CN108931245A - The local method for self-locating and equipment of mobile robot - Google Patents
The local method for self-locating and equipment of mobile robot Download PDFInfo
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- CN108931245A CN108931245A CN201810872628.9A CN201810872628A CN108931245A CN 108931245 A CN108931245 A CN 108931245A CN 201810872628 A CN201810872628 A CN 201810872628A CN 108931245 A CN108931245 A CN 108931245A
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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
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- 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
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
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Abstract
This application provides the local method for self-locating and equipment of a kind of mobile robot, the motion state of mobile robot can be determined according to mileage information and acceleration information, when motion state is abnormality, obtain the particle distribution information for predicting mobile robot pose, particle is clustered again, the corresponding pose of particle weights mean value in the maximum classification of particle weights mean value is determined as to predict pose, and prediction pose map is matched with current time laser observations data, determine the pose of observation matching degree and mobile robot, and when observing matching degree less than first threshold, redefine the pose of observation matching degree and mobile robot, so as to larger with map discrepancies in current environment, or there is wheel slip in mobile robot, when the abnormal conditions such as idle running, realize the accurate self-positioning of mobile robot, it keeps away Exempt from the problem of localization for Mobile Robot loss occur, scheme is simple, easily realizes, improves the accuracy of Mobile robot self-localization.
Description
Technical field
This application involves the local method for self-locating of mobile robot technology field more particularly to a kind of mobile robot and
Equipment.
Background technique
It is also more next to the research of mobile robot the relevant technologies with the development and further landing of mobile robot technology
It is more deep, since the working environment of mobile robot has unstructured and uncertain, the autonomous intelligence of mobile robot
Energy Journal of Sex Research becomes of crucial importance.Mobile robot self-localization technology, as mobile robot research three big hot spot technologies it
One, obtain extensive concern.
Currently, common Mobile robot self-localization algorithm has based on odometer reckoning, the landmark identification of view-based access control model, base
In a variety of method for self-locating such as the Global localizations, gyroscopic navigation, GPS of map match, every kind of technology has the advantages that respective and office
It is sex-limited.Although calculating that short-term accuracy is high, at low cost based on odometer, it is not avoided that the unlimited accumulation of error.Gyroscopic navigation without
External reference is needed, but has drift at any time, is not suitable for prolonged be accurately positioned.GPS positioning is vulnerable to factors such as precision, safety
Limitation, and the problems such as be not available there are interior.Vision positioning is easy to be influenced by illumination, deformation, high-speed motion etc., and counts
It is higher to calculate complexity.
Chinese patent " a kind of Position Fixing Navigation System of container Automatic Guided Vehicle of CN201397390Y " using inertial navigation,
GPS and laser positioning, which combine, realizes the self-positioning of mobile robot, although positioning real-time is good, precision is high, is still to rely on
GPS is just able to achieve self-positioning, and this method cannot be used in indoor environment.In addition, currently employed inertial navigation system and laser positioning
In conjunction with the Robot Self-Localization realized based on the self-positioning algorithm in adaptive Monte Carlo, in current environment and map
It differs greatly or when mobile robot the abnormal conditions such as wheel slip, idle running occurs, can not achieve the accurate of mobile robot
It is self-positioning, it is easy to appear the problems such as positioning is lost.
Apply for content
The purpose of the application is to provide the local method for self-locating and equipment of a kind of mobile robot, existing to solve
Have caused by mobile robot differs larger with existing map because of situations such as skidding, idle running and observing environment in technology and locally determines
The inaccurate problem in position.
To achieve the above object, this application provides a kind of local method for self-locating of mobile robot, wherein the shifting
Mobile robot includes inertial navigation system and laser radar, and the inertial navigation system includes odometer, accelerometer and gyro
Instrument, this method comprises:
According to the mileage information and acceleration information obtained from inertial navigation system, the movement shape of mobile robot is determined
State；
The motion state be abnormality when, according to obtained from inertial navigation system mileage information, acceleration believe
Breath and angle change information, obtain the particle distribution information for predicting mobile robot pose；
Particle is clustered according to the particle distribution information, and particle in the maximum classification of particle weights mean value is weighed
The corresponding pose of weight-average value is determined as predicting pose；
The corresponding map of the prediction pose is matched with current time laser observations data, determines observation matching degree
With the pose of mobile robot；
If observing matching degree is less than first threshold, according to the corresponding moveable robot movement range of matching observation window, weight
The new pose for determining observation matching degree and mobile robot, the matching observation window includes having by what laser radar obtained
The multiframe laser observations data of sequential relationship.
