CN108225302A - A kind of petrochemical factory's crusing robot alignment system and method - Google Patents
A kind of petrochemical factory's crusing robot alignment system and method 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
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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
- G01C21/165—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 combined with non-inertial navigation instruments
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
- G01S19/46—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being of a radio-wave signal type
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
- G01S19/47—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
- G01S19/49—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system whereby the further system is an inertial position system, e.g. loosely-coupled
Abstract
The invention discloses a kind of petrochemical factory's crusing robot alignment system and methods.System includes GPS observing units, UWB observing units, visual observation unit, laser observations unit, mileage observing unit, combination decision module, inertial states estimation module, can use location observation/SINS composite modules and localization information fusion module.5 observing units acquire respectively observation data respectively, are sent to combination decision module；The environmental map model of combination decision module combining environmental model prior information module storage, carries out the positioning based on multilayer map, and judgement can use observation type；Observation data can will can be used to combine with the correspondence equation of motion that inertial states estimation module is established with location observation/SINS composite modules, combined result is sent to localization information fusion module, carries out final integrated pose estimation.The present invention solves the problems, such as seamless positioning of the crusing robot in the various complex region operations of petrochemical factory, ensure that the continuity of position fixing process and the stability of positioning result.
Description
Technical field
The invention belongs to specialized robot positioning field, a kind of more particularly to petrochemical factory's crusing robot alignment system
And method.
Background technology
The work area of the factories such as petroleum chemical enterprise, oil depot, warehouse ＆ transport corporation is big, environment form is complicated, and security risk is not
Easily investigation.The instrument and meter of the workplaces such as each tank field, pump area, electric substation can only be carried out by manually at present live on foot
Detection.Petrochemical factory needs the content of inspection various, generally includes pipeline road, oil storage pot bottom, pumping plant, metering room, flow
Between, all kinds of workplaces such as flame proof passageway, safe investigation etc. is carried out to various valves, instrument, equipment.These contents to be checked exist
In it is indoor with outdoor, structuring with unstructured, daytime and night, lighting condition is preferable and poor, overcast and rainy wind and snow, in some instances it may even be possible to
It has in the adverse circumstances such as flue dust, steam, huge working strength and danger is brought to patrol task.Using autonomous inspection
Robot replaces manually performing patrol task, can greatly reduce worker's working strength, reduces work risk.
Crusing robot has been achieved with answering in the preferable environment of the structurings condition such as substation, office work region at this stage
With and promote, but the application in petrochemical industry is but made slow progress, the reason is that compared to substation and indoor environment, petrochemical factory
The unstructured degree higher of environment, need inspection content and location type and environment it is more complicated.In addition also to meet daytime and
The boisterous inspection work such as night, sleet.Due to the complexity of patrol task, robot must have very strong positioning energy
Power should be adapted to all kinds of complicated occasions and environmental condition, while necessary strict guarantee positioning accuracy, so that it is guaranteed that robot
It will not be collided in independent navigation and motion process with hazardous equipment or other objects.Running to scene changes, environmental characteristic
When the region of larger change occurs, concussion or failure conditions cannot occur for locating effect.The autonomous crusing robot of petrochemical factory exists
The high-precision of all kinds of complex environments, high reliability positioning play vital work for the independence of robot and safety
With.
The crusing robot of petrochemical industry application at present is less.The common localization method of other field crusing robot have with
Under it is several：
1. magnetic stripe guiding plus radio frequency discrimination RFID auxiliary positioning：Such method guides vehicle and substation for current factory AGV
The common positioning method of crusing robot realizes robot localization by being laid with track and RFID label tag in advance, and major defect is
It needs to arrange magnetic stripe in advance, construction workload and later maintenance cost are high, and polling path is fixed dumb.And it only adapts to and puts down
Smooth road surface, for region bad adaptability with a varied topography.Plant area of petrochemical factory area is big, and Late reformation difficulty is big, and landform is answered
It is miscellaneous, it is navigated using magnetic stripe of high cost, and being accurately positioned under complicated landform can not be met.
2.GNSS/INS integrated positionings：Such method in onboard system, outdoor moving robot and unmanned plane extensive use,
Positioning is combined using high-precision GPS and IMU, major defect is equipment cost height, and there are multipath effects due to GPS
It answers, for the inspection place bad adaptability that equipment and building are stood in great numbers, waiting can not use under occluded environments indoors.Petrochemical industry
Between factory's flow, metering room, the indoor inspection environment such as electric substation it is more, and oil storage tank, pump area's equipment etc. are stood in great numbers, simple to use
GPS location precision is difficult to ensure that.
