CN107991683B - A kind of robot autonomous localization method based on laser radar - Google Patents
A kind of robot autonomous localization method based on laser radar Download PDFInfo
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- CN107991683B CN107991683B CN201711096684.XA CN201711096684A CN107991683B CN 107991683 B CN107991683 B CN 107991683B CN 201711096684 A CN201711096684 A CN 201711096684A CN 107991683 B CN107991683 B CN 107991683B
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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
- G01S17/88—Lidar systems specially adapted for specific applications
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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
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Abstract
The robot autonomous localization method based on laser radar that the invention discloses a kind of, it include: centered on robot initial position, N number of particle constituent particle group is randomly generated, the current time of robot operation, the real-time moving distance of robot and real time rotation angle measured according to robot sensor updates population；For each particle, calculating laser radar point cloud is overlapped score of the quantity as each particle with the barrier of map, and the weighting pose mean value of weight calculation population is scored at each particle, estimates pose as AMCL；Scan matching pose is obtained using the scan matching algorithm based on Gaussian weighting marks method using AMCL estimation pose as initial value, the optimal pose at the current time as robot operation；Using AMCL algorithm resampling population, the optimal pose of whole process is as positioning result when finally obtaining robot operation.The stability of convergence rate, positioning accuracy and positioning that the present invention positions has a distinct increment.
Description
Technical field
The invention belongs to localization for Mobile Robot and field of navigation technology, are based on laser radar more particularly, to one kind
Robot autonomous localization method.
Background technique
With intelligentized fast development, mobile robot is widely applied in various industries.Electric system nobody
In substation on duty, robot automatic tour inspection system is the important directions of intelligent substationization development.In order to realize robot intelligence
Energy inspection, reliable and accurate autonomous localization and navigation technology are bases.Existing unattended operation transformer station automatic crusing robot
System need to generally install the auxiliary locators such as laser reflection plate, magnetic nail, RF tag or two dimensional code, need to carry out to substation
Certain transformation, this can not only increase difficulty of construction, but also reliability is not also high.Meanwhile the robot based on laser radar is fixed
Adaptive monte carlo localization (Adaptive Monte Carlo Localization:AMCL) algorithm used by the system of position
In practical applications, there is a problem of that positioning accuracy is not high, it is difficult to meet the requirement of substation's automatic detecting high accuracy positioning.
It can be seen that existing robot localization technology is there are difficulty of construction height, positioning accuracy is lower, is difficult to meet substation
The technical issues of automatic detecting high accuracy positioning requires.
Summary of the invention
For the disadvantages described above and Improvement requirement for solving the prior art, the present invention provides a kind of machines based on laser radar
People's autonomic positioning method, thus solves existing location technology there are difficulty of construction that high, positioning accuracy is lower, is difficult to meet substation
The technical issues of automatic detecting high accuracy positioning requires.
To achieve the above object, according to one aspect of the present invention, provide a kind of robot based on laser radar from
Master positioning method, comprising:
(1) centered on robot initial position, N number of particle constituent particle group is randomly generated using Gaussian Profile, each
Particle represents the pose of robot, and population indicates the probability distribution of robot pose；
(2) current time of robot operation, the real-time moving distance of robot and reality measured according to robot sensor
Shi Xuanzhuan angle updates each of the population corresponding pose of particle；
(3) each particle obtained for step (2), the scanning result of laser radar is corresponding with each particle
Pose is mapped to map and obtains laser radar point cloud, calculates laser radar point cloud and is used as often with the quantity that is overlapped of the barrier of map
The score of one particle is scored at the weighting pose mean value of weight calculation population with each particle, estimates as AMCL
Pose；
(4) it is obtained using AMCL estimation pose as initial value using the scan matching algorithm based on Gaussian weighting marks method
Scan matching pose, the optimal pose at the current time as robot operation；
(5) it is inserted into population using scan matching pose as one high score particle, uses AMCL algorithm resampling grain
Subgroup continues to execute from step (2) in the subsequent time of robot operation；
(6) step (2)-(5) are repeated, the optimal pose of whole process is as positioning result when finally obtaining robot operation.
Further, map is grid probability map.
Further, step (4) scan matching optimization process includes:
To grid probability map carry out it is down-sampled, obtain multiresolution map, then from low resolution to high-resolution into
The more wheel optimizations of row, first run optimization is using AMCL estimation pose as initial value；In each round optimization, by laser radar point cloud to estimate
Pose is mapped on epicycle resolution ratio map, and carries out bilinear interpolation to grating map is occupied to calculate gradient, uses Gauss
Newton iteration method obtains more preferably pose, the initial value as next round optimization；By successive ignition, make laser radar point cloud
It is overlapped that quantity is most with the barrier of grid probability map, by the optimum results that last is taken turns, as scan matching pose.