Further, according to the mileage information and acceleration information obtained from inertial navigation system, mobile robot is determined
Motion state, comprising:
If the mileage information obtained from odometer continues to increase and the acceleration information variation obtained from accelerometer is less than
When second threshold, the motion state of mobile robot is determined as abnormality.
Further, it according to mileage information, acceleration information and the angle change information obtained from inertial navigation system, obtains
It takes in the particle distribution information of prediction mobile robot pose, comprising:
It is obtained according to the mileage information obtained from odometer, from the acceleration information of accelerometer acquisition and from gyroscope
Angle change information obtains the particle distribution for predicting mobile robot pose by the self-positioning algorithm in adaptive Monte Carlo
Information.
Further, the particle for predicting mobile robot pose is obtained by the self-positioning algorithm in adaptive Monte Carlo
Distributed intelligence, comprising:
According to mileage information, acceleration information and angle change information, initialize for predicting mobile robot pose
Particle distribution information；
Particle position is updated according to mileage information；
The current time laser observations data obtained from laser radar are matched with the map of particle position, root
Particle weights are determined according to matching result；
Particle resampling is carried out according to particle weights, the particle distribution information after obtaining resampling.
Further, particle position is updated according to mileage information, comprising:
According to the particle distribution information of last moment, believed by the particle distribution that preset motion model obtains current time
Breath.
Further, particle resampling is carried out according to particle weights, comprising:
According to the particle weights of last moment, sampling particle is regenerated by stochastic sampling strategy.
Further, particle is clustered according to the particle distribution information, and by maximum point of particle weights mean value
The corresponding pose of particle weights mean value is determined as predicting pose in class, comprising:
According to the particle distribution information, particle is clustered by unsupervised learning clustering algorithm, obtains particle point
Class；
According to particle classifying, the wherein maximum classification of particle weights mean value is obtained；
The corresponding pose of particle weights mean value in the maximum classification of particle weights mean value is determined as to predict pose.
Further, the corresponding map of the prediction pose is matched with current time laser observations data, is determined
Observe the pose of matching degree and mobile robot, comprising:
The corresponding map of the prediction pose and current time laser observations data are carried out by iteration closest approach algorithm
Point cloud matching obtains the pose of observation matching degree and mobile robot.
Further, according to the corresponding moveable robot movement range of matching observation window, mobile robot is redefined
Pose, comprising:
According to the mobile robot pose of multiframe laser observations data in matching observation window, moveable robot movement is determined
Range；
According to the moveable robot movement range, the parameter and acquisition for adjusting the predetermined movement model of particle distribution are used for
Predict the particle distribution information of mobile robot pose；
Particle is clustered according to the particle distribution information, and particle in the maximum classification of particle weights mean value is weighed
The corresponding pose of weight-average value is determined as predicting pose；
The corresponding map of the prediction pose is matched with laser observations data in observation window are matched, determines observation
The pose of matching degree and mobile robot.
Further, the laser observations data are the laser scanning information comprising angle and distance data.
Further, this method further include:
When the motion state is abnormality, the quantity of laser observations data in matching observation window is reduced.
Further, this method further include:
When present laser observes the matching degree of data and map greater than third threshold value, increase laser in matching observation window
Observe the quantity of data.
Present invention also provides a kind of local self-locating devices of mobile robot, which includes for storing computer
The memory of program instruction and processor for executing program instructions, wherein when the computer program instructions are by the processor
When execution, the equipment is made to execute the local method for self-locating of aforementioned mobile robot.
Present invention also provides a kind of computer-readable mediums, are stored thereon with computer-readable instruction, the computer
Readable instruction can be executed by processor the local method for self-locating to realize aforementioned mobile robot.
Compared with prior art, scheme provided by the present application can determine moving machine according to mileage information and acceleration information
The motion state of device people obtains the particle distribution for predicting mobile robot pose and believes when motion state is abnormality
Breath, then particle is clustered, the corresponding pose of particle weights mean value in the maximum classification of particle weights mean value is determined as pre-
Location appearance, and will predict that the corresponding map of pose is matched with current time laser observations data, determine observation matching degree and
The pose of mobile robot, and when observing matching degree less than first threshold, redefine observation matching degree and mobile robot
Pose, so as to current environment and map discrepancies are larger or mobile robot wheel slip, idle running etc. occur abnormal
When situation, the accurate self-positioning of mobile robot is realized, avoid the occurrence of the problem of localization for Mobile Robot is lost, scheme is simple,
It easily realizes, improves the accuracy of Mobile robot self-localization.
Detailed description of the invention
By reading a detailed description of non-restrictive embodiments in the light of the attached drawings below, the application's is other
Feature, objects and advantages will become more apparent upon:
Fig. 1 is a kind of flow chart of the local method for self-locating of mobile robot provided by the embodiments of the present application.