3. reckoning positions：Such method is mainly used in the fields such as indoor mobile robot, using odometer or is used to
Property measuring unit establish motion model by reckoning carry out pose estimation, major defect is the expansion with motion range,
The accumulated error increase of reckoning, gradually loses stationkeeping ability, and for the rugged bad adaptability in ground.Petrochemical industry work
There are the road surfaces such as rugged, slope for factory, big using only reckoning positioning accumulated error.
4. two-dimensional laser SLAM is positioned：Such method is mainly used in indoor mobile robot and Intelligent Mobile Robot
On, realize synchronous positioning and map structuring using laser SLAM methods, major defect is that adaptive capacity to environment is not strong, dilute to feature
Thin occasion can not be realized and is accurately positioned, for rough ground bad adaptability.There is indoor longer-range corridor, room in petrochemical factory
The few occasion of features such as in outer fire wall, two-dimensional laser SLAM methods can not adapt to such environment, while laser is for rainy day work
The bad adaptability of work.
5. vision SLAM is positioned：Such method is mainly used in the occasions such as domestic sweeper device people, and major defect is to light
It according to sensitivity, can not work at night, the occasions such as quickly rotating in robot easily positions failure.Petrochemical factory's crusing robot needs
It to work at night, realized using vision SLAM difficult.
In addition, also there is part research to realize robot localization for the method for Multi-sensor Fusion, but it is still to be directed to
The integrated positioning application of specific occasion is usually applied under the conditions of sensor signal is stable, status transmission is preferable, for signal
Integrated positioning under loss situation, stable position are indifferent.
In short, at present for petrochemical factory's crusing robot orientation problem, there is no can and meanwhile indoors with it is outdoor,
Flat and rough ground, daytime and night, feature are sparse accurate, stable with all kinds of complicated occasions realizations such as apparent, sleety weather
Positioning crusing robot alignment system.In addition the important point, the equipment that petrochemical factory uses also need to meet explosion-proof peace
Full requirement for all kinds of positioning devices used at this stage, is all difficult to meet and is applied directly to such environmental area.
Invention content
In order to solve the technical issues of above-mentioned background technology proposes, the present invention is intended to provide a kind of petrochemical factory's inspection machine
People's alignment system and method, seamless positioning of the Supplementary Study crusing robot in the various complex region operations of petrochemical factory are asked
Topic, ensures the continuity of position fixing process and the stability of positioning result.
In order to realize above-mentioned technical purpose, the technical scheme is that：
A kind of petrochemical factory's crusing robot alignment system, including GPS observing units, UWB observing units, visual observation list
Member, can use location observation/SINS at laser observations unit, mileage observing unit, combination decision module, inertial states estimation module
Composite module and localization information fusion module；GPS observing units, UWB observing units, visual observation unit, laser observations unit
With mileage observing unit respective observation data are acquired respectively, and combination decision module is sent to specific frequency；Combination decision
Module receives the environmental map model of environmental model prior information module storage, is carried out with reference to each observation data based on multilayer map
Positioning, judgement can use observation type, and available location observation/SINS composite modules can be sent to observation data；It can use
Location observation/SINS composite modules will can carry out group with observation data with the corresponding equation of motion that inertial states estimation module is established
It closes, and combined result is sent to localization information fusion module, carry out final integrated pose estimation.
Based on the localization method of above-mentioned petrochemical factory's crusing robot alignment system, include the following steps：
(1) when robot booting or artificial shift position, the observation z of 5 observing units is initializedi(0) it is and used
The state variable space x of character state estimation modulei(0)；5 observing units acquire observation data z respectivelyi(ki), and with specific frequency
Rate is sent to combination decision module；Subscript i=1,2,3,4,5, represent 5 observing units；
(2) combination decision module combines the observation information of each observing unit and the ring of environmental model prior information module storage
Condition graph model carries out pose according to a preliminary estimate, determines available observation type i, then by available observation data with frequency SiHair
Send observation data zi(ki) and corresponding observing matrix Ci(ki) to available location observation/SINS composite modules；
(3) the corresponding each available observation data z of inertial states estimation module structurei(ki) motion state variable xi(k), will
xi(k) with identical frequency S0It is sent to available location observation/SINS composite modules；
(4) location observation/SINS composite modules can be used by each motion state variable xi(k) with corresponding observing matrix Ci
(ki) be respectively combined, it by interpolation and is extrapolated using virtual observation method by each observing matrix Ci(ki) be to frequency uniformly
S0Observing matrix Ci(k), and local filter is built；
(5) localization information fusion module uses adaptive combined filtering blending algorithm by the estimated result of each local filter
Integrated combination is carried out, obtains final combination pose estimated result.
Further, environmental model prior information module is aligned to obtain GIS electronic maps, visual signature by original map
Map and the cartographic model of laser point cloud map three types superposition.