In general, through the invention it is contemplated above technical scheme is compared with the prior art, can obtain down and show
Beneficial effect:
(1) it is not high for the adaptive monte carlo localization algorithm of tradition to there is positioning accuracy in the present invention in practical applications
Problem increases scan matching link and is iterated optimization to pose, improves the alignment of laser radar scanning point cloud and map
Effect, so that robot localization is more accurate.And the optimization pose for obtaining scan matching, it has been fused to population, has been accelerated
Position the stability of convergence rate and positioning.
(2) present invention is without installing the auxiliary locators such as laser reflection plate, magnetic nail, RF tag or two dimensional code, can be with
Realize high-precision, the robot autonomous localization of high reliability and tracklessization navigation.Present invention improves over the calculating sides of particle score
Formula improves computational efficiency；Positioning accuracy and the convergence rate of positioning have biggish promotion.
Detailed description of the invention
Fig. 1 is a kind of robot autonomous localization method flow diagram based on laser radar of the embodiment of the present invention；
Fig. 2 is the population of the embodiment of the present invention and the matching schematic diagram of grating map；
Fig. 3 is the bilinear interpolation of the embodiment of the present invention；
Fig. 4 is the scan matching process flow diagram flow chart of the embodiment of the present invention；
Fig. 5 (a) is the distribution map for the position fixing process particle original state that the embodiment of the present invention 1 provides.
Fig. 5 (b) is the distribution map for the position fixing process particle convergence process that the embodiment of the present invention 1 provides.
Fig. 5 (c) is the schematic diagram for the position fixing process particle convergence result that the embodiment of the present invention 1 provides.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.As long as in addition, technical characteristic involved in the various embodiments of the present invention described below
Not constituting a conflict with each other can be combined with each other.
As shown in Figure 1, a kind of robot autonomous localization method based on laser radar, comprising:
(1) random to generate the grain for largely representing robot pose (position coordinates, posture direction) in robot initial position
Son, only considers two-dimensional localization in the embodiment of the present invention, thus the pose of mobile robot by robot under world coordinate system two
It ties up coordinate and deflection in the horizontal plane forms.According to the initial position of robot, an initial pose, the initial bit are obtained
Appearance indicates the rough estimate to the currently practical pose of robot, for accelerating the convergence rate of positioning.If the deviation of initial pose
Larger, then positioning needs the long period to restrain, but not positioning is caused to fail.A small range near initial pose, with height
This distribution generates 5000 particles at random.The deflection of these particles equally meets the Gauss centered on inceptive direction angle point
Cloth.Population is made of this 5000 particles, indicates the probability distribution of robot pose.
(2) current time of robot operation, the real-time moving distance of robot and reality measured according to robot sensor
Shi Xuanzhuan angle updates each of the population corresponding pose of particle；
(3) each particle obtained for step (2), the scanning result of laser radar is corresponding with each particle
Pose is mapped to map and obtains laser radar point cloud, calculates laser radar point cloud and is used as often with the quantity that is overlapped of the barrier of map
The score of one particle is scored at the weighting pose mean value of weight calculation population with each particle, estimates as AMCL
Pose；
(4) it is obtained using AMCL estimation pose as initial value using the scan matching algorithm based on Gaussian weighting marks method
Scan matching pose, the optimal pose at the current time as robot operation；
(5) it is inserted into population using scan matching pose as one high score particle, uses AMCL algorithm resampling grain
Subgroup makes population focus more on optimal pose, continues to execute from step (2) in the subsequent time of robot operation；
(6) step (2)-(5) are repeated, so that population concentrates on optimal pose always in robot operational process, most
Whole optimal pose is obtained when robot operation eventually as positioning result.
Preferred adaptive Monte Carlo localization (AMCL) algorithm of the embodiment of the present invention is as positioning framework.AMCL algorithm tool
Have the advantages that positioning fast convergence rate, population are adaptive, it is robot autonomous fixed suitable for using laser radar sensor to carry out
Position.AMCL algorithm uses the thought of particle filter, and population remains various possibility of robot pose, and with particle
Weight indicates that robot is located at the probability size of some pose.If there are multiple possible due to environment or sensor
Pose, the characteristic of AMCL algorithm have ensured the validity of positioning.In addition, AMCL algorithm can also solve robot abduction issue.
If robot is moved by external force, pose is jumped, and AMCL algorithm still can find correct pose, and ensure that positioning can
By property.