Fig. 2 is the sight that a kind of preferred mobile robot present laser provided by the embodiments of the present application observes data and map
Survey matching degree schematic diagram.
Fig. 3 is a kind of schematic diagram of the local self aligning system of preferred mobile robot provided by the embodiments of the present application.
Specific embodiment
The application is described in further detail with reference to the accompanying drawing.
In a typical configuration of this application, terminal, the equipment of service network and trusted party include one or more
Processor (CPU), input/output interface, network interface and memory.
Memory may include the non-volatile memory in computer-readable medium, random access memory (RAM) and/or
The forms such as Nonvolatile memory, such as read-only memory (ROM) or flash memory (flash RAM).Memory is computer-readable medium
Example.
Computer-readable medium includes permanent and non-permanent, removable and non-removable media can be by any method
Or technology come realize information store.Information can be computer readable instructions, data structure, the module of program or other data.
The example of the storage medium of computer includes, but are not limited to phase change memory (PRAM), static random access memory (SRAM), moves
State random access memory (DRAM), other kinds of random access memory (RAM), read-only memory (ROM), electric erasable
Programmable read only memory (EEPROM), flash memory or other memory techniques, read-only disc read only memory (CD-ROM) (CD-ROM),
Digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or
Any other non-transmission medium, can be used for storage can be accessed by a computing device information.As defined in this article, computer
Readable medium does not include non-temporary computer readable media (transitory media), such as the data-signal and carrier wave of modulation.
Some embodiments of the present application provide a kind of local method for self-locating of mobile robot.The mobile robot can
Including inertial navigation system and laser radar.Inertial navigation system may include but be not limited to odometer, accelerometer and gyroscope.
Laser radar can include but is not limited to single line laser radar and multi-line laser radar.As shown in Figure 1, this method specifically include as
Lower step:
Step S101 determines mobile robot according to the mileage information and acceleration information obtained from inertial navigation system
Motion state；
Step S102, the motion state be abnormality when, according to the mileage information obtained from inertial navigation system,
Acceleration information and angle change information obtain the particle distribution information for predicting mobile robot pose；
Step S103 clusters particle according to the particle distribution information, and by maximum point of particle weights mean value
The corresponding pose of particle weights mean value is determined as predicting pose in class；
Step S104 matches the corresponding map of the prediction pose with current time laser observations data, determines
Observe the pose of matching degree and mobile robot；
Step S105 is transported if observation matching degree is less than first threshold according to the corresponding mobile robot of matching observation window
Dynamic range, redefines the pose of observation matching degree and mobile robot, and the matching observation window includes passing through laser radar
The multiframe laser observations data with sequential relationship obtained.
The program is especially suitable for the self-positioning scene in part of mobile robot, can be according to mileage information and acceleration
Information judges whether mobile robot is in abnormal motion state, if abnormal motion state is in, according to inertial navigation system
The information provided with laser radar is self-positioning to mobile robot progress part, redefines the current pose of mobile robot.
Here, the local method for self-locating of the program, is to pass through the offset of the opposite starting point from a determining starting point
Come determine mobile robot pose method.
In step s101, according to the mileage information and acceleration information obtained from inertial navigation system, moving machine is determined
The motion state of device people.Here, mileage information is obtained from the odometer in inertial navigation system, acceleration information is from inertial navigation
Accelerometer in system obtains.Mileage information is code-disc (wheel revolution) statistical information, as long as wheel rotates, will be done corresponding
Accumulation, mileage information reflection be relative to last moment on two-dimensional surface x, the variation of y-coordinate and mobile robot court
To variation.Accelerometer is mainly provided in the directional acceleration information on three-dimensional (x, y, z).Gyroscope mainly provides phase
To towards changing, i.e., gyroscope obtains angle by integral, further according to the gravity direction that accelerometer detects, finally by
Kalman filter obtains final angle.
In the real work of mobile robot, it may occur that a variety of abnormal conditions, for example, sky has occurred in mobile robot
The case where turning, skidding, i.e. wheel are turning but the position of robot there is no mobile or wheel occurs does not turn still robot
Position have occurred movement, skid, idle running when, often all can error accumulation mileage so that robot localization mistake.The application's
In some embodiments, auxiliary information is provided by acceleration information to judge whether robot is in situ, such as accelerometer
Do not change in the direction x, y of two-dimensional surface, can determine that the robot is in situ.In another example if listening to odometer always
Variation but accelerometer can be used as when robot skid foundation when not having significant change,
Specifically, if the acceleration letter for continuing to increase from the mileage information that odometer obtains and being obtained from accelerometer
When breath variation is less than second threshold, then the motion state of mobile robot is determined as abnormality.Here, mileage information is lasting
Increase illustrates that the wheel of mobile robot is rotating always, but the information change that accelerometer detects is not obvious, second
Threshold value is the preset value of a very little, for illustrating the variation degree of acceleration information, if the variation of acceleration information is less than
Second threshold, it is believed that the acceleration information of mobile robot is without too big variation.Therefore, the wheel of mobile robot has occurred
Son is in the case where rotation and acceleration have no change, then it is assumed that the mobile robot is located at abnormality, to be transported
Dynamic state is determined as abnormality.