Further, in step (2), the method for determining available observation type is as follows：
Combination decision module receives the GPS satellite location information of GPS observing units acquisition, in environmental model prior information mould
Coordinate setting is carried out in the GIS electronic maps that block provides, if the continuous t of GPS observing units1Normally in GIS electronic maps in time
Middle positioning, i.e. observation satellite quantity be no less than 4, position dilution of precision be less than 4, then it is assumed that GPS observing units acquisition observation
Data are available observation type；
Combination decision module receives the UWB wireless location information of UWB observing units acquisition, in environmental model prior information mould
Coordinate setting is carried out in the GIS electronic map indoor environments that block provides, if the continuous t of UWB observing units2Normally in GIS in time
It is positioned in electronic map, i.e. RSSI received signal strength PL (d) ＞ Rmin, RminSignal strength threshold for setting, then it is assumed that UWB
The observation data of observing unit acquisition are available observation type；
Combination decision module receives the image feature information of visual observation unit acquisition, in environmental model prior information module
Characteristic matching positioning is carried out in the visual signature map of offer, if by rejecting mistake in the environment that visual observation unit detects
Feature key points after matching are more than given threshold N to number3, then it is assumed that the observation data of visual observation unit acquisition are available
Observe type；
Combination decision module receives the environment point cloud information of laser observations unit acquisition, in environmental model prior information module
The positioning of cloud map registration is provided in the laser point cloud map of offer, if laser observations unit in laser point cloud matching process,
After noise remove erroneous matching, the number of feature points of successful match is more than given threshold N4, then it is assumed that laser observations list
The observation data of member acquisition are available observation type.
Further, inertial states estimation module measures the z-axis acceleration input combination decision perpendicular to robot platform
Module, whether the observation data for judging mileage observing unit are available observation type：
Combination decision module receives the mileage information of mileage observing unit acquisition encoder feedback, believes in environmental model priori
It is positioned in the GIS electronic maps that breath module provides according to reckoning method, if the z-axis of inertial states estimation module output
The acceleration average rate of change is less than given threshold in Δ T timeThe observation data for then thinking mileage observing unit are available
Observe type.
Further, in step (4), if the observation data of GPS observing units acquisition are available observation type, accordingly
Local filter uses the alternate position spike of GPS and SINS positioning and speed difference structure observational equation z1；If the acquisition of UWB observing units
Data are observed as available observation type, corresponding local filter is observed using the location information difference structure of UWB and SINS positioning
Equation z2；If the observation data of visual observation unit acquisition are available observation type, corresponding local filter using vision with
The alternate position spike of SINS positioning and pose difference structure observational equation z3；If the observation data of laser observations unit acquisition are seen to be available
Type is surveyed, corresponding local filter uses laser and the alternate position spike of SINS positioning and posture difference structure observational equation z4；In if
The observation data of journey observing unit are available observation types, and corresponding local filter uses the position of reckoning and SINS positioning
Put difference and posture difference structure observational equation z5。
Further, step (5) is as follows：
(51) the available observation type judged according to combination decision module, determines combined information distribution coefficient βi, setting is respectively
Observation type of topical wave filter initial value can be used；
(52) by globalstate estimationCovariance matrix PfAnd public noise matrix QfIt is assigned to by information sharing principle
In each local filter；
(53) elapsed time synchronize after each available observational equation ziThe movement exported respectively with inertial states estimation module
State variable xiLocal position and posture estimation is carried out, obtains part filter resultWhereinEstimate for local state, Pi
For corresponding covariance matrix, i ∈ [1,5]；
(54) each part filter positioning result is transformed into from local coordinate system under robot geocentric coordinate system, with reference to respectively may be used
With local pose estimated result and senior filter feedback result, estimation is combined using senior filter, global optimum is obtained and estimates
Meter
(55) each local filter and the state estimation and covariance matrix of senior filter are reset, carries out the following iteration period
Pose estimation.
Further, in step (51), the combined information distribution coefficient βiMeet information conservation principle：
Wherein, βmFor the information sharing scheme of senior filter, set according to place feature；
For the unavailable observation type that combination decision module judges, corresponding βi=0, the corresponding β of observation type can be usedi
According to information conservation equation mean allocation.
Further, in step (55), the conversion of local coordinate system to robot geocentric coordinate system is to utilize each observation
What the coordinate transformation relation of the installation site of unit to robot geocentric coordinate system determined.