The embodiment of the present invention conventional AMC L algorithm on the basis of increase a scan matching process, positioning accuracy and fixed
The convergence rate of position has biggish promotion；And it is directed to the characteristic of laser radar sensor, the calculation of particle score is improved,
Improve the computational efficiency of algorithm.
The embodiment of the present invention is preferred, and step (3) includes:
Each endpoint of (3-1) for laser radar sensor acquisition back, there are four types of situations for the matching with map, such as
Shown in Fig. 2 and table 1.
Table 1
| Matching score | Free | Occupied |
| Free | a | b |
| Occupied | c | d |
As shown in table 1, every kind of match condition has phase reserved portion, such as (a, b, c, d)=(+1, -5, -5 ,+10), however real
Trampling the white space for showing that laser radar scanning goes out can be more, and the score for calculating this grid is quite time-consuming.Therefore it only counts
Calculate the identical number of the barrier in the laser sensor barrier and map that navigate to, i.e., modification score be determined as (a, b, c,
D)=(0,0,0 ,+1), and score respectively each particle.Particle score is higher to be indicated to swash under its pose represented
The matching degree of optical scanning and map is higher, that is to say, its bright pose is closer to true pose.
After (3-2) scores to each particle, using the weighted average pose of population as the moment robot
Pose estimation.
The embodiment of the present invention is preferred, and step (4) includes:
(4-1) scan matching principle
The robot of laser sensor is carried, the orientation problem in Probabilistic Cell map can be converted to one such as following formula
Shown in match optimize the problem of:
S in above formulaiWhen (ξ) is that mobile robot is in pose ξ, coordinate of the laser scanning point cloud under map coordinates system, M
(Si(ξ)) it is map in given coordinate points SiOccupation probability on (ξ).Its function expression are as follows:
P in above formulaxAnd pyIndicate that the coordinate of laser sensor, ψ are the deflection of robot, sI, xAnd sI, yIt is at robot
When pose ξ, coordinate of the laser radar point cloud in x, y both direction under map coordinates system.
The target of scan matching process is: it is one and the lesser initial estimation ξ of attained pose deviation given, find one partially
Difference DELTA ξ enables laser scanning point cloud reach optimally aligned with Probabilistic Cell map, sets up following formula.
To M (S in above formulai(ξ+Δ ξ)) first order Taylor expansion is carried out, the minimum value for seeking expansion (seeks local derviation to Δ ξ
Counting and enabling partial derivative is 0), finally to acquire:
In above formula, Hessian matrix H are as follows:
SiThe partial derivative of (ξ) to ξ are as follows:
Since map M is grid probability map, be it is discrete and discontinuous, for the gradient for acquiring map M, can to map into
Row bilinear interpolation, as shown in Figure 3.
The core concept of bilinear interpolation is i.e.: carrying out an interpolation respectively in x, y both direction.
As can be seen from FIG. 3, PmThe probability value of point are as follows:
Wherein, M (P (x, y)) indicates that grid P (x, y) is the probability of obstacle.The partial derivative of M (P (x, y)) are as follows:
The core of scan matching is gone using the first order Taylor series expansion of Gaussian weighting marks method approximatively instead of non-
Linear regression model (LRM) finally minimizes the residual sum of squares (RSS) of master mould then by successive ignition.The ξ finally acquired+Δ ξ
Laser scanning result and Probabilistic Cell map is exactly enabled to reach optimally aligned optimization pose, i.e. scan matching pose.
(4-2) scan matching process
After mobile robot completes Global localization, orientation problem is converted into posture tracking problem.AMCL algorithm estimates
The pose of robot is the weighted center of Particle Cluster, and there are still certain deviations for robot attained pose.It is swept according to above-mentioned
Matching principle is retouched, Gaussian weighting marks method is taken, the pose that AMCL algorithm is solved is as initial value is calculated, in multiresolution
By slightly successively being optimized to essence on figure.In every layer of calculating, deviation is found out using gauss-newton method, to obtain more smart
True pose.Detailed process is as shown in Figure 4.
As can be seen from FIG. 4, only when the population of AMCL algorithm is gathered near the same point, that is, the pose solved is estimated
It collects when holding back, the pose of robot is just advanced optimized using scan matching algorithm.Firstly, the pose that AMCL algorithm is solved is made
It, by low resolution to high-resolution, is carried out layer by layer then on multiresolution map for the initial pose of scan matching process
Gaussian weighting marks.After the completion of iterative calculation, the more accurately pose that will obtain.If solution and AMCL that scan matching obtains
Error between the solution of algorithm is less than error threshold limit, then solution is inserted into population as high weight particle；If error mistake
Greatly, it indicates that wrong solution may have been found out, then abandons the matched solution of present scan.