In addition, mobile robot, which may also will appear mileage information, changes unobvious or very little, but acceleration information is held
The case where continuous a period of time, such case illustrates that the wheel rotation amplitude of mobile robot is smaller, but acceleration change is larger,
The mobile robot may skid, and the mobile robot also is located at abnormality at this time, can also be determined as its motion state
Abnormality.
In step s 102, if robot motion's state is abnormality, according to what is obtained from inertial navigation system
Mileage information, acceleration information and angle change information obtain the particle distribution information for predicting mobile robot pose.?
When detecting the motion state exception of mobile robot, the pose of mobile robot may be just inaccurate at this time, needs to shifting
The present bit posture progress part of mobile robot is self-positioning, redefines accurate pose.Here the pose packet of mobile robot
Position and the posture for including mobile robot, for indicating the location of mobile robot and direction.
Here, mileage information is obtained from the odometer in inertial navigation system, acceleration information is from inertial navigation system
Accelerometer obtain, angle change information from inertial navigation system gyroscope obtain.It specifically, is according to mileage
Information, acceleration information and angle change information are obtained by the self-positioning algorithm in adaptive Monte Carlo for predicting moving machine
The particle distribution information of device people's pose.
Adaptive Monte Carlo localization algorithm (Adaptive Monte Carlo Localization, AMCL) is a kind of
The probabilistic method of mobile robot, this method on the basis of known map by using particle filter algorithm to track machine
The pose of people.
In some embodiments of the present application, obtained by the self-positioning algorithm in adaptive Monte Carlo for predicting mobile machine
The particle distribution information of people's pose, it may include following steps:
According to mileage information, acceleration information and angle change information, initialize for predicting mobile robot pose
Particle distribution information；
Particle position is updated according to mileage information；
The current time laser observations data obtained from laser radar are matched with the map of particle position, root
Particle weights are determined according to matching result；
Particle resampling is carried out according to particle weights, the particle distribution information after obtaining resampling.
Here, the mileage information that will acquire, acceleration information and angle change information are made by oneself as adaptive Monte Carlo
The prior information of position algorithm, the adaptive self-positioning algorithm in Monte Carlo carry out just particle distribution information according to these prior informations
Beginningization.
After having initial particle distribution information, particle position is updated further according to mileage information.Specifically, it is
According to the particle distribution information of last moment, the particle distribution information at current time is obtained by preset motion model.This Shen
In some embodiments please, preset motion model is Gauss model, and mileage information can be inputted to the motion model to obtain
The covariance and mean value of the model.Particle update be according to the distribution of last moment each particle, in conjunction with motion model to
Obtain the prediction distribution of each particle at current time.
Particle position is updated, after obtaining updated particle distribution information, then is worked as what is obtained from laser radar
Preceding moment laser observations data are matched with the map of particle position, determine particle weights according to matching result.Here,
Laser observations data are the laser scanning information comprising angle and distance data, and laser observations data are obtained from laser radar, this
In some embodiments of application, laser radar rotates a circle on two-dimensional surface can be obtained a laser scanning information.Meanwhile
Mobile robot also carries the global map of mobile environment, which can be established by the scan data of laser radar,
It can also be used for being matched with laser observations data.If the matching result of the map of laser observations data and some particle position
Height then can determine that the weight of the particle is high, if laser observations data are low with the matching result of the map of some particle position,
It can determine that the weight of the particle is low.
After respective weights have been determined for each particle, particle resampling is carried out further according to particle weights, after obtaining resampling
Particle distribution information.Specifically, for each particle, according to the particle weights w (t-1) of last moment, by adopting at random
Sample strategy regenerates sampling particle, to obtain the particle distribution information after resampling.
In step s 103, particle is clustered according to the particle distribution information, and particle weights mean value is maximum
Classification in the corresponding pose of particle weights mean value be determined as predict pose.Here, for predicting the grain of mobile robot pose
In the larger context, each particle has respective weight for son distribution, and the sum of weight of all particles is 1, the weight of particle
I.e. mobile robot appears in the probability on the particle corresponding position.Since there is no true for the self-positioning algorithm in adaptive Monte Carlo
The position that a fixed mobile robot most probable occurs, in some embodiments of the present application, by cluster particle come in advance
It surveys the most probable current pose of mobile robot and predicts pose, the prediction pose is determined further according to the perception to current pose
It is whether correct.