The advantageous effect brought using above-mentioned technical proposal：
The present invention realizes crusing robot in the interior of petrochemical factory and room by the combination of various positioning subsystems
Outside, it is flat sparse accurate, steady with all kinds of complicated occasions realizations such as apparent, sleety weather with rough ground, daytime and night, feature
Fixed application ensure that the seamless positioning under the whole scene full working scope of robot.By the way that the data of all kinds of observation sensors are first
Estimation is integrated with inertial states estimation module, obtains the observational equation that frequency is inertial states estimation module output frequency,
It ensure that the synchronization of different frequency sensor observation.It is realized using adaptive combined filtering algorithm and sentenced automatically under various circumstances
Disconnected available location observation type, positioning is combined using sensor appropriate under this scene, obtain less than exclusive use its
In any positioning method error locating effect, ensure that the accuracy of seamless positioning.
Description of the drawings
Fig. 1 is the block diagram of system of the present invention；
Fig. 2 is flow chart of the method for the present invention；
Fig. 3 is the adaptive combined filtering blending algorithm schematic diagram towards patrol task；
Fig. 4 is the step flow chart of adaptive combined filtering algorithm；
Major Symbol explanation：
1st, GPS observing units；2nd, UWB observing units；3rd, visual observation unit；4th, laser observations unit；5th, mileage is observed
Unit；6th, environmental model prior information module；7th, combination decision module；8th, inertial states estimation module；9th, can use location observation/
SINS composite modules；10th, localization information fusion module.
Specific embodiment
Below with reference to attached drawing, technical scheme of the present invention is described in detail.
As shown in Figure 1, petrochemical factory's crusing robot alignment system that the present invention designs, including GPS observing units 1, UWB
Observing unit 2, visual observation unit 3, laser observations unit 4, mileage observing unit 5, environmental model prior information module 6, group
It closes decision-making module 7, inertial states estimation module 8, location observation/SINS composite modules 9 and localization information fusion module 10 can be used.
Combination decision module 7 receives the environmental map model of 6 storage of environmental model prior information module, with reference to each observation number
According to the positioning based on multilayer map is carried out, judgement can use observation type, and can be sent to observation data available location observation/
SINS composite modules 9；It can be established with location observation/SINS composite modules 9 with observation data with inertial states estimation module 8
The correspondence equation of motion be combined, and combined result is sent to localization information fusion module 10, carry out final integrated position
Appearance is estimated.
As shown in Fig. 2, the localization method based on above-mentioned alignment system, is as follows.
Step S21, robot booting or during artificial shift position, the observation z of initialization observing unit 1-5i(0) with
And the state variable space x of inertial states estimation module 8i(0).Each observing unit 1-5 acquires observation information z respectivelyi(ki), with
Specific frequency is sent to combination decision module 7.
GPS observing units 1 are when robot operates in outdoor and unobstructed environment and GPS signal can be received continuously, automatically
Acquisition GPS signal is simultaneously sent to combination decision module 7.In the present embodiment, transmission frequency 1Hz.
UWB observing units 2 operate in the indoor environment and UWB signal for laying UWB wireless sensor networks in advance in robot
When can continuously receive, automatic collection UWB signal is simultaneously sent to combination decision module 7.In the present embodiment, transmission frequency is set as
1Hz。
For visual observation unit 3 in robot operational process, automatic collection image information is simultaneously sent to combination decision module
7.In the present embodiment, transmission frequency 20Hz.
For laser observations unit 4 in robot operational process, automatic collection laser point cloud information is simultaneously sent to combination decision
Module 7.In the present embodiment, frequency acquisition is set as 10Hz.
For mileage observing unit 5 in robot operational process, automatic collection mileage is simultaneously sent to combination decision module
7.In the present embodiment, frequency acquisition is set as 20Hz.
Environmental model prior information module 6 is aligned to obtain GIS electronic maps, visual signature map, swash by original map
The cartographic model of luminous point cloud map three types map superposition.In the present embodiment, GIS electronic maps utilize ARCGIS softwares two
Secondary exploitation structure plant area's three-dimensional space data library, on the basis of remote sensing aerial survey image GIS electronic maps, by indoor UWB anchor nodes
Environment under signal cover is stored using artificial surveying and mapping data and the GIS electronic maps that are added to；Sparse spy therein
Sign point map carries out sparse environmental modeling by using vision ORB-SLAM and obtains and be added on GIS electronic maps；Wherein
Laser point cloud map obtain and fold by using laser odometer LOAM (Lidar Odometry and Mapping) method
It is added on GIS electronic maps.The all types of sensing datas acquired during drawing, which synchronize, to carry out and arrives generation by the way that coordinate conversion is unified
Under boundary's coordinate system, the environmental model that three layers of map are superimposed and are aligned is obtained with this.
Step S22, combination decision module 7 combine each observing unit observation information and environmental model prior information into line position
Appearance is estimated for the first time, determines available observation type i (i expressions can use observing unit serial number), then can use observation i with frequency Si
Send observation zi(ki) to available location observation/SINS composite modules 9.
