of the present invention
Apply example and its illustrate, for explaining the present invention, not constituting inappropriate limitation of the present invention.In the accompanying drawings：
Fig. 1 is that a kind of mobile robot indoor orientation method based on Multi-sensor Fusion described in the embodiment of the present invention shows
It is intended to.
Specific embodiment
It should be noted that in the case where not conflicting, the embodiment and the feature in embodiment in the present invention can phase
Mutually combine.
Below with reference to the accompanying drawings and in conjunction with the embodiments describing the present invention in detail.
Scan matching positioning of the present invention using the speedometer location data compensation laser radar based on reckoning, the party
The realization of method is based on following theoretical：
(1) the reckoning model based on encoder
Assume within the sampling period, receive motor encoder feedback, unit conversion obtain revolver, right wheel movement away from
From respectively Δ SlWith Δ Sr, the angle for turning over is Δ θ, then can calculate the movement for obtaining robot within this is using the cycle
Distance and anglec of rotation angle：
Wherein, 2R be robot two-wheeled spacing, Δ S be robot movement distance, the angle that Δ θ is turned over for robot.
Due to the sampling interval it is very short, it is believed that robot displacement is similar to straight line, then robot is in world coordinate system
Under motion can be shown below：
Wherein, the cumulative angle that robot has been rotated through before θ is this moment.
It follows that k moment robots pose is pk=(xk,yk,θk), the change of unit sampling time inner machine people pose
For Δ pk=(Δ xk,Δyk,Δθk), then according to dead reckoning, pose of the robot at the k+1 moment is：
(2) ICP algorithm matched based on laser data
The thought of ICP algorithm is that the two neighboring laser data frame to continuous acquisition is matched, and is obtained between the two
Relative pose transformation relation, so as to incrementally update the current pose of robot, algorithm steps can be summarized as：
A the laser scanning of this moment is designated as Current Scan D by (), upper moment scanning is designated as reference scan M；
B () obtains D is matched the optimal transformation (R, T) of M by the matching process of point to line, wherein R is rotation transformation
Matrix, T are translation vector：
C () calculates pose changes delta p of current robot according to (R, T)k=(Δ xk,Δyk,Δθk)T, it is assumed that k moment machines
Device people pose is pk=(xk,yk,θk)T, then k+1 moment robots pose be：
D Current Scan D is designated as new reference scan M by (), continue sampling laser data, changed by step (a) again
Generation.
As shown in figure 1, the algorithm steps of Multi-sensor Fusion positioning of the present invention：
In circumstances not known, during environmental map is set up in manual control machine device people walking, straight ahead and original place rotation are only done
Rotate and make, the purpose of this agreement is reduced during mapping because the map that error causes is inclined.
The hypothesis sampling period is Δ t, and before the ith sample cycle, the accurate pose of robot is p=(x, y, θ)T。
The first step, carries out positioning estimation using the ICP algorithm matched based on laser data mentioned in key theory (2),
In the ith sample cycle, the pose change for obtaining robot movement by laser scanning matching is turned to：Δpicp_i=[Δ xipc_i
Δyipc_i Δθipc_i]T
If now the translational speed of robot is vicp_i=[vicp_xi,vicp_yi,wicp_i]T, for Two-wheeled movement is moved
Mobile robot, vy=0 permanent establishment.Arranged according to premise, only exist straight forward and pivot turn, thus below equation it is permanent into
It is vertical：
vicp_xi=Δ xicp_i/ Δ t or vicp_xi=Δ yicp_i/ Δ t, and Δ Sicp_i=Δ xicp_iOr Δ Sicp_i=Δ
yicp_i。
Wherein Δ Sicp_iIt is displacement of the robot obtained by matching algorithm within this sampling period.
Second step, carries out positioning estimation using dead reckoning, obtains machine in the ith sample cycle according to formula (1)～(3)
The pose change of device people movement is turned to：
Δptrack_i=[Δ xtrack_i,Δytrack_i,Δθtrack_i]T
If now the translational speed of robot is vtrack_i=[vtrack_xi,vtrack_yi,wtrack_i]T, then：
vtrack_xi=Δ Strack_i/ Δ t, vtrack_yi=0.0, wi=Δ θtrack_i/Δt。
Wherein Δ Strack_iIt is displacement of the robot obtained by dead reckoning within this sampling period.
3rd step, verifies whether the pose change that laser scanning matching is obtained is correct：
First and second step is that the pose change to ith sample cycle inner machine people movement is estimated, if both estimates
Calculate correct, Ying You：
|Δptrack_i-Δpicp_i|≤ξ (5)
Wherein ξ is a number more than zero, and its implication is the limits of error of the pose that two kinds of pose estimation algorithms are obtained.