Specifically, in some embodiments of the present application, particle is carried out to cluster and determine prediction pose, first according to particle
Distributed intelligence clusters particle by unsupervised learning clustering algorithm, obtains particle classifying.The essence of cluster be exactly according to
Certain principle maps the data into different attribute sets.Preferably, it in some embodiments of the present application, can be used
DBSCAN clustering algorithm clusters particle.DBSCAN(Density-Based Spatial Clustering of
Applications with Noise) it is a more representational density-based algorithms, with other divisions and layer
Secondary clustering method is different, and cluster is defined as the maximum set of the connected point of density by it, can be with region highdensity enough
It is divided into cluster, and can find the cluster of arbitrary shape in the spatial database of noise.
After particle classifying after being clustered, further according to particle classifying, the wherein maximum classification of particle weights mean value is obtained.
It include multiple particles, some implementations of the application in each particle classifying here, multiple particles classification can be obtained after being clustered
In example, according to the particle weights mean value of the corresponding each particle classifying of weight calculation of particles all in particle classifying, then grain is obtained
The sub- maximum particle classifying of weight equal value.
After determining the maximum particle classifying of particle weights mean value, then by particle weights in the maximum classification of particle weights mean value
The corresponding pose of mean value is determined as predicting pose.Here, particle weights mean value has obtained, which may be with certain grains
The weight of son is identical, it is also possible to and it is close with the weight of certain particles, it, can be by the weight equal value pair in some embodiments of the present application
The pose answered is determined as pose corresponding to the particle same or similar with its weight equal value, and the corresponding pose of the weight equal value is
To predict pose, i.e., according to the mobile robot pose of mileage information, acceleration information and angle change information prediction.
It in step S104, will predict that the corresponding map of pose is matched with current time laser observations data, determine
Observe the pose of matching degree and mobile robot.Since the mobile robot prediction pose clustered by particle is that comparison is thick
Rough pose, in some embodiments of the present application, then by the laser observations data at current time map corresponding with prediction pose
Point cloud matching is carried out by iteration closest approach algorithm, obtains the pose of observation matching degree and mobile robot, which is to compare
More accurate pose can be obtained in fine matching.
Iteration closest approach (Iterative Closest Point, ICP) algorithm is a kind of iterative calculation method, can be made
Point clouds merging under different coordinates is into the same coordinate system.The purpose of ICP algorithm is to find subject to registration cloud
Rotation parameter R and translation parameters T between data and reference cloud data, so that meeting under certain measurement criterion between two point datas
Optimum Matching.ICP algorithm is fundamentally based on the optimal method for registering of least square method, and algorithm repeats to select corresponding pass
Mooring points pair calculates optimal rigid body translation, until meeting the convergence precision being correctly registrated requirement.
The pose for observing matching degree and mobile robot accordingly is obtained after being matched by ICP algorithm, which is
Pose after most accurate matching, that is, the final prediction pose of current mobile robot.As shown in Fig. 2, white rectangle in figure
The part of composition is indoor environment locating for mobile robot, the yin there are multiple barriers in indoor environment, in indoor environment
Shadow part is the unimpeded scanning range of laser radar, and the point on indoor environment edge or indoor barrier represents laser observations
Data, the related data obtained according to the figure are several it is found that laser observations data and the observation matching degree of map reach 95% or more
It can all match, illustrate that the final prediction pose of the mobile robot and true pose are very close.
In step S105, if observation matching degree is less than first threshold, according to the corresponding mobile robot of matching observation window
Motion range redefines the pose of observation matching degree and mobile robot.Here, matching observation window includes that multiframe laser is seen
Measured data, these laser observations data by laser radar obtain, and have sequential relationship, i.e., these laser observations data be
It obtains at the time of different, and is arranged according to the sequencing at moment.First threshold is pre-set observation matching degree
Threshold value, for determining the need for relocating mobile robot, first threshold can be configured according to the actual situation,
It can also be adjusted according to positioning scenarios of the mobile robot in actual scene.In some embodiments of the present application, if seen
Survey matching degree be less than first threshold, illustrate to the self-positioning ineffective of mobile robot, it is therefore desirable to mobile robot into
Capable local positioning again.If observation matching degree is all larger than first threshold, illustrate Mobile robot self-localization success, it is mobile
The final pose of robot is exactly the final prediction pose of the mobile robot obtained after being matched by ICP algorithm.