Combination decision module 7 is according to the sensing data and environment mould of the acquisition of each location observation unit received
The multilayer cartographic information of type prior information module 6, determines currently available sensor type.Specific method is as follows：
Combination decision module 7 receives the GPS satellite location information of 1 acquisition of GPS observing units, in environmental model prior information
Coordinate setting is carried out in the GIS electronic maps that module 6 provides, judges assembled scheme whether comprising GPS observation informations.If GPS is seen
Survey the continuous t of unit1Normally (observation satellite quantity is no less than 4, position dilution of precision for positioning in GIS electronic maps in time
Less than 4), then it is assumed that the assembled scheme of current kinetic position includes GPS observation informations.Combination decision module is with frequency S1It sends and sees
Survey z1(k1), corresponding observing matrix C1(k1), observation noise v1(k1)。
Combination decision module 7 receives the UWB wireless location information of 2 acquisition of UWB observing units, in environmental model prior information
Coordinate setting is carried out in the GIS electronic map indoor environments that module 6 provides, judges assembled scheme whether comprising UWB observation informations.
If the continuous t of UWB observing units2(RSSI received signal strength PL (d) ＞ R are normally positioned in time in GIS electronic mapsmin),
RminSignal strength threshold for setting, then it is assumed that the assembled scheme of current kinetic position includes UWB observation informations.Combination decision
Module is with frequency S2Send observation z2(k2), corresponding observing matrix C2(k2), observation noise v2(k2)。
Combination decision module 7 receives the image feature information of 3 acquisition of visual observation unit, in environmental model prior information mould
Characteristic matching positioning is carried out in the characteristics map that block 6 provides, judges assembled scheme whether comprising visual observation information.If vision is seen
The feature critical in the environment that unit 3 detects after rejecting erroneous matching is surveyed to count out more than specified threshold N3, then it is assumed that
The assembled scheme of current kinetic position includes visual observation information.Combination decision module is with frequency S3Observe z3(k3), corresponding observation
Matrix C3(k3), observation noise v3(k3).As preference, characteristic matching uses ORB characteristic points, stochastical sampling consistency is utilized
Algorithm (RANSAC) rejects the registration point pair of mistake, realizes that robot pose is estimated using the station-keeping mode of ORB-SLAM.
Combination decision module 7 receives the environment point cloud information that the laser observations unit 4 acquires, in environmental model priori
A cloud map registration positioning is carried out in the point cloud map that information module 6 provides, judges whether assembled scheme is believed comprising laser observations
Breath.If laser observations unit 4 is in laser point cloud matching process, after noise remove erroneous matching, the feature of successful match
Point is more than specified threshold N to quantity4, then it is assumed that the assembled scheme of current kinetic position includes laser observations information.Combination decision
Module is with frequency S4Send observation z4(k4), corresponding observing matrix C4(k4), observation noise v4(k4).In the present embodiment, feature
Matching process is carried out, with putting cloud using kd tree K-NN search method as feature using inquiry vertex neighborhood normal vector during matching
Figure registration realizes that robot pose is estimated using iteration closest approach (ICP) method.
Combination decision module 7 receives the mileage information of the encoder feedback of 5 acquisition of mileage observing unit, in environmental model elder generation
It tests in the GIS electronic maps of the offer of information module 6 and is positioned according to reckoning method, judge assembled scheme whether comprising inner
Journey observation information.If the z-axis acceleration average rate of change that inertial states estimation module 8 exports is less than setting threshold in Δ T time
ValueThen think that the assembled scheme of current kinetic position includes mileage observation information.Combination decision module is with frequency S5Observe z5
(k5), corresponding observing matrix C5(k5), observation noise v5(k5)。
Step S23, inertial states estimation module builds corresponding each observation observation z respectivelyi(ki) state space variable xi
(k), state-transition matrix Ai(k), system noise item wi(k).By each motion prediction xi(k) with identical frequency S0Being sent to can
With location observation/SINS composite modules (9).
Inertial states estimation module 8 is according to different observation structure robot motion's predicted state information xi(k), it is input to
Multisensor synchronization module 9.The z-axis acceleration input combination decision module 7 perpendicular to robot platform is measured simultaneously, judges group
Whether conjunction scheme includes mileage observation information.In the present embodiment, inertial states estimation transmission frequency S0=100Hz.
Step S24, it can be transported with each SINS that location observation/SINS composite modules (9) send inertial states estimation module
Dynamic prediction xi(k), the C that can be sent with each different frequency with observing uniti(ki) be compared respectively, utilize virtual observation method
It is S to obtain scale0Same frequency observed result Ci(k), local filter is built.
