CN110262287A - Canopy height on-line water flushing method for the highly automated control of the harvest machinery ceding of Taiwan - Google Patents

Canopy height on-line water flushing method for the highly automated control of the harvest machinery ceding of Taiwan Download PDF

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Publication number
CN110262287A
CN110262287A CN201910632739.7A CN201910632739A CN110262287A CN 110262287 A CN110262287 A CN 110262287A CN 201910632739 A CN201910632739 A CN 201910632739A CN 110262287 A CN110262287 A CN 110262287A
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point
point cloud
cloud data
coordinate system
delaunay
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赵奉奎
李旭东
王宇歌
苏珊珊
谢璐阳
朱少华
夏兆君
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Nanjing Forestry University
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Nanjing Forestry University
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    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01D—HARVESTING; MOWING
    • A01D75/00—Accessories for harvesters or mowers
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00—Systems involving the use of models or simulators of said systems
    • G05B17/02—Systems involving the use of models or simulators of said systems electric

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  • Environmental Sciences (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
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Abstract

The invention discloses a kind of canopy height on-line water flushing methods for the highly automated control of the harvest machinery ceding of Taiwan, comprising: point cloud data is transformed into world coordinate system by the point cloud data for obtaining crop in front of harvester from polar coordinates；Region of interest is established, the point cloud data other than region of interest is rejected；Remove the noise point in point cloud data；Digital surface model is established based on a small amount of first control point and using Delaunay irregular triangle network method of formation；Digital terrain model is established using Delaunay irregular triangle network method of formation based on a small amount of second control point；Digital surface model is subtracted into digital terrain model, obtains canopy height model, and then obtain canopy height.The present invention can detect canopy height, ground level in front of harvest machinery, and the intelligent control for harvest machinery ceding of Taiwan height provides data foundation.

Description

Canopy height on-line water flushing method for the highly automated control of the harvest machinery ceding of Taiwan
Technical field
The invention belongs to Agricultural Intelligent System production fields, and in particular to one kind is used for the highly automated control of the harvest machinery ceding of Taiwan Canopy height on-line water flushing method.
Background technique
Grain loss rate during current harvester operation is about 2%~4%, and ceding of Taiwan height influences grain loss rate It is very big.In order to reduce grain loss rate, operating personnel needs constantly to adjust manipulation according to the canopy height and ground level of crop Bar.According to the automatic control of the information realizations ceding of Taiwan height such as crop canopies height, ground level, operating personnel's work can reduce Amount, mentions high control precision.Agriculturally often crop canopies height is carried out quick, lossless and accurate by laser radar technique Measurement, current measurement means mainly have high-altitude remote sensing, fix position ground radar and UAV system radar, however these methods The real-time control of harvest machinery is not cannot be used for.General information acquisition method currently used for the control of ceding of Taiwan height includes using Ultrasonic wave and feeler mechanism.It is easy to be interfered using ultrasonic wave when ultrasonic measurement crop canopies height, measurement accuracy and can It is not high by property.Feeler mechanism is mainly used for perceiving ground fluctuations situation, controls foundation as ceding of Taiwan height, can not detect work Object canopy height.
It is the intelligence of harvest machinery ceding of Taiwan height therefore, it is necessary to study crop canopies height, ground level estimation on line method Can control and data foundation is provided, enhancing combine harvester to different terrain, different cultivars, different upgrowth situation crop adaptation Property.
Summary of the invention
It is a kind of for harvest machinery the technical problem to be solved by the present invention is to provide in view of the above shortcomings of the prior art The canopy height on-line water flushing method of the highly automated control of the ceding of Taiwan, the canopy that this is used for the highly automated control of the harvest machinery ceding of Taiwan are high Degree on-line water flushing method can be evaluated whether canopy height, ground level in front of harvest machinery, be the intelligence of harvest machinery ceding of Taiwan height Control provides data foundation.
To realize the above-mentioned technical purpose, the technical scheme adopted by the invention is as follows:
A kind of canopy height on-line water flushing method for the highly automated control of the harvest machinery ceding of Taiwan, comprising the following steps:
(1), the point cloud data that crop in front of harvester is obtained by laser radar, the point cloud data that laser radar is exported It is transformed into world coordinate system from polar coordinates；
(2), region of interest is established, the point cloud data other than region of interest is rejected；
(3), the noise point in point cloud data is removed；
(4), the XOY horizontal plane for the point cloud data place coordinate system O-XYZ that step (3) obtain is divided into multiple sizes one The cell of cause establishes multiple histogram bodies using each cell as bottom surface, finds height value maximum respectively in each histogram body Point cloud, regard the point cloud searched out as the first control point, by all first mapping of control points to XOY plane, be based on XOY Digital surface model is established at the first control point and use Delaunay irregular triangle network method of formation in plane；
(5), the XOY horizontal plane for the point cloud data place coordinate system O-XYZ that step (3) obtain is divided into multiple sizes one The cell of cause establishes multiple histogram bodies using each cell as bottom surface, finds height value minimum respectively in each histogram body Point cloud, it is flat based on XOY by all second mapping of control points to XOY plane using the point cloud searched out as the second control point Digital terrain model is established at the second control point and use Delaunay irregular triangle network method of formation on face；
(6), digital surface model is subtracted into digital terrain model, obtains canopy height model, and then obtain canopy height.
Technical solution as a further improvement of that present invention, the step (1) specifically include:
Laser radar is mounted on harvester middle position, establishes laser radar coordinate system O respectivelyL-XLYLZLAnd harvester Coordinate system OV-XVYVZV；
The point cloud data that crop in front of harvester is obtained by laser radar, by the point cloud data of laser radar output from pole Coordinate is transformed into laser radar cartesian coordinate system OL-XLYLZLIt is interior, by laser radar coordinate system OL-XLYLZLInterior point cloud data It is transformed into harvester coordinate system OV-XVYVZVIt is interior；By harvester coordinate system OV-XVYVZVInterior point cloud data is transformed into world coordinates It is in O-XYZ.
Technical solution as a further improvement of that present invention, the step (2) specifically include:
A certain range in front of harvester is set as region of interest, rejects the point cloud data other than region of interest；
The step (3) specifically includes:
Intensity threshold is set, filters out the point cloud data that reflected intensity in point cloud data is less than intensity threshold, and use KD- Tree point cloud denoising