Specifically, it is determined first according to the mobile robot pose of multiframe laser observations data in matching observation window
Moveable robot movement range.Here, moveable robot movement range refers to the corresponding robot pose of matching observation window
The range of variation determines that the motion range of mobile robot can be in first record matching observation window in first frame laser observations data
Robot pose, re-record matching observation window in subsequent frame laser observations data in robot pose, according to these
Robot pose determines the motion range of mobile robot.
Next, adjusting the ginseng of the predetermined movement model of particle distribution further according to obtained moveable robot movement range
It counts and obtains the particle distribution information for predicting mobile robot pose.In some embodiments of the present application, predetermined movement mould
Type is Gauss model, and by adjusting the whole particle distribution of Parameter adjustable of predetermined movement model, uniform particle is distributed in the movement
In range, to obtain new particle distribution information.
It is similar to the method for the aforementioned pose for determining mobile robot according to particle distribution information, then according to particle distribution
Information clusters particle, and the corresponding pose of particle weights mean value in the maximum classification of particle weights mean value is determined as pre-
Location appearance, then will predict that the corresponding map of pose is matched with laser observations data in observation window are matched, so that it is determined that seeing
Survey the pose of matching degree and mobile robot.The robot position that the pose of the mobile robot obtained at this time as finally determines
Appearance, the pose are likely located at greatly near the true pose of mobile robot very much.
, can also be when the motion state of mobile robot be abnormality in some embodiments of the present application, reduction
Quantity with laser observations data in observation window can dynamically adjust participation machine by reducing the quantity of laser observations data
The self-positioning history cumulative observations data of device people improve matching efficiency, shorten the time of robot self-localization.
In some embodiments of the present application, third threshold can also be greater than in the matching degree that present laser observes data and map
When value, increase the quantity of laser observations data in matching observation window.Here third threshold value is the upper of preset observation matching degree
Threshold value is limited, is used to determine whether the size of adjustment matching observation window, if the matching degree of present laser observation data and map
It is larger, and when being more than third threshold value, matching observation window is tuned up, that is, increases laser observations number in matching observation window
According to quantity, to avoid false triggering.
In some embodiments of the present application, a kind of local self aligning system of preferred mobile robot is additionally provided, such as
Shown in Fig. 3, which mainly includes three parts: dynamic window generation module, self-positioning module and data acquisition module.
The mileage information and acceleration information that dynamic window generation module is used to that inertial navigation system to be combined to provide, judge robot
Whether in abnormalities such as idle running, if robot is in abnormality, it is big to reduce dynamic window (i.e. matching observation window)
It is small, quickly locally to recalculate particle distribution, exclude positioning abnormal problem.
Data acquisition module is mainly used for obtaining laser observations data, odometer information and acceleration information, is other moulds
Block operation provides data supporting, is mainly believed by sensor data acquisition filter module, odometer data obtaining module, acceleration
Breath obtains module composition.Sensor data acquisition filter module is calculated for acquiring laser sensor data, and using correlation filtering
Method removes extra laser measurement data and extra noise, i.e., laser observations data are filtered and are removed with noise.Filtering is calculated
Method can be used single-point filtering, median filtering, extraction houghline etc. and not correspond to caused by measurement is not allowed similar to plane to evade
The problem of.Odometer data obtaining module provides priori knowledge for obtaining mileage for self-positioning module.Acceleration information
Module is obtained for obtaining acceleration information, for robot wheel whether skid, dallying provides judgment basis.
Self-positioning module is mainly used for according to environmental map and odometer information, laser observations information and dynamic window,
Using improved particle filter algorithm, robot self-localization function is realized.It is mainly clustered by improvement particle filter module, particle
Module, ICP matching algorithm module are formed based on window particle distribution computing module.
Particle filter module is improved to be used for for each particle, first with stochastic sampling strategy, based on last moment
Weight w (t-1) regenerates sampling particle, and the robot motion's information obtained using odometer, updates particle position, then
Using the map similarity of laser observations data and particle position, particle weights are calculated, the grain under Current observation is obtained
Son distribution.Particle cluster module is used to obtain maximum cluster according to particle distribution using clustering algorithm.ICP matching algorithm module
For combining maximum cluster mean value pose, laser observations and global map used for positioning, point cloud matching is carried out, is matched
Pose after degree and accurate matching.It is used to be generated according to dynamic window generation module based on window particle distribution computing module dynamic
State window is respectively less than a certain given threshold in current position matching degree, and matching times reach laser observations data in window
Quantity (i.e. matching degree be greater than window size) when, then according to the dynamic window size of robot, recalculate particle distribution
And particle weights.
Some embodiments of the present application additionally provide a kind of local self-locating devices of mobile robot, which includes using
Memory in storage computer program instructions and processor for executing program instructions, wherein when the computer program refers to
When enabling by processor execution, the equipment is made to execute the local method for self-locating of aforementioned mobile robot.