Location observation/SINS composite modules 9 can be used by the observation data C of each available observing uniti(ki) respectively with inertia shape
The x of state estimation module (8)i(k) structure local filter is combined, after discretization, system is：
xi(k+1)=Ai(k)xi(k)+Γi(k)wi(k)
zi(ki)=Ci(ki)xi(ki)+vi(ki)
Wherein, xi(ki)=x (ni(ki-1)+1)；niFor SINS and the sample rate ratio of each observing unit frequency；xi(k)
Position and posture, w are estimated for corresponding i-th kind of method of estimationi(k) it is process noise matrix, Γi(k) it is noise inputs matrix,
Corresponding process noise variance matrix is Qi(k)；zi(ki) for observational equation, Ci(ki) represent observing matrix；Observation error vi(ki) be
Zero mean Gaussian white noise, variance Ri(ki)。
It is obtained using virtual observation method by interpolation and extrapolation by under the observing frequency unification to SINS of each observing unit
The respectively location estimation equation under observation：
xi(k+1)=Ai(k)xi(k)+Γi(k)wi(k)
zi(k)=Ci(k)xi(k)+vi(k)
Wherein, The estimation of the common conditions of each subfilters for various observation structures, i.e. it is fixed in SINS
Position position, speed and attitude error and the state space of gyroscope and accelerometer bias.It is distinctive for i-th of wave filter
State estimation.xcEstimate to obtain optimal global estimation, x by associated filters two-stage suboptimal filteringsiIt is carried out in subfilter
Primary filtering output estimation value.
The state equation of local filter system can be expressed as：
Wherein,
The platform stance error of SINS systemsVelocity errorSite errorGyroscope constant value zero drift
εT, accelerometer constant errorGyroscope white noiseAccelerometer white noise
Local filter 1 uses the alternate position spike that GPS and SINS is formed and speed difference structure observational equation z1：
z1(k)=C1(k)x1(k)+v1(k)
x1(k)=xc(k)
Wherein,For the velocity error of GPS,For site error.
The location information difference structure observational equation z that local filter 2 is obtained using UWB and SINS positioning2：
z2(k)=C2(k)x2(k)+v2(k)
x2(k)=xc(k)
Wherein,For UWB attitude errors.
Local filter 3 uses vision positioning and the alternate position spike of SINS positioning and pose difference structure observational equation z3：
z3(k)=C3(k)x3(k)+v3(k)
Wherein,For the Ocular measure factor,For the attitude error of camera location estimation,Position for camera location estimation
Put error.
Local filter 4 uses laser positioning and the alternate position spike of SINS positioning and posture difference structure observational equation z4：
z4(k)=C4(k)x4(k)+v4(k)
Wherein,For the attitude error of laser radar location estimation,Site error for laser radar location estimation.
Local filter 5 uses the posture difference and alternate position spike of reckoning and SINS structure observational equation z5：
z5(k)=C5(k)x5(k)+v5(k)
Wherein,For reckoning attitude error,For site error, δ K are odometer scale coefficient error.
Step S25, localization information fusion module 10 uses a kind of adaptive combined filtering towards patrol task
(Federated Filter) blending algorithm estimates the local filter of each observation structureIt carries out
Integrated combination obtains final combination pose estimated result
Fig. 3 is the adaptive combined filtering blending algorithm schematic diagram towards patrol task, is sent by each observing unit 1-5
Observation information z1~z5, the state estimation information x with the transmission of inertial states estimation module 81~x5It is respectively combined,
It is input to corresponding local filter.Local filter i combination equations of motion xi, observational equation ziAnd the senior filter letter of feedback
BreathCarry out part filter location estimation.The part filter result of output carries out Federated filter by senior filter,
Obtain final pose estimation output
Fig. 4 is the step flow chart of adaptive combined filtering algorithm, and detailed process is：
Step S41, the available observation type judged according to combination decision module 7, determines combined information distribution coefficient βiIf
Put each available observation local filter initial value.
Combined information distribution coefficient βiMeet information conservation principle：
Wherein βmFor the information sharing scheme of senior filter, (0.1 ＜ β are manually specified according to the characteristics of experimental sitem
＜ 0.3).For the unavailable observation that combination decision module 7 judges, corresponding βi=0, the corresponding β of observation can be used in remainingiAccording to
Information conservation equation is distributed equally.
The covariance P of each available local filter and senior filter, noise variance R initial value designs are：
Step S42, by globalstate estimationCovariance matrix PfAnd public noise matrix QfBy information sharing principle point
It is fitted in each local filter：
Step S43, the elapsed time synchronize after each available observation ziRespectively with the motion state x of SINSiCarry out local pose
State estimation obtains available part filter result
Local filter and senior filter carry out status predication and update respectively：
Prediction process：
Renewal process：
Pi(k | k)=[In-Ki(k)Ci(k)]Pi(k|k-1)
InIt is unit matrix；
Senior filter updates：
Pm(k+1)=Pm(k|k-1)
Step S44, each part filter positioning result is transformed into from local coordinate system under robot geocentric coordinate system, with reference to
Each available local pose estimated result and senior filter feedback result, are combined estimation using senior filter and obtain global optimum
Estimation
Local coordinate system is sat to the conversion of robot geocentric coordinate system using each sensor mounting location to robot barycenter
The coordinate transformation relation of mark system is determined.