Since scan matching process needs the solution of AMCL to sweep as initial value if AMCL solution differs larger with attained pose
Retouching matching process just will fail.Further it is necessary to which the optimal particle that scan matching is obtained is inserted into the population of AMCL, formed
Closed loop, so that AMCL solution be avoided to separate with the solution of scan matching.
It should be noted that scan matching process uses the thought of the layer-by-layer iteration of multiresolution map, to guarantee that map connects
Continuous to lead, map is using Probabilistic Cell map.Low resolution map in multilayer map is from original high resolution map
It is down-sampled to obtain.
The embodiment of the present invention is preferred, and step (5) includes:
The scan matching pose that scan matching algorithm optimization is obtained is inserted into particle as the particle of a high score
In group.Using AMCL algorithm, particle resampling is carried out.Then return step (2) wait new sensing data.Due to adopting again
Particle after sample concentrates near high score particle, so that the AMCL estimation pose of subsequent time is more accurate.
Embodiment 1
The mobile robot laser radar autonomous positioning of the embodiment of the present invention 1 is tested, as shown in Figure 5.Positioning experiment is big
Small is 16.075m × 9.9m, is carried out on the grating map that resolution ratio is 2.5cm.From Fig. 5 (a) as can be seen that in initial pose
A large amount of particles (small arrow, number of particles minimum value 500, maximum value 5000) is generated near estimation.Robot is transporting
In dynamic process, particle gradually restrains polymerization, as shown in Fig. 5 (b) and Fig. 5 (c).Big arrow indicates robot each moment in figure
Operation posture, point cloud be laser scanning point cloud.
Experimental result: the maximum positioning error using conventional AMC L algorithm is about two grid sizes (5.0cm), average fixed
Position error is more than a grid size (1.25cm).Using the method for the present invention, the improvement AMCL for increasing scan matching process is calculated
The maximum positioning error of method is half of grid size (1.25cm), and average localization error is about three point one of grid size
(0.8cm)。
In the method for the present invention, the population of scan matching solution and AMCL algorithm forms closed loop, so that robot localization is more
It is accurate to add.This improved AMCL algorithm fusion conventional AMC L algorithm and scan matching algorithm, not only inherit conventional AMC L
The position stability and reliability of algorithm also have the advantages that scan matching algorithm high accuracy positioning.
As it will be easily appreciated by one skilled in the art that the foregoing is merely illustrative of the preferred embodiments of the present invention, not to
The limitation present invention, any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should all include
Within protection scope of the present invention.
Claims (1)
1. a kind of robot autonomous localization method based on laser radar characterized by comprising
(1) centered on robot initial position, N number of particle constituent particle group, each particle is randomly generated using Gaussian Profile
The pose of robot is represented, population indicates the probability distribution of robot pose；
(2) current time of robot operation, the real-time moving distance of robot measured according to robot sensor and in real time rotation
Gyration updates each of the population corresponding pose of particle；
(3) each particle obtained for step (2), by the scanning result of laser radar with the corresponding pose of each particle
It is mapped to map and obtains laser radar point cloud, calculating laser radar point cloud is overlapped quantity as each with the barrier of map
The score of particle is scored at the weighting pose mean value of weight calculation population with each particle, estimates pose as AMCL；
(4) it is scanned using AMCL estimation pose as initial value using the scan matching algorithm based on Gaussian weighting marks method
Pose is matched, the optimal pose at the current time as robot operation；
(5) it is inserted into population using scan matching pose as one high score particle, using AMCL algorithm resampling population,
Continue to execute from step (2) in the subsequent time of robot operation；
(6) step (2)-(5) are repeated, the optimal pose of whole process is as positioning result when finally obtaining robot operation；
The map is grid probability map；
Step (4) the scan matching optimization process includes:
It is down-sampled to the progress of grid probability map, multiresolution map is obtained, is then carried out from low resolution to high-resolution more
Wheel optimization, first run optimization is using AMCL estimation pose as initial value；In each round optimization, by laser radar point cloud to estimate pose
It is mapped on epicycle resolution ratio map, and carry out bilinear interpolation to grating map is occupied to calculate gradient, uses Gauss-Newton
Iterative method obtains more preferably pose, the initial value as next round optimization；By successive ignition, make laser radar point cloud and grid
The coincidence quantity of the barrier of lattice probability map is most, by the optimum results that last is taken turns, as scan matching pose.
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