Some embodiments of the present application additionally provide a kind of computer-readable medium, are stored thereon with computer-readable finger
It enables, the computer-readable instruction can be executed by processor the local method for self-locating to realize aforementioned mobile robot.
In conclusion the scheme that some embodiments of the present application provide can be determined according to mileage information and acceleration information
The motion state of mobile robot obtains the particle for predicting mobile robot pose when motion state is abnormality
Distributed intelligence, then particle is clustered, the corresponding pose of particle weights mean value in the maximum classification of particle weights mean value is true
It is set to prediction pose, and will predicts that the corresponding map of pose is matched with current time laser observations data, determines observation
Pose with degree and mobile robot, and when observing matching degree less than first threshold, redefine observation matching degree and movement
The pose of robot, so as to larger or wheel slip, idle running occurs in mobile robot in current environment and map discrepancies
Etc. abnormal conditions when, realize the accurate self-positioning of mobile robot, avoid the occurrence of localization for Mobile Robot lose the problem of, scheme
Simply, it easily realizes, improves the accuracy of Mobile robot self-localization.
It should be noted that the application can be carried out in the assembly of software and/or software and hardware, for example, can adopt
With specific integrated circuit (ASIC), general purpose computer or any other realized similar to hardware device.In one embodiment
In, the software program of the application can be executed to implement the above steps or functions by processor.Similarly, the application
Software program (including relevant data structure) can be stored in computer readable recording medium, for example, RAM memory,
Magnetic or optical driver or floppy disc and similar devices.In addition, hardware can be used to realize in some steps or function of the application, example
Such as, as the circuit cooperated with processor thereby executing each step or function.
In addition, a part of the application can be applied to computer program product, such as computer program instructions, when its quilt
When computer executes, by the operation of the computer, it can call or provide according to the present processes and/or technical solution.
And the program instruction of the present processes is called, it is possibly stored in fixed or moveable recording medium, and/or pass through
Broadcast or the data flow in other signal-bearing mediums and transmitted, and/or be stored according to described program instruction operation
In the working storage of computer equipment.Here, including an equipment according to one embodiment of the application, which includes using
Memory in storage computer program instructions and processor for executing program instructions, wherein when the computer program refers to
When enabling by processor execution, method and/or skill of the equipment operation based on aforementioned multiple embodiments according to the application are triggered
Art scheme.
It is obvious to a person skilled in the art that the application is not limited to the details of above-mentioned exemplary embodiment, Er Qie
In the case where without departing substantially from spirit herein or essential characteristic, the application can be realized in other specific forms.Therefore, no matter
From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and scope of the present application is by appended power
Benefit requires rather than above description limits, it is intended that all by what is fallen within the meaning and scope of the equivalent elements of the claims
Variation is included in the application.Any reference signs in the claims should not be construed as limiting the involved claims.This
Outside, it is clear that one word of " comprising " does not exclude other units or steps, and odd number is not excluded for plural number.That states in device claim is multiple
Unit or device can also be implemented through software or hardware by a unit or device.
Claims (14)
1. a kind of local method for self-locating of mobile robot, wherein the mobile robot includes inertial navigation system and swashs
Optical radar, the inertial navigation system include odometer, accelerometer and gyroscope, this method comprises:
According to the mileage information and acceleration information obtained from inertial navigation system, the motion state of mobile robot is determined；
The motion state be abnormality when, according to obtained from inertial navigation system mileage information, acceleration information and
Angle change information obtains the particle distribution information for predicting mobile robot pose；
Particle is clustered according to the particle distribution information, and particle weights in the maximum classification of particle weights mean value are equal
It is worth corresponding pose to be determined as predicting pose；
The corresponding map of the prediction pose is matched with current time laser observations data, determines observation matching degree and shifting
The pose of mobile robot；
If observing matching degree is less than first threshold, according to the corresponding moveable robot movement range of matching observation window, again really
Surely the pose of matching degree and mobile robot is observed, the matching observation window includes being obtained by laser radar with timing
The multiframe laser observations data of relationship.
2. according to the method described in claim 1, wherein, being believed according to the mileage information and acceleration obtained from inertial navigation system
Breath, determines the motion state of mobile robot, comprising:
If the mileage information obtained from odometer continues to increase and the acceleration information obtained from accelerometer changes less than second
When threshold value, the motion state of mobile robot is determined as abnormality.
3. according to the method described in claim 1, wherein, believed according to the mileage information that is obtained from inertial navigation system, acceleration
Breath and angle change information, obtain the particle distribution information for predicting mobile robot pose, comprising:
According to the mileage information obtained from odometer, the acceleration information obtained from accelerometer and the angle obtained from gyroscope
Change information obtains the particle distribution for predicting mobile robot pose by the self-positioning algorithm in adaptive Monte Carlo and believes
Breath.