Senior filter carries out adaptive combined information fusion：
Step S45, local coordinate system where the fusion positioning result of senior filter being converted back each sensor coordinate system, profit
Each local filter and the location estimation value and covariance matrix of senior filter are reset with coordinate transform, carries out the following iteration period
Pose estimation.
Embodiment is merely illustrative of the invention's technical idea, it is impossible to protection scope of the present invention is limited with this, it is every according to
Technological thought proposed by the present invention, any change done on the basis of technical solution, each falls within the scope of the present invention.
Claims (9)
1. a kind of petrochemical factory's crusing robot alignment system, which is characterized in that single including GPS observing units (1), UWB observations
First (2), visual observation unit (3), laser observations unit (4), mileage observing unit (5), combination decision module (7), inertia shape
State estimation module (8) can use location observation/SINS composite modules (9) and localization information fusion module (10)；GPS observing units
(1), UWB observing units (2), visual observation unit (3), laser observations unit (4) and mileage observing unit (5) acquire respectively respectively
From observation data, and combination decision module (7) is sent to specific frequency；It is first that combination decision module (7) receives environmental model
The environmental map model of information module (6) storage is tested, the positioning based on multilayer map is carried out with reference to each observation data, judges available
Type is observed, and available location observation/SINS composite modules (9) can be sent to observation data；Location observation/SINS can be used
Composite module (9) can be combined with observation data with the corresponding equation of motion that inertial states estimation module (8) is established, and will
Combined result is sent to localization information fusion module (10), carries out final integrated pose estimation.
2. the localization method based on petrochemical factory's crusing robot alignment system described in claim 1, which is characterized in that including with
Lower step：
(1) when robot booting or artificial shift position, the observation z of 5 observing units (1-5) is initializedi(0) it is and used
The state variable space x of character state estimation module (8)i(0)；5 observing units (1-5) acquire observation data z respectivelyi(ki), and
Combination decision module (7) is sent to specific frequency；Subscript i=1,2,3,4,5, represent 5 observing units；
(2) observation information of each observing unit of combination decision module (7) combination and environmental model prior information module (6) store
Environmental map model carries out pose according to a preliminary estimate, determines available observation type i, then by available observation data with frequency Si
Send observation data zi(ki) and corresponding observing matrix Ci(ki) to available location observation/SINS composite modules (9)；
(3) the corresponding each available observation data z of inertial states estimation module (8) structurei(ki) motion state variable xi(k), by xi
(k) with identical frequency S0It is sent to available location observation/SINS composite modules (9)；
(4) location observation/SINS composite modules (9) can be used by each motion state variable xi(k) with corresponding observing matrix Ci(ki)
It is respectively combined, by interpolation and is extrapolated using virtual observation method by each observing matrix Ci(ki) unify to frequency to be S0's
Observing matrix Ci(k), and local filter is built；
(5) localization information fusion module (10) uses adaptive combined filtering blending algorithm by the estimated result of each local filter
Integrated combination is carried out, obtains final combination pose estimated result.
3. localization method according to claim 2, which is characterized in that environmental model prior information module (6) is by primitively
Figure alignment obtains the cartographic model of GIS electronic maps, visual signature map and the superposition of laser point cloud map three types.
4. localization method according to claim 3, which is characterized in that in step (2), determine the available method for observing type
It is as follows：
Combination decision module (7) receives the GPS satellite location information that GPS observing units (1) acquire, in environmental model prior information
Coordinate setting is carried out in the GIS electronic maps that module (6) provides, if the continuous t of GPS observing units1Normally in GIS electronics in time
Positioned in map, i.e., observation satellite quantity be no less than 4, position dilution of precision be less than 4, then it is assumed that GPS observing units (1) acquire
Observation data be available observation type；
Combination decision module (7) receives the UWB wireless location information that UWB observing units (2) acquire, in environmental model prior information
Coordinate setting is carried out in the GIS electronic map indoor environments that module (6) provides, if the continuous t of UWB observing units2It is normal in time
It is positioned in GIS electronic maps, i.e. RSSI received signal strength PL (d) ＞ Rmin, RminFor the signal strength threshold of setting, then recognize
Observation data for UWB observing units (2) acquisition are available observation type；
Combination decision module (7) receives the image feature information that visual observation unit (3) acquires, in environmental model prior information mould
Characteristic matching positioning is carried out in the visual signature map that block (6) provides, if passing through in the environment that visual observation unit (3) detects
It rejects the feature key points after erroneous matching and given threshold N is more than to number3, then it is assumed that the sight of visual observation unit (3) acquisition
Measured data is available observation type；
Combination decision module (7) receives the environment point cloud information that laser observations unit (4) acquires, in environmental model prior information mould
A cloud map registration positioning is carried out in the laser point cloud map that block (6) provides, if laser observations unit (4) is matched in laser point cloud
In the process, after noise remove erroneous matching, the number of feature points of successful match is more than given threshold N4, then it is assumed that laser
The observation data of observing unit (4) acquisition are available observation type.