If result meets (5) formula, then it is assumed that the pose change that laser scanning matching now is obtained is correct, then during the sampling
Between after, robot pose piCan be expressed as：
pi=pi-1+Δpicp_i (6)
If result of calculation is unsatisfactory for (5) formula, determine whether, if now vicp_i≈ 0.0 but vtrack_i≠ 0.0, then sentence
The pose change mistake that disconnected laser scanning matching now is obtained, guess robot enter the single section of architectural feature, enable boat
The pose change that mark predication method is obtained carries out positioning result compensation, i.e.,：
pi=pi-1+Δptrack_i (7)
And so on, till the environmental map of closing is created until robot.
Presently preferred embodiments of the present invention is the foregoing is only, not to limit the present invention, all essences in the present invention
Within god and principle, any modification, equivalent substitution and improvements made etc. should be included within the scope of the present invention.
Claims (8)
1. a kind of mobile robot indoor orientation method based on Multi-sensor Fusion, it is characterised in that：Using laser data
The ICP algorithm matched somebody with somebody carries out positioning estimation, and the pose change obtained using dead reckoning carries out positioning result compensation.
2. a kind of mobile robot indoor orientation method based on Multi-sensor Fusion according to claim 1, its feature
It is：The ICP algorithm of described utilization laser data matching carries out positioning estimation to be included：
If the sampling period is Δ t, before the ith sample cycle, the accurate pose of robot is
P=(x, y, θ)T
In the ith sample cycle, the pose change for obtaining robot movement by laser scanning matching is turned to：
Δpicp_i=[Δ xipc_i Δyipc_i Δθipc_i]T；
If now the translational speed of robot is vicp_i=[vicp_xi,vicp_yi,wicp_i]T, therefore there is following relation：
vicp_xi=Δ xicp_i/ Δ t or vicp_xi=Δ yicp_i/ Δ t, and Δ Sicp_i=Δ xicp_iOr Δ Sicp_i=Δ
yicp_i,
Wherein Δ Sicp_iIt is displacement of the robot obtained by matching algorithm within this sampling period.
3. a kind of mobile robot indoor orientation method based on Multi-sensor Fusion according to claim 2, its feature
It is：The ICP algorithm matched using laser data is carried out positioning estimation and is specifically included：
(a1) laser scanning of this moment is designated as into Current Scan D, upper moment scanning is designated as reference scan M；
(b1) obtain D is matched the optimal transformation (R, T) of M by the matching process of point to line, wherein R is rotation transformation square
Battle array, T is translation vector：
(c1) pose changes delta p of current robot is calculated according to (R, T)k=(Δ xk,Δyk,Δθk)T, it is assumed that k moment machines
People's pose is pk=(xk,yk,θk)T, then k+1 moment robots pose be：
(d1) Current Scan D is designated as into new reference scan M, continues sampling laser data, the iteration again by step (a1).
4. a kind of mobile robot indoor orientation method based on Multi-sensor Fusion according to claim 1, its feature
It is：The pose change that described use dead reckoning is obtained carries out positioning result compensation to be included：
Positioning estimation is carried out using dead reckoning, the pose change for obtaining ith sample cycle inner machine people movement is turned to：
Δptrack_i=[Δ xtrack_i,Δytrack_i,Δθtrack_i]T；
If now the translational speed of robot is vtrack_i=[vtrack_xi,vtrack_yi,wtrack_i]T, then：
vtrack_xi=Δ Strack_i/ Δ t, vtrack_yi=0.0, wi=Δ θtrack_i/Δt；
Wherein Δ Strack_iIt is displacement of the robot obtained by dead reckoning within this sampling period.
5. a kind of mobile robot indoor orientation method based on Multi-sensor Fusion according to claim 4, its feature
It is：The pose change that described use dead reckoning is obtained carries out positioning result compensation and specifically includes：
(a2) assume within the sampling period, receive motor encoder feedback, unit conversion obtain revolver, right wheel movement away from
From respectively Δ SlWith Δ Sr, the angle for turning over is Δ θ, then can calculate the movement for obtaining robot within this is using the cycle
Distance and anglec of rotation angle：
Wherein, 2R be robot two-wheeled spacing, Δ S be robot movement distance, the angle that Δ θ is turned over for robot；
(b2) motion of the robot under world coordinate system can be shown below：
Wherein, the cumulative angle that robot has been rotated through before θ is this moment；
(c2) it follows that k moment robots pose is pk=(xk,yk,θk), the change of unit sampling time inner machine people pose is turned to
Δpk=(Δ xk,Δyk,Δθk), then according to dead reckoning, pose of the robot at the k+1 moment is：
6. a kind of mobile robot indoor orientation method based on Multi-sensor Fusion according to claim 1, its feature
It is：Also include that the ICP algorithm matched using laser data carries out the pose change and push away using flight path that positioning estimation obtains
The pose change that algorithm is obtained makes the difference, and obtains pose difference Δ p.
7. a kind of mobile robot indoor orientation method based on Multi-sensor Fusion according to claim 6, its feature
It is：If meeting
Δ p≤ξ, wherein ξ>0,
Then think that the pose change that laser scanning matching now is obtained is correct.
8. a kind of mobile robot indoor orientation method based on Multi-sensor Fusion according to claim 6, its feature
It is：
If being unsatisfactory for
Δ p≤ξ, wherein ξ>0,
Then enabling the pose change that dead reckoning obtains carries out positioning result compensation.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106918830A (en) * | 2017-03-23 | 2017-07-04 | 安科机器人有限公司 | A kind of localization method and mobile robot based on many navigation modules |
| CN107300696A (en) * | 2017-06-16 | 2017-10-27 | 北京军立方机器人科技有限公司 | A kind of position of mobile robot bearing calibration and device based on RFID |
| CN107990893A (en) * | 2017-11-24 | 2018-05-04 | 南京航空航天大学 | The detection method that environment is undergone mutation is detected in two-dimensional laser radar SLAM |
| CN108036786A (en) * | 2017-12-01 | 2018-05-15 | 安徽优思天成智能科技有限公司 | Position and posture detection method, device and computer-readable recording medium based on auxiliary line |
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103777220A (en) * | 2014-01-17 | 2014-05-07 | 西安交通大学 | Real-time and accurate pose estimation method based on fiber-optic gyroscope, speed sensor and GPS |
| CN105180933A (en) * | 2015-09-14 | 2015-12-23 | 中国科学院合肥物质科学研究院 | Mobile robot track plotting correcting system based on straight-running intersection and mobile robot track plotting correcting method |
| CN105547288A (en) * | 2015-12-08 | 2016-05-04 | 华中科技大学 | Self-localization method and system for mobile device in underground coal mine |
| CN106123890A (en) * | 2016-06-14 | 2016-11-16 | 中国科学院合肥物质科学研究院 | A kind of robot localization method of Fusion |
-
2016
- 2016-12-28 CN CN201611230784.2A patent/CN106525053A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103777220A (en) * | 2014-01-17 | 2014-05-07 | 西安交通大学 | Real-time and accurate pose estimation method based on fiber-optic gyroscope, speed sensor and GPS |
| CN105180933A (en) * | 2015-09-14 | 2015-12-23 | 中国科学院合肥物质科学研究院 | Mobile robot track plotting correcting system based on straight-running intersection and mobile robot track plotting correcting method |
| CN105547288A (en) * | 2015-12-08 | 2016-05-04 | 华中科技大学 | Self-localization method and system for mobile device in underground coal mine |
| CN106123890A (en) * | 2016-06-14 | 2016-11-16 | 中国科学院合肥物质科学研究院 | A kind of robot localization method of Fusion |
Cited By (21)
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|---|---|---|---|---|
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| CN108548536A (en) * | 2018-01-05 | 2018-09-18 | 广东雷洋智能科技股份有限公司 | The dead reckoning method of unmanned intelligent robot |
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| CN109144056B (en) * | 2018-08-02 | 2021-07-06 | 上海思岚科技有限公司 | Global self-positioning method and device for mobile robot |
| CN109144056A (en) * | 2018-08-02 | 2019-01-04 | 上海思岚科技有限公司 | The global method for self-locating and equipment of mobile robot |
| CN110045733A (en) * | 2019-04-04 | 2019-07-23 | 肖卫国 | A kind of real-time location method and its system, computer-readable medium |
| CN110045733B (en) * | 2019-04-04 | 2022-11-01 | 肖卫国 | Real-time positioning method and system and computer readable medium |
| CN110553652A (en) * | 2019-10-12 | 2019-12-10 | 上海高仙自动化科技发展有限公司 | robot multi-sensor fusion positioning method and application thereof |
| CN110553652B (en) * | 2019-10-12 | 2022-06-24 | 上海高仙自动化科技发展有限公司 | Robot multi-sensor fusion positioning method and application thereof |
| CN112923931A (en) * | 2019-12-06 | 2021-06-08 | 北理慧动(常熟)科技有限公司 | Feature map matching and GPS positioning information fusion method based on fixed route |
| CN110954100A (en) * | 2019-12-30 | 2020-04-03 | 广东省智能制造研究所 | Method for estimating body state of foot type robot based on fusion of laser and inertial navigation |
| CN111638530A (en) * | 2020-05-27 | 2020-09-08 | 广州蓝胖子机器人有限公司 | Forklift positioning method, forklift and computer readable storage medium |
| CN111638530B (en) * | 2020-05-27 | 2023-09-19 | 广州蓝胖子移动科技有限公司 | Fork truck positioning method, fork truck and computer readable storage medium |
| CN115366102A (en) * | 2022-08-23 | 2022-11-22 | 珠海城市职业技术学院 | Navigation method and system of mobile robot in indoor unknown dynamic environment |
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