4. according to the method described in claim 3, wherein, being obtained by the self-positioning algorithm in adaptive Monte Carlo for predicting to move
The particle distribution information of mobile robot pose, comprising:
According to mileage information, acceleration information and angle change information, the particle for predicting mobile robot pose is initialized
Distributed intelligence；
Particle position is updated according to mileage information；
The current time laser observations data obtained from laser radar are matched with the map of particle position, according to
Particle weights are determined with result；
Particle resampling is carried out according to particle weights, the particle distribution information after obtaining resampling.
5. according to the method described in claim 4, wherein, updating particle position according to mileage information, comprising:
According to the particle distribution information of last moment, the particle distribution information at current time is obtained by preset motion model.
6. according to the method described in claim 4, wherein, carrying out particle resampling according to particle weights, comprising:
According to the particle weights of last moment, sampling particle is regenerated by stochastic sampling strategy.
7. according to the method described in claim 1, wherein, clustered according to the particle distribution information to particle, and by grain
The corresponding pose of particle weights mean value is determined as predicting pose in the sub- maximum classification of weight equal value, comprising:
According to the particle distribution information, particle is clustered by unsupervised learning clustering algorithm, obtains particle classifying；
According to particle classifying, the wherein maximum classification of particle weights mean value is obtained；
The corresponding pose of particle weights mean value in the maximum classification of particle weights mean value is determined as to predict pose.
8. according to the method described in claim 1, wherein, by the corresponding map of the prediction pose and current time laser observations
Data are matched, and determine the pose of observation matching degree and mobile robot, comprising:
The corresponding map of the prediction pose and current time laser observations data are subjected to a cloud by iteration closest approach algorithm
Matching obtains the pose of observation matching degree and mobile robot.
9. according to the method described in claim 1, wherein, according to the corresponding moveable robot movement range of matching observation window,
Redefine the pose of mobile robot, comprising:
According to the mobile robot pose of multiframe laser observations data in matching observation window, moveable robot movement model is determined
It encloses；
According to the moveable robot movement range, adjusts the parameter of the predetermined movement model of particle distribution and obtain for predicting
The particle distribution information of mobile robot pose；
Particle is clustered according to the particle distribution information, and particle weights in the maximum classification of particle weights mean value are equal
It is worth corresponding pose to be determined as predicting pose；
The corresponding map of the prediction pose is matched with laser observations data in observation window are matched, determines observation matching
The pose of degree and mobile robot.
10. according to the method described in claim 1, wherein, the laser observations data are swashing comprising angle and distance data
Optically scanning information.
11. according to the method described in claim 1, wherein, this method further include:
When the motion state is abnormality, the quantity of laser observations data in matching observation window is reduced.
12. according to the method described in claim 1, wherein, this method further include:
When present laser observes the matching degree of data and map greater than third threshold value, increase laser observations in matching observation window
The quantity of data.
13. a kind of local self-locating devices of mobile robot, which includes the storage for storing computer program instructions
Device and processor for executing program instructions, wherein when the computer program instructions are executed by the processor, make the equipment
Method described in any one of perform claim requirement 1 to 12.
14. a kind of computer-readable medium, is stored thereon with computer-readable instruction, the computer-readable instruction can be processed
Device is executed to realize the method as described in any one of claims 1 to 12.
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|---|---|---|---|---|
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090024251A1 (en) * | 2007-07-18 | 2009-01-22 | Samsung Electronics Co., Ltd. | Method and apparatus for estimating pose of mobile robot using particle filter |
| CN105652871A (en) * | 2016-02-19 | 2016-06-08 | 深圳杉川科技有限公司 | Repositioning method for mobile robot |
| 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 |
| CN107356252A (en) * | 2017-06-02 | 2017-11-17 | 青岛克路德机器人有限公司 | A kind of Position Method for Indoor Robot for merging visual odometry and physics odometer |
-
2018
- 2018-08-02 CN CN201810872628.9A patent/CN108931245B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090024251A1 (en) * | 2007-07-18 | 2009-01-22 | Samsung Electronics Co., Ltd. | Method and apparatus for estimating pose of mobile robot using particle filter |
| CN105652871A (en) * | 2016-02-19 | 2016-06-08 | 深圳杉川科技有限公司 | Repositioning method for mobile robot |
| 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 |
| CN107356252A (en) * | 2017-06-02 | 2017-11-17 | 青岛克路德机器人有限公司 | A kind of Position Method for Indoor Robot for merging visual odometry and physics odometer |
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