5. localization method according to claim 3, which is characterized in that inertial states estimation module (8) is measured perpendicular to machine
The z-axis acceleration input combination decision module (7) of people's platform, whether the observation data for judging mileage observing unit (5) are available
Observe type：
Combination decision module (7) receives the mileage information that mileage observing unit (5) acquires encoder feedback, in environmental model priori
It is positioned in the GIS electronic maps that information module (6) provides according to reckoning method, if inertial states estimation module (8)
The z-axis acceleration average rate of change of output is less than given threshold in Δ T timeThen think the sight of mileage observing unit (5)
Measured data is available observation type.
6. localization method according to claim 2, which is characterized in that in step (4), if GPS observing units (1) acquisition
Data are observed as available observation type, corresponding local filter is built using the alternate position spike and speed difference of GPS and SINS positioning
Observational equation z1；If the observation data of UWB observing units (2) acquisition are available observation type, corresponding local filter uses
The location information difference structure observational equation z of UWB and SINS positioning2；If the observation data of visual observation unit (3) acquisition are available
Type is observed, corresponding local filter uses vision and the alternate position spike of SINS positioning and pose difference structure observational equation z3；
If the observation data of laser observations unit (4) acquisition are available observation type, corresponding local filter uses laser and SINS
Alternate position spike and posture difference structure the observational equation z of positioning4；If the observation data of mileage observing unit (5) are available observation types,
Corresponding local filter uses reckoning and the alternate position spike of SINS positioning and posture difference structure observational equation z5。
7. localization method according to claim 6, which is characterized in that step (5) is as follows：
(51) the available observation type judged according to combination decision module (7), determines combined information distribution coefficient βi, set and respectively may be used
With observation type of topical wave filter initial value；
(52) by globalstate estimationCovariance matrix PfAnd public noise matrix QfIt is assigned to by information sharing principle each
In local filter；
(53) elapsed time synchronize after each available observational equation ziThe movement shape exported respectively with inertial states estimation module (8)
State variable xiLocal position and posture estimation is carried out, obtains part filter resultWhereinEstimate for local state, PiFor
Corresponding covariance matrix, i ∈ [1,5]；
(54) each part filter positioning result is transformed into from local coordinate system under robot geocentric coordinate system, with reference to each available office
Position appearance estimated result and senior filter feedback result, are combined estimation using senior filter, obtain global best estimates
(55) each local filter and the state estimation and covariance matrix of senior filter are reset, carries out the position in following iteration period
Appearance is estimated.
8. localization method according to claim 7, which is characterized in that in step (51), the combined information distribution coefficient βi
Meet information conservation principle：
Wherein, βmFor the information sharing scheme of senior filter, set according to place feature；
For the unavailable observation type that combination decision module (7) judges, corresponding βi=0, the corresponding β of observation type can be usediIt presses
According to information conservation equation mean allocation.
9. localization method according to claim 7, which is characterized in that in step (55), local coordinate system to machine hostage
The conversion of heart coordinate system is to utilize the installation site of each observing unit (1-5) to the coordinate transformation relation of robot geocentric coordinate system
Determining.
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| CN112214019A (en) * | 2020-09-21 | 2021-01-12 | 国网浙江省电力有限公司 | Non-blind area intelligent feedback control system, method and terminal for unmanned inspection equipment |
| CN112180405B (en) * | 2020-11-27 | 2021-02-23 | 北京建筑大学 | GPS-based method for positioning and determining postures of sporters in mountain snowfield |
| CN112180405A (en) * | 2020-11-27 | 2021-01-05 | 北京建筑大学 | GPS-based method for positioning and determining postures of sporters in mountain snowfield |
| CN113255600A (en) * | 2021-06-29 | 2021-08-13 | 上海影创信息科技有限公司 | Point cloud map updating optimization method, system, medium and equipment based on client |
| CN113255600B (en) * | 2021-06-29 | 2021-10-01 | 上海影创信息科技有限公司 | Point cloud map updating optimization method, system, medium and equipment based on client |
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