CN103575272A - Method for extracting natural landmarks for mobile robot in forest environment - Google Patents

Method for extracting natural landmarks for mobile robot in forest environment Download PDF

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Publication number
CN103575272A
CN103575272A CN201310568954.8A CN201310568954A CN103575272A CN 103575272 A CN103575272 A CN 103575272A CN 201310568954 A CN201310568954 A CN 201310568954A CN 103575272 A CN103575272 A CN 103575272A
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trunk
segmentation
environment
forest environment
robot
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孙凤池
宋萌
代晓芳
苑晶
耿达
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Nankai University
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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
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/36—Input/output arrangements for on-board computers
    • G01C21/3602—Input other than that of destination using image analysis, e.g. detection of road signs, lanes, buildings, real preceding vehicles using a camera
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/50—Depth or shape recovery
    • G06T7/521—Depth or shape recovery from laser ranging, e.g. using interferometry; from the projection of structured light

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Abstract

The invention relates to a method for extracting natural landmarks for a mobile robot in a forest environment. Landmarks are used as indications for positioning and navigation on a robot, and in an unknown environment, preset landmarks do not exist and natural features in the unknown environment need to be extracted and are used as landmarks. The method comprises the following steps of carrying out three-dimensional laser scanning on a forest environment, carrying out tree trunk-oriented point cloud segmentation on the scanning result, carrying out geometrical element extraction, and carrying out tree trunk section clustering and integration to obtain a straight trunk section which uniquely represents the tree and is used as the landmark. The method utilizes a tree trunk in the forest environment as the natural landmark, realizes extraction of the tree trunk and its geometric features in the complex forest environment and utilizes the tree trunk and its geometric features to realize positioning and navigation on the mobile robot. The method solves the problem of high difficulty of positioning and navigation on the robot in the forest environment, and realizes extraction of high-quality natural feature landmarks in the non-structural forest environment in which a coded disc, a visual sensor and a GPS system difficultly realize effective robot positioning.

Description

Natural landmark for mobile robot extracting method in forest environment
Technical field
The invention belongs to localization for Mobile Robot and field of navigation technology, relate to the extracting method of natural landmark for mobile robot in a kind of forest environment, the intelligent mobile robot or the Autonomous Vehicles that are adapted at operation in the forest environment of field are used.
Background technology
Through development for many years, intelligent independent mobile robot has demonstrated more and more wide application prospect in fields such as industry, agricultural, service sectors.Particularly in unknown or dangerous environment, mobile robot can replace the mankind to carry out the operations such as exploration, detection and operation, and this makes mobile robot have huge potential value and demand in fields such as celestial body detecting, subsurface investigation, ocean development, military anti-terrorism, disaster relief, dangerous material processing, nuclear environment operations.
Regardless of mobile robot's applied environment, in its motion process, to solve all the time self location and navigation problem.Development along with mobile robot's research and application, orientation problem research turns to destructuring outdoor environment from structurized buildings environment gradually, from two dimensional surface environment, turn to three-dimensional environment, from small-sized simple environment, turn to complex environment on a large scale, from the constant environment of static state, turn to the environment of dynamic change.Yet outdoor physical environment self complexity, irregular feature have brought very large challenge to location and navigation, particularly difficult especially for this mixed and disorderly extensive environment of forest.In forest environment, gps signal can block and then lose efficacy due to tree branches in the region of the branches and leaves by dense and vegetation covering, and in this case, by robot self-sensor device, positioning navigation is the unique method addressing this problem.Vision sensor and laser sensor are the most frequently used robot sensors, yet visual sensing data are subject to the impact of the factors such as illumination variation, shade larger, thus therefrom extract object information become difficulty and unreliable.This makes to adopt laser sensor to obtain environmental information becomes the most appropriate solution.In addition, earth's surface that forest landform has is coarse, slope change is large etc., and feature not only makes us cannot predict definite ground level, and causes the six-freedom degree pose of robot all to have larger error, is badly in need of proofreading and correct.The object in scene that makes in a jumble of environment blocks mutually, even the slight variation at robot location and visual angle all can cause the violent difference of visible scenery.Scenery is carried out identification, the understanding of semantic hierarchies in non-structured dense forest environment, and then the extraction that completes high-level geometric element is also a complexity and difficult task.
Summary of the invention
The object of the invention is to solve the orientation problem of mobile robot in forest environment, natural landmark for mobile robot extracting method in a kind of forest environment is provided.
The present invention adopts three-dimensional laser sensor to obtain forest environment information, has invented a kind ofly brand-new from the three-dimensional point cloud of laser measurement data, to extract trunk as the method for natural landmark.This natural landmark extracting method take that scene is understood and trees automatic analysis is basic, and from counting rareness and extracted the geometric model of trunk the serious 3-D scanning of noise, concrete grammar is divided into three steps:
The 1.th pair of forest environment carries out 3 D laser scanning detection, utilizes the circular feature of trunk profile that three-dimensional point cloud is divided into sweep segment, according to space adjacency, sweep segment is organized into trunk segmentation.
The inadequate trunk segmentation of the 2.th geological information obtaining from the 1st step, extract circle model, and according to the center of circle, simulate the axis (this process is an iterative process) of trunk segmentation.
The 3.th by analyzing the discontinuous type in space of trunk segmentation, they are carried out to cluster and integration, and the difference according to trunk segmentation in space distribution, combines the segmentation that belongs to same trunk, remove crotch, and it is dry as road sign finally to obtain unique Naoki that represents trees simultaneously.
Obtain trunk as high-level characteristic after, can use paired scan matching method based on feature to follow the tracks of the six-dimensional pose of robot, realize robot location.
advantage of the present invention and beneficial effect:
Natural landmark extracting method based on heat transfer agent provided by the invention, solved a difficult problem of carrying out robot location in forest environment, be applicable to be blocked at gps signal, code-disc, vision sensor be difficult to the stable forest environment that reliable location is provided, this method is not subject to weather and illumination effect, is a kind of round-the-clock field robot localization method.The fields such as the method robot, Autonomous Vehicles, military affairs, exploration rescue in the wild all have application prospect.
Accompanying drawing explanation
Fig. 1 is that natural landmark is extracted process flow diagram.
Fig. 2 is two-dimensional scan plane and the crossing sweep segment schematic diagram that obtained of trunk.
Fig. 3 is the upper bound and the lower bound of circle model radius, (a) lower bound, (b) upper bound.
Fig. 4 is for to work as a little or
Figure 2013105689548100002DEST_PATH_IMAGE004
axis in the time of not in respective axis
Figure 2013105689548100002DEST_PATH_IMAGE006
with
Figure 2013105689548100002DEST_PATH_IMAGE008
distance.
Fig. 5 is the discontinuous three kinds of situations of trunk segment space, (a) fracture, (b) bending, (c) bifurcated.
Fig. 6 is the original 3D scanning with sloping scale Forest Scene.
Fig. 7 is that in Fig. 6, the road sign with sloping scale Forest Scene extracts result.
Embodiment
one, towards the three-dimensional point cloud of trunk, cut apart
Forest environment is carried out to 3 D laser scanning detection, and robot scans resulting three-dimensional point cloud with certain pose to special scenes and is called as a width 3-D scanning.One width 3-D scanning operates in by pitching the 2D scan-data that different scanning plane obtains by two-dimensional laser sensor and forms, and therefore the data of obtaining can be called to a line of 3-D scanning in a plane of scanning motion, and arranging of three-dimensional point cloud can be regarded one as accordingly
Figure 2013105689548100002DEST_PATH_IMAGE010
two-dimensional matrix.Due to the intrinsic sampling mode of pitching type three-dimensional laser sensor,
Figure 2013105689548100002DEST_PATH_IMAGE012
individual two-dimensional scan plane is not parallel to each other, and spacing is also not quite similar.Use represent the scanning angle between the current plane of scanning motion and local reference plane, can obtain following matrix description:
3-D scanning
Figure 2013105689548100002DEST_PATH_IMAGE016
comprise
Figure 899227DEST_PATH_IMAGE012
oK, , wherein,
Figure 2013105689548100002DEST_PATH_IMAGE020
represent the
Figure 2013105689548100002DEST_PATH_IMAGE022
the scan-data that the individual plane of scanning motion obtains is capable,
Figure 2013105689548100002DEST_PATH_IMAGE024
be
Figure 629417DEST_PATH_IMAGE022
the scanning angle that line scanning is corresponding, and have
Figure 2013105689548100002DEST_PATH_IMAGE026
.Each provisional capital comprises
Figure 2013105689548100002DEST_PATH_IMAGE028
individual data point,
Figure 2013105689548100002DEST_PATH_IMAGE030
, position represents the
Figure 156957DEST_PATH_IMAGE022
row,
Figure 2013105689548100002DEST_PATH_IMAGE034
the analyzing spot of column position, .
In order to obtain environmental information from a cloud, need to cut apart a cloud, object is according to whether hitting same object for according to being divided into segmentation by each two-dimensional scan line.Because the final goal of this method is to extract trunk, so algorithm is cut apart the geometric properties around trunk first targetedly to a cloud.
Utilize two spaces between object known, if there is the laser beam that does not hit any object between two analyzing spots in same two-dimensional scan plane, two analyzing spots are not continuous, and they necessarily belong to different objects.
In addition, for continuous sweep point
Figure 178265DEST_PATH_IMAGE032
with if from the view of robot, what they hit is two trees that block mutually front and back, also should be separated, and in order to cut apart two-dimensional line segment now, according to the cylindrical character of trunk profile, analyzing spot is carried out to projection.Background context knowledge according to solid geometry can be learnt, any cross section of right circular cylinder, no matter be to tilt or level, ellipse is still just round, along after its axis projection all corresponding to cylindrical positive rounded bottom surface.And trees can carry out modeling with right circular cylinder just.Although hit between the Scanning Section of same trunk and not parallel, their projection can be summed up as same circular arc, so just 3D problem has been changed into 2D problem.If point
Figure 2013105689548100002DEST_PATH_IMAGE040
with
Figure 54954DEST_PATH_IMAGE032
meet one of following two conditions, they can distinguish two adjacent sectional
Figure 2013105689548100002DEST_PATH_IMAGE042
with
Figure 2013105689548100002DEST_PATH_IMAGE044
:
1)
Figure 500585DEST_PATH_IMAGE040
with it not two-dimensional scan row
Figure 2013105689548100002DEST_PATH_IMAGE046
on continuity point.
2)
Figure 91153DEST_PATH_IMAGE040
with be
Figure 379494DEST_PATH_IMAGE046
on continuity point, but Euclidean distance between their projections is greater than threshold value.
When two-dimensional scan plane, cut apart completely, sweep segment screening need to be combined into the trunk segmentation of containing high layer information.The basic ideas that address this problem are to use the barycenter of sweep segment to describe its position, according to each segmentation, at the vertical adjacency in space, they are connected into grouping in turn, and what according to the height of the length of sweep segment and grouping, differentiate which grouping describes is trunk or sturdy crotch.
For weighing the relation of each sweep segment in three dimensions, first need to describe the position of each sweep segment.By 3-D scanning
Figure 133823DEST_PATH_IMAGE022
on row
Figure 57786DEST_PATH_IMAGE034
individual segmentation is denoted as .Because the sweep segment height in same two-dimensional scan plane is not quite similar, therefore use line index
Figure 518854DEST_PATH_IMAGE022
the position of sign sweep segment in the vertical direction.In addition, in the horizontal direction, will
Figure 634184DEST_PATH_IMAGE048
projection obtains in partial reference plane
Figure 2013105689548100002DEST_PATH_IMAGE050
.
Figure 813493DEST_PATH_IMAGE050
by width
Figure 2013105689548100002DEST_PATH_IMAGE052
and barycenter describe, wherein
Figure 213250DEST_PATH_IMAGE052
be defined as
Figure 545137DEST_PATH_IMAGE048
starting point
Figure 2013105689548100002DEST_PATH_IMAGE056
and end point
Figure 2013105689548100002DEST_PATH_IMAGE058
at the Euclidean distance of overall reference plane subpoint, be
Figure 733858DEST_PATH_IMAGE048
middle a little mean place in partial reference plane.
Trunk segmentation can be regarded as by adjacent sweep segment on one group of space and form.These sweep segment are from continuous two-dimensional scan plane.Therefore, k trunk segmentation is represented as
Figure 2013105689548100002DEST_PATH_IMAGE060
, and respectively scan between angle and have
Figure 2013105689548100002DEST_PATH_IMAGE062
, wherein,
Figure 2013105689548100002DEST_PATH_IMAGE064
be
Figure 2013105689548100002DEST_PATH_IMAGE066
the index of individual continuous two-dimensional scan plane.
In order to construct trunk segmentation, need to filter out long sweep segment.If
Figure 373525DEST_PATH_IMAGE048
width meet following condition:
Figure 2013105689548100002DEST_PATH_IMAGE068
Explanation
Figure 58453DEST_PATH_IMAGE048
can be regarded as radius and be no more than the upper limit
Figure 2013105689548100002DEST_PATH_IMAGE070
right circular cylinder on arc,
Figure 456199DEST_PATH_IMAGE048
may be obtained from trunk.Otherwise,
Figure 406837DEST_PATH_IMAGE048
necessarily take from unbroken ground or dense tree crown.
After filtering for the first time, the segmentation of candidate's trunk will successively be constructed.Construction algorithm is by the trunk set from empty
Figure 2013105689548100002DEST_PATH_IMAGE072
start, according to
Figure 413976DEST_PATH_IMAGE014
ascending order, be each sweep segment search for the trunk segmentation of its subordinate.For current
Figure 646691DEST_PATH_IMAGE048
, algorithm inspection
Figure 84626DEST_PATH_IMAGE072
in existing each trunk segmentation
Figure 2013105689548100002DEST_PATH_IMAGE074
if, do not have trunk segmentation to meet neighbour's condition, explanation up to the present, in space, still not do not form and contiguous grouping, therefore
Figure 623870DEST_PATH_IMAGE048
start alone a new grouping
Figure 2013105689548100002DEST_PATH_IMAGE076
, and will
Figure 2013105689548100002DEST_PATH_IMAGE078
add set
Figure 363418DEST_PATH_IMAGE072
.Otherwise, will
Figure 288649DEST_PATH_IMAGE048
the trunk segmentation that adds its subordinate as last sweep segment.
Form trunk segmentation
Figure 450640DEST_PATH_IMAGE074
each sweep segment by according to the angle of its place two-dimensional scan plane
Figure 699087DEST_PATH_IMAGE014
ascending order is arranged, and has maximum
Figure 983438DEST_PATH_IMAGE014
sweep segment be denoted as
Figure 2013105689548100002DEST_PATH_IMAGE080
.According to the scanning sequency in vertical direction, be
Figure 47133DEST_PATH_IMAGE074
in the most approaching
Figure 166398DEST_PATH_IMAGE048
sweep segment.Therefore, if
Figure 356071DEST_PATH_IMAGE048
belong to
Figure 442845DEST_PATH_IMAGE074
, so
Figure 946639DEST_PATH_IMAGE048
should be positioned at
Figure 248307DEST_PATH_IMAGE080
neighborhood in.Two components based on description sweep segment mentioned above
Figure 297297DEST_PATH_IMAGE052
with
Figure 684416DEST_PATH_IMAGE054
, when following two conditions meet simultaneously,
Figure 991900DEST_PATH_IMAGE048
be considered to be positioned at
Figure 69447DEST_PATH_IMAGE080
neighbour:
1）
Figure 2013105689548100002DEST_PATH_IMAGE082
2）
Figure 2013105689548100002DEST_PATH_IMAGE084
Condition 1) mean explanation
Figure 489670DEST_PATH_IMAGE080
with be positioned at adjacent two-dimensional scan plane, and the former
Figure 209682DEST_PATH_IMAGE014
larger.Condition 2) mean
Figure 407314DEST_PATH_IMAGE048
horizontal centroid be positioned at in the border circular areas determining, the center of circle in this region is exactly
Figure 409085DEST_PATH_IMAGE080
horizontal centroid, radius is
Figure 120689DEST_PATH_IMAGE080
half of width, requirement with
Figure 750832DEST_PATH_IMAGE080
projection is overlapping, be because in adjacent scanning, the segmentation that belongs to same object has continuity, therefore in the vertical direction not only, and in the horizontal direction can be apart from not too far away, the center of a segmentation is bound to drop in another segmentation limit of adjacent scanning slice.
If scanning
Figure 334261DEST_PATH_IMAGE016
comprise
Figure 770927DEST_PATH_IMAGE012
oK,
Figure 693884DEST_PATH_IMAGE018
, wherein
Figure 3642DEST_PATH_IMAGE026
.
Figure 759852DEST_PATH_IMAGE046
row comprises
Figure 2013105689548100002DEST_PATH_IMAGE086
individual sweep segment,
Figure 2013105689548100002DEST_PATH_IMAGE088
, .Current set
Figure 875576DEST_PATH_IMAGE072
comprise altogether
Figure 2013105689548100002DEST_PATH_IMAGE092
individual trunk segmentation.Algorithm according to row from
Figure 2013105689548100002DEST_PATH_IMAGE094
arrive order to each sweep segment in each row
Figure 341454DEST_PATH_IMAGE048
search for and distribute.Finally, we have obtained trunk segmentation set
Figure 2013105689548100002DEST_PATH_IMAGE098
, and each
Figure 9065DEST_PATH_IMAGE074
in sweep segment all store from bottom to up,
Figure 239189DEST_PATH_IMAGE060
,
Figure 2013105689548100002DEST_PATH_IMAGE100
,
Figure 2013105689548100002DEST_PATH_IMAGE102
it is sweep segment
Figure 2013105689548100002DEST_PATH_IMAGE104
the z coordinate of barycenter.
two, the extraction of geometric element
The estimation of circle model
After completing the identification of trunk segmentation, extract the potential geometric model of trunk segmentation.The fitting algorithm adopting is an iterative process, comprises two steps:
1) each sweep segment in trunk segmentation is justified to matching.
2) center of circle in trunk segmentation is fitted to trunk axis.
Because original point cloud cannot provide enough stem form information, the information of trunk curvature is particularly described, can cause larger error of fitting, we wish from the analyzing spot of each segmentation, to excavate not by the constant of noise, reduce the real radius of potential round model.
The sweep segment that the pitching scanning of same trees is obtained is not necessarily perpendicular to trunk axis, thus algorithm by sweep segment projection in the projection plane of trunk.Projection plane is crossed laser launching centre
Figure 2013105689548100002DEST_PATH_IMAGE106
and perpendicular to trunk axis.Owing to can not learning trunk axis direction in advance, so the normal of plane is initialized to the Z axis of robot local coordinate system.
Owing to hitting that the laser of trees counts, be invariant just, therefore obtain after projection scanning segmentation, we will utilize its angular span to estimate the radius of circle model, then according to subpoint, estimate the center of circle.
If projected segmentation comprise individual, be denoted as .If projection radius of a circle is
Figure 2013105689548100002DEST_PATH_IMAGE112
, it is variable to be estimated.
Figure 816036DEST_PATH_IMAGE112
meet:
Figure 2013105689548100002DEST_PATH_IMAGE114
Figure 2013105689548100002DEST_PATH_IMAGE116
with
Figure 2013105689548100002DEST_PATH_IMAGE118
be respectively
Figure 671472DEST_PATH_IMAGE112
the upper bound and lower bound.Their value only depends on end points
Figure 2013105689548100002DEST_PATH_IMAGE120
with
Figure 2013105689548100002DEST_PATH_IMAGE122
.But the position of these two points is uncertain due to the interference of noise.The situation when radius that Figure 3 shows that projection circle model reaches extreme value.Point
Figure 191315DEST_PATH_IMAGE106
laser emission center,
Figure 2013105689548100002DEST_PATH_IMAGE124
it is the center of circle of projection circle.On projection plane with
Figure 677791DEST_PATH_IMAGE106
for initial point is set up coordinate system.
Figure 454248DEST_PATH_IMAGE120
,
Figure 721282DEST_PATH_IMAGE122
polar coordinates be respectively
Figure 2013105689548100002DEST_PATH_IMAGE126
, .Below they will be used as known quantity statement
Figure 44816DEST_PATH_IMAGE116
with
Figure 385798DEST_PATH_IMAGE118
.
As shown in Fig. 3 (a), when get lower bound
Figure 84556DEST_PATH_IMAGE116
time,
Figure 759251DEST_PATH_IMAGE120
with
Figure 282637DEST_PATH_IMAGE122
two projection laser beams at place will be tangent with circle model.Due to
Figure 2013105689548100002DEST_PATH_IMAGE130
,
Figure 2013105689548100002DEST_PATH_IMAGE132
be two tangent lines of circle, therefore have
Figure 2013105689548100002DEST_PATH_IMAGE134
(1)
With
Figure 2013105689548100002DEST_PATH_IMAGE136
the length that represents tangent section, but in practice
Figure 2013105689548100002DEST_PATH_IMAGE138
with
Figure 2013105689548100002DEST_PATH_IMAGE140
may strictly not equate, therefore by their average, estimate :
Figure 2013105689548100002DEST_PATH_IMAGE142
(2)
The character tangent with circle according to straight line,
Figure 563893DEST_PATH_IMAGE116
can be solved by following equation:
Figure 2013105689548100002DEST_PATH_IMAGE144
(3)
Be different from
Figure 790082DEST_PATH_IMAGE116
,
Figure 371236DEST_PATH_IMAGE112
can not reach the upper bound
Figure 706402DEST_PATH_IMAGE118
.As shown in Fig. 3 (b), when
Figure 622275DEST_PATH_IMAGE118
during appearance, virtual point
Figure 2013105689548100002DEST_PATH_IMAGE146
with
Figure 2013105689548100002DEST_PATH_IMAGE148
two projection laser beams and the trunk at place are tangent. be previous point, be
Figure 2013105689548100002DEST_PATH_IMAGE152
a rear point,
Figure 2013105689548100002DEST_PATH_IMAGE154
really do not comprise this two points.If their polar coordinates under robot local coordinate system are with .The laser range of virtual point can arbitrarily arrange, and establishes .Due to
Figure 40156DEST_PATH_IMAGE032
with
Figure 256374DEST_PATH_IMAGE150
the two-dimensional scan plane at place is known, therefore scans angle with
Figure 2013105689548100002DEST_PATH_IMAGE164
determine.According to the angular resolution of two-dimensional laser sensor,
Figure 2013105689548100002DEST_PATH_IMAGE166
with
Figure 2013105689548100002DEST_PATH_IMAGE168
value can be by
Figure 2013105689548100002DEST_PATH_IMAGE170
with know by inference.
Especially, when be in the plane of scanning motion first time
Figure 2013105689548100002DEST_PATH_IMAGE174
(4)
When
Figure 366729DEST_PATH_IMAGE152
when in the plane of scanning motion, last is put
Figure 2013105689548100002DEST_PATH_IMAGE176
(5)
So, according to the relation between polar coordinate system and cartesian coordinate system, can be from
Figure 2013105689548100002DEST_PATH_IMAGE178
with
Figure 932447DEST_PATH_IMAGE152
local pole coordinate obtain its overall Cartesian coordinates, and then obtain the polar coordinates of subpoint on projection plane
Figure 2013105689548100002DEST_PATH_IMAGE180
with
Figure 2013105689548100002DEST_PATH_IMAGE182
.
Due to
Figure 2013105689548100002DEST_PATH_IMAGE184
, be two tangent lines of circle, therefore have
(6)
If
Figure 2013105689548100002DEST_PATH_IMAGE190
laser beam that tangent line is adjacent with it angle on projection plane,
Figure 2013105689548100002DEST_PATH_IMAGE192
the length of tangent line projection.At triangle
Figure 2013105689548100002DEST_PATH_IMAGE194
,
Figure 2013105689548100002DEST_PATH_IMAGE196
, middle utilization straight line and the tangent character of circle, sine, the cosine law, has following system of equations:
Figure 2013105689548100002DEST_PATH_IMAGE200
(7)
Wherein,
Figure 931233DEST_PATH_IMAGE136
,
Figure 289533DEST_PATH_IMAGE190
,
Figure 2013105689548100002DEST_PATH_IMAGE202
known quantity,
Figure 949054DEST_PATH_IMAGE118
,
Figure 734607DEST_PATH_IMAGE192
, it is unknown quantity.Three known quantities are denoted as respectively ,
Figure 2013105689548100002DEST_PATH_IMAGE208
,
Figure 2013105689548100002DEST_PATH_IMAGE210
, solving equations (7), obtains two solutions, get before radical sign minor for+solution for final, separate.
Figure 2013105689548100002DEST_PATH_IMAGE212
（8）
Due to consecutive point pair
Figure 2013105689548100002DEST_PATH_IMAGE214
with
Figure 2013105689548100002DEST_PATH_IMAGE216
symmetry,
Figure 96056DEST_PATH_IMAGE190
with
Figure 258047DEST_PATH_IMAGE136
value have two kinds may:
1) work as use with while calculating
Figure 2013105689548100002DEST_PATH_IMAGE218
Figure 2013105689548100002DEST_PATH_IMAGE220
Two formula substitution (8) formulas can be tried to achieve
Figure 2013105689548100002DEST_PATH_IMAGE222
.
2) work as use
Figure 95050DEST_PATH_IMAGE122
with while calculating
Figure 2013105689548100002DEST_PATH_IMAGE224
Figure 2013105689548100002DEST_PATH_IMAGE226
Two formula substitution (8) formulas can be tried to achieve
Figure 2013105689548100002DEST_PATH_IMAGE228
.
Figure 786855DEST_PATH_IMAGE118
end value be
Figure 2013105689548100002DEST_PATH_IMAGE230
(9)
If end points
Figure 179790DEST_PATH_IMAGE120
with
Figure 266564DEST_PATH_IMAGE122
in between two extreme value places separately, the probability of any point equates, therefore, gets
Figure 832675DEST_PATH_IMAGE112
expectation value conduct
Figure 806447DEST_PATH_IMAGE112
estimation:
Figure 2013105689548100002DEST_PATH_IMAGE232
(10)
Will as known quantity, use least square method to projection arc
Figure 58699DEST_PATH_IMAGE108
each point justify matching, try to achieve the center of circle
Figure 632768DEST_PATH_IMAGE124
coordinate
Figure 2013105689548100002DEST_PATH_IMAGE238
estimated value.
Point set is designated as
Figure 2013105689548100002DEST_PATH_IMAGE240
Figure 2013105689548100002DEST_PATH_IMAGE242
, coordinate
Figure 2013105689548100002DEST_PATH_IMAGE246
, need minimized objective function to be:
Figure 2013105689548100002DEST_PATH_IMAGE248
(11)
Order
Figure 484793DEST_PATH_IMAGE006
represent point
Figure 578651DEST_PATH_IMAGE244
distance with circumference
Figure 2013105689548100002DEST_PATH_IMAGE250
(12)
According to pertinent literature, have:
Figure 2013105689548100002DEST_PATH_IMAGE252
Figure 2013105689548100002DEST_PATH_IMAGE254
Figure 188755DEST_PATH_IMAGE006
be nonlinear function, use Levenberg-Marquardt method to minimize objective function, and with point set
Figure 797591DEST_PATH_IMAGE108
the x of barycenter, the conduct of y coordinate initial value.
Suppose the original sweep segment before projection corresponding its center of circle of round model is
Figure 2013105689548100002DEST_PATH_IMAGE260
, radius is
Figure 2013105689548100002DEST_PATH_IMAGE262
.
Figure 2013105689548100002DEST_PATH_IMAGE264
be defined as
Figure 718886DEST_PATH_IMAGE258
the z coordinate of barycenter.
Given
Figure 667251DEST_PATH_IMAGE264
after,
Figure 2013105689548100002DEST_PATH_IMAGE266
can pass through will from projected coordinate system, be transformed into robot local coordinate system
Figure 2013105689548100002DEST_PATH_IMAGE270
obtain.Under projected coordinate system, the axis mistake of tree
Figure 448256DEST_PATH_IMAGE124
and perpendicular to projection plane X-Y.Appoint and get 2 points on axis with
Figure 2013105689548100002DEST_PATH_IMAGE274
, calculate they
Figure 167557DEST_PATH_IMAGE270
coordinate under coordinate system
Figure 2013105689548100002DEST_PATH_IMAGE276
with , can determine thus
Figure 738216DEST_PATH_IMAGE276
with
Figure 806666DEST_PATH_IMAGE278
straight-line equation.Due to
Figure 2013105689548100002DEST_PATH_IMAGE280
point be also positioned on this straight line and
Figure 571622DEST_PATH_IMAGE264
known,
Figure 342001DEST_PATH_IMAGE280
point exists
Figure 529399DEST_PATH_IMAGE270
coordinate under coordinate system also can determine.
the centerline fit of trunk segmentation
From sweep segment, extract after circle model trunk segmentation
Figure 223596DEST_PATH_IMAGE074
in effective fitting circle center of circle will be used to matching center line .Fitting algorithm still adopts least square method.If
Figure 471038DEST_PATH_IMAGE074
the center of circle set of the effective fitting circle comprising is
Figure 2013105689548100002DEST_PATH_IMAGE286
, radius set is
Figure 2013105689548100002DEST_PATH_IMAGE288
,
Figure 2013105689548100002DEST_PATH_IMAGE290
.
Figure 354811DEST_PATH_IMAGE284
direction vector be
Figure 2013105689548100002DEST_PATH_IMAGE292
, point set
Figure 2013105689548100002DEST_PATH_IMAGE294
barycenter for
Figure 532852DEST_PATH_IMAGE284
on a bit.
For estimating
Figure 2013105689548100002DEST_PATH_IMAGE298
, need minimized objective function
Figure 2013105689548100002DEST_PATH_IMAGE300
If
Figure 2013105689548100002DEST_PATH_IMAGE302
for
Figure 2013105689548100002DEST_PATH_IMAGE304
matrix
Figure 2013105689548100002DEST_PATH_IMAGE306
Right
Figure 2013105689548100002DEST_PATH_IMAGE308
carry out SVD decomposition, select maximum singular value characteristic of correspondence vector conduct
Figure 58118DEST_PATH_IMAGE298
estimated value.
Now, for each trunk segmentation
Figure 538778DEST_PATH_IMAGE074
can define some attributes:
1)
Figure 519634DEST_PATH_IMAGE074
center line:
Figure 314415DEST_PATH_IMAGE284
.
2)
Figure 2013105689548100002DEST_PATH_IMAGE310
upper extreme point:
Figure 2013105689548100002DEST_PATH_IMAGE312
,
Figure 133335DEST_PATH_IMAGE284
on a bit, its z coordinate with
Figure 784896DEST_PATH_IMAGE074
the maximum z value of mid point equates.
3)
Figure 515699DEST_PATH_IMAGE310
lower extreme point:
Figure 2013105689548100002DEST_PATH_IMAGE314
,
Figure 848591DEST_PATH_IMAGE284
on a bit, its z coordinate with
Figure 584335DEST_PATH_IMAGE074
the minimum z value of mid point equates.
4)
Figure 672377DEST_PATH_IMAGE074
axis:
Figure 877093DEST_PATH_IMAGE310
, straight line
Figure 498829DEST_PATH_IMAGE284
upper with
Figure 167708DEST_PATH_IMAGE312
with
Figure 364334DEST_PATH_IMAGE314
line segment for end points.
5)
Figure 853084DEST_PATH_IMAGE074
radius:
Figure 2013105689548100002DEST_PATH_IMAGE316
,
Figure 980309DEST_PATH_IMAGE074
the mean value of upper effective radius of circle.
6) maximum radius: ,
Figure 291129DEST_PATH_IMAGE074
the maximal value of upper effective radius of circle.
After centerline fit completes, the direction vector of tree will become the new normal of projection plane.In order to improve the accuracy of matching, algorithm is carried out circle matching and these two steps of centerline fit repeatedly by iterative, until the direction vector of tree is stable, the iterations generally needing is twice.
three, the cluster of trunk segmentation and integration
The foundation of using trees to locate as robot as natural landmark, must guarantee that every one tree is only used unique and representative trunk segmentation as feature.The road sign so obtaining could be used to carry out associated one to one in different scanning.We utilize the spatial relationship of trunk segmentation axis first trunk segmentation to be carried out to cluster, then to belonging to the segmentation of same one tree, integrate.
the cluster of trunk segmentation
Pass through some steps of algorithm, some trunks, because the uncontinuity in its space has excessively been divided into many segmentations, will divide into groups to trunk segmentation here before, and each segmentation can be assigned to the trees grouping that it should subordinate.
Extraction result based on geometric model, by trunk segmentation
Figure 2013105689548100002DEST_PATH_IMAGE320
with
Figure 2013105689548100002DEST_PATH_IMAGE322
between distance definition be axis
Figure 657388DEST_PATH_IMAGE006
with
Figure 198091DEST_PATH_IMAGE008
between distance.
Figure 2013105689548100002DEST_PATH_IMAGE324
In addition, use line segment distance but not air line distance is to fall within on the extended line of line segment and cause the trunk segmentation on two far trees of space length to be divided into same cluster by mistake for fear of the public affairs point that hangs down.Real determine trunk segmentation in all dimensions in space position be its axis segment rather than central line.
Known line segment
Figure 264398DEST_PATH_IMAGE006
with
Figure 802827DEST_PATH_IMAGE008
the central line at place is
Figure 2013105689548100002DEST_PATH_IMAGE326
with
Figure 2013105689548100002DEST_PATH_IMAGE328
, their common vertical line is handed over
Figure 390803DEST_PATH_IMAGE326
with
Figure 420682DEST_PATH_IMAGE328
respectively at point
Figure 918660DEST_PATH_IMAGE002
with .If
Figure 765579DEST_PATH_IMAGE002
with
Figure 648084DEST_PATH_IMAGE004
drop on respectively line segment
Figure 938251DEST_PATH_IMAGE006
with
Figure 615220DEST_PATH_IMAGE008
upper, axial line distance equals air line distance.
Figure 2013105689548100002DEST_PATH_IMAGE330
Otherwise, establish
Figure 2013105689548100002DEST_PATH_IMAGE332
, have
(13)
Here,
Figure 866204DEST_PATH_IMAGE332
mean trunk segmentation
Figure 490083DEST_PATH_IMAGE320
on minimum point not higher than
Figure 382560DEST_PATH_IMAGE322
on minimum point, now claim
Figure 496009DEST_PATH_IMAGE320
below, by the distance definition of axis is upper extreme point
Figure 2013105689548100002DEST_PATH_IMAGE336
to straight line distance and
Figure 319292DEST_PATH_IMAGE008
lower extreme point
Figure 2013105689548100002DEST_PATH_IMAGE338
to straight line
Figure 92338DEST_PATH_IMAGE328
distance between smaller value.Fig. 4 has shown this situation.
If scanning
Figure 120337DEST_PATH_IMAGE016
comprise trunk segmentation set , will
Figure 881805DEST_PATH_IMAGE072
be divided into cluster (Cluster), the trunk segmentation in each cluster is subordinated to same one tree.
If k cluster comprise
Figure DEST_PATH_IMAGE342
individual trunk segmentation,
Figure DEST_PATH_IMAGE344
, and meet
Figure DEST_PATH_IMAGE346
.
Trunk segmentation
Figure 591748DEST_PATH_IMAGE320
to cluster
Figure 157858DEST_PATH_IMAGE340
distance definition be
Figure 131631DEST_PATH_IMAGE320
arrive
Figure 446200DEST_PATH_IMAGE340
in from its nearest trunk segmentation distance.
(14)
In the distance calculating method of trunk segmentation and cluster, we have emphasized the trunk segmentation impact on tolerance result near the end points at discontinuous place.Why doing like this, is because may be divided into multistage with the trunk of one tree, but only have current trunk segmentation and neutralize with one tree the discontinuous place, space existing between its nearest trunk segmentation, is only and causes both sides to be forced to the reason of segmentation.When near discontinuous place, the distance between trunk segmentation can be more and more less.Therefore, dissimilar according to discontinuous place, space, can analyze in same cluster the upper bound of distance between each segmentation.
As shown in Figure 5, discontinuously on the space of trunk point cloud be divided into following three classes, the distance between trunk segmentation also should meet corresponding conditions.
1) fracture, as Fig. 5 (a)), the center section of tree, because analyzing spot lacks, causes the trunk of its above and below to be forced to segmentation.Causing the reason of analyzing spot disappearance may be that trunk is blocked by the object in leaf or the place ahead, may be also that trees are apart from robot is too far away or trunk is meticulous.Now, the axial line distance of trunk segmentation is almost 0.
2) bending, as Fig. 5 (b), the trunk of differing heights has lofty slope difference.The axial line distance of trunk segmentation can not surmount the higher value in both sides' radius.
3) bifurcated, branch and trunk belong to different trunk segmentations.Their junction is the change point of topological structure.Branch type is discontinuous can be divided into again two kinds of situations:
I) as shown in left side situation in Fig. 5 (c), branch appears at trunk top.Both sides are in the vertical direction relation that continues, and the radius of trunk is generally large than branch radius.Now between branch and trunk, slope is different, the situation while being similar to bending, and the axial line distance between them can not be greater than trunk radius.
Ii) as shown in right side situation in Fig. 5 (c), branch grows from a side of trunk, and both sides are certain juxtaposition relationship.Now the distance of axis often branch lower extreme point to the distance between trunk axis, this distance be necessarily less than branch and trunk radius and.
In practice, fracture, bending, the situations such as bifurcated may be in minute intersegmental appearance simultaneously of same one tree.
We may safely draw the conclusion from the above analysis, works as segmentation
Figure 505422DEST_PATH_IMAGE320
be subordinated to cluster
Figure 62175DEST_PATH_IMAGE340
time,
Figure 156033DEST_PATH_IMAGE320
with in cluster from its nearest trunk segmentation
Figure 953087DEST_PATH_IMAGE348
between distance should not be greater than their maximum radius sum.
Figure DEST_PATH_IMAGE352
(15)
Due in actual conditions, the radius of trunk segmentation is not to equate everywhere, and therefore, the radius that we are used as threshold value should be two sections of trunks arc radius at close discontinuous place, and should not be the average of all arc radius in trunk segmentation.Yet discontinuous place, these spaces is also not easy to determine, so we use the radius upper limit of circular arc in trunk segmentation as threshold value, to prevent that some trunk segmentations from being isolated outside the tree cluster under own.
In clustering algorithm, we are first to segmentation set
Figure 185092DEST_PATH_IMAGE072
in each trunk segmentation sort.The object of doing is like this that the trunk segmentation that guarantees to be subordinated to same one tree is all to enter corresponding cluster according to order from low to high, while that is to say that top trunk section can not occur enters cluster, the root trunk section of its below not yet enters the situation of cluster, branch can not occur yet and prior to trunk, enter the situation of cluster.Set after sequence is
Figure DEST_PATH_IMAGE354
,
Figure DEST_PATH_IMAGE356
.
The current cluster set of initialization for empty set.Algorithm traversal trunk segmentation set
Figure 217639DEST_PATH_IMAGE072
, be each trunk section
Figure 916736DEST_PATH_IMAGE320
cluster under finding.Check whether each cluster meets (15) formula.If cluster
Figure 884692DEST_PATH_IMAGE340
satisfy condition, will
Figure 918507DEST_PATH_IMAGE320
insert
Figure 817062DEST_PATH_IMAGE340
.If
Figure 947829DEST_PATH_IMAGE320
do not find affiliated cluster, illustrated before this search, also do not run into
Figure 24369DEST_PATH_IMAGE320
trunk segmentation in affiliated tree, therefore will
Figure 51141DEST_PATH_IMAGE320
as in affiliated tree one section of trunk of close root start alone a new cluster
Figure DEST_PATH_IMAGE360
.Consider in the situation that some are extremely special,
Figure 504119DEST_PATH_IMAGE320
may find a plurality of satisfying condition
Figure 489393DEST_PATH_IMAGE340
.Now, chosen distance
Figure 986102DEST_PATH_IMAGE320
nearest cluster is as its subordinate cluster.
Because the trunk segmentation in this algorithm requirement cluster is according to from low paramount sequential storage, therefore trunk segmentation is inserted to cluster relevant to the close structure of tree.According to the type of tree, will
Figure 728930DEST_PATH_IMAGE340
be divided into Types Below, and with indicating
Figure DEST_PATH_IMAGE362
represent.
1)
Figure DEST_PATH_IMAGE364
,
Figure 408436DEST_PATH_IMAGE340
in only contain a segmentation.
2)
Figure DEST_PATH_IMAGE366
,
Figure 435166DEST_PATH_IMAGE340
in contain more than one segmentation, and
Figure 853509DEST_PATH_IMAGE340
in each segmentation between there is bifurcated relation.Each trunk segmentation is from low paramount storage until bifurcated for the first time, and all the other segmentations enter cluster in turn.The trunk segmentation of crotch is for the first time designated as
Figure DEST_PATH_IMAGE368
.
3)
Figure DEST_PATH_IMAGE370
,
Figure 97015DEST_PATH_IMAGE340
in contain more than one segmentation, and
Figure 626217DEST_PATH_IMAGE340
in each segmentation between there is not bifurcated relation.
Analysis based on above, will
Figure 773033DEST_PATH_IMAGE320
insert
Figure 424595DEST_PATH_IMAGE340
time position may exert an influence to trunk type.The step of insertion algorithm is as follows:
1) if
Figure DEST_PATH_IMAGE372
and be arranged in cluster best result section
Figure DEST_PATH_IMAGE374
on, will
Figure 163323DEST_PATH_IMAGE320
insert
Figure 977695DEST_PATH_IMAGE340
afterbody, establish
Figure DEST_PATH_IMAGE376
.
Here, "
Figure 924791DEST_PATH_IMAGE372
" illustrate that in cluster, existing trunk segmentation is arranged in turn by order from low to high, or only have a trunk segmentation in original cluster."
Figure 191824DEST_PATH_IMAGE320
be arranged in cluster best result section on " actual algorithm statement be
Figure 682772DEST_PATH_IMAGE320
the barycenter z coordinate of upper minimum scan bow is more than or equal to
Figure 676136DEST_PATH_IMAGE374
the minimum z coordinate of maximum scan arc.Use
Figure 617416DEST_PATH_IMAGE374
the minimum z coordinate of upper maximum scan arc and not use the highest z coordinate be to consider that sweep segment out-of-level done a kind of condition relax.
2) if
Figure DEST_PATH_IMAGE378
, will insert
Figure 769491DEST_PATH_IMAGE340
afterbody.
For the cluster of bifurcated type, we enter each son field of sequential storage of cluster and the segmentation of higher height by it.Therefore, no matter whether the higher height of this tree exists bifurcated again, or whether branch be divided into multistage, all no longer changes the type of cluster.Once set bifurcated, we just think that it is all the time
Figure 933756DEST_PATH_IMAGE366
type cluster.And, because we are only concerned about the trunk of root, for the position of branch or higher tree section, no longer distinguished.Now, trunk is Already in cluster, and crotch is also indicated for the first time, so as long as will
Figure 113065DEST_PATH_IMAGE320
as tail element, add
Figure 840718DEST_PATH_IMAGE340
, do not need to change cluster type.
3) if
Figure 218610DEST_PATH_IMAGE372
and be arranged in cluster best result section
Figure 905867DEST_PATH_IMAGE374
under, travel through according to the order of sequence
Figure 250261DEST_PATH_IMAGE340
find minimum bifurcated trunk.If on minimum sweep segment be not less than
Figure 191989DEST_PATH_IMAGE348
on minimum sweep segment, and also not higher than
Figure 329578DEST_PATH_IMAGE348
on maximum scan segmentation, and this condition met for the first time, so
Figure 149766DEST_PATH_IMAGE348
it is exactly the trunk segmentation of crotch for the first time.If and
Figure DEST_PATH_IMAGE380
.
the integration of trunk segmentation
Cluster after a given sequence , the object that trunk segmentation is integrated is that all segmentations are wherein combined into the dry section of a Naoki.If
Figure 508832DEST_PATH_IMAGE378
, first will
Figure 381979DEST_PATH_IMAGE368
after segmentation remove, be about to branch segment and remove.Then, the cluster that each segments is greater than to 1 , the segmentation in them is all joined in turn, so we change according to their direction of sequential search from low to high, and merges.
The method that direction checks is as follows, first segmentation from cluster
Figure DEST_PATH_IMAGE382
start, check successively whether the direction of a rear segmentation differs too large with previous comparing, with
Figure DEST_PATH_IMAGE386
between angle whether be less than threshold value
Figure DEST_PATH_IMAGE388
.Here, define two trunk segmentations with between angle between the angle that the becomes axis that is them (span is
Figure DEST_PATH_IMAGE390
) according to experience, allow .
Once too greatly direction difference stop attended operation, and no longer consider subsequent segment, by they all from
Figure 225859DEST_PATH_IMAGE340
with
Figure DEST_PATH_IMAGE394
middle deletion.Only retain the segmentation consistent with first segmentation axis direction, and they are end to end.Specific practice is, by
Figure 677569DEST_PATH_IMAGE386
each sweep segment is connected to current from low to high successively
Figure 696340DEST_PATH_IMAGE382
maximum scan segmentation on.
After merging, obtain
Figure 46550DEST_PATH_IMAGE382
to be the dry section of a Naoki, sweep segment therebetween perhaps has fracture but can not have obvious bending point.Next, right axis carry out matching again, and calculate association attributes.
So far, solved the problem of extracting natural landmark from forest environment, robot can use these road signs to navigate in conjunction with a certain location algorithm.Fig. 6, Fig. 7 are respectively that a secondary original 3 D laser scanning and the road sign of scale Forest Scene extracts result.Through on-the-spot operation, prove that the technology of the present invention is reliable and stable, for forest environment natural landmark extracting method provides a kind of method of science.

Claims (1)

1. a natural landmark for mobile robot extracting method in forest environment, is characterized in that the method comprising the steps of:
The 1.th pair of forest environment carries out 3 D laser scanning detection, utilizes the circular feature of trunk profile that three-dimensional point cloud is divided into sweep segment, according to space adjacency, sweep segment is organized into trunk segmentation;
The inadequate trunk segmentation of the 2.th geological information obtaining from the 1st step, extract circle model, and according to the center of circle, simulate the axis of trunk segmentation;
The 3.th by analyzing the discontinuous type in space of trunk segmentation, carry out cluster and integration, the difference according to trunk segmentation in space distribution, combines the segmentation that belongs to same trunk, remove crotch, and it is dry as road sign finally to obtain unique Naoki that represents trees simultaneously.
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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108647321A (en) * 2018-05-11 2018-10-12 长安大学 A kind of intelligence multi-source heterogeneous manufacture big data integrated model in workshop and semantic computation method
CN110780669A (en) * 2019-07-29 2020-02-11 苏州博田自动化技术有限公司 Forest robot navigation and information acquisition method
CN111325796A (en) * 2020-02-28 2020-06-23 北京百度网讯科技有限公司 Method and apparatus for determining pose of vision device
CN111461023A (en) * 2020-04-02 2020-07-28 山东大学 Method for quadruped robot to automatically follow pilot based on three-dimensional laser radar
CN114295118A (en) * 2021-12-30 2022-04-08 杭州海康机器人技术有限公司 Multi-robot positioning method, device and equipment

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108647321A (en) * 2018-05-11 2018-10-12 长安大学 A kind of intelligence multi-source heterogeneous manufacture big data integrated model in workshop and semantic computation method
CN108647321B (en) * 2018-05-11 2021-10-01 长安大学 Tree-shaped intelligent workshop manufacturing big data integrated modeling and semantic calculation method
CN110780669A (en) * 2019-07-29 2020-02-11 苏州博田自动化技术有限公司 Forest robot navigation and information acquisition method
CN111325796A (en) * 2020-02-28 2020-06-23 北京百度网讯科技有限公司 Method and apparatus for determining pose of vision device
CN111325796B (en) * 2020-02-28 2023-08-18 北京百度网讯科技有限公司 Method and apparatus for determining pose of vision equipment
CN111461023A (en) * 2020-04-02 2020-07-28 山东大学 Method for quadruped robot to automatically follow pilot based on three-dimensional laser radar
CN111461023B (en) * 2020-04-02 2023-04-18 山东大学 Method for quadruped robot to automatically follow pilot based on three-dimensional laser radar
CN114295118A (en) * 2021-12-30 2022-04-08 杭州海康机器人技术有限公司 Multi-robot positioning method, device and equipment
CN114295118B (en) * 2021-12-30 2024-01-26 杭州海康机器人股份有限公司 Positioning method, device and equipment for multiple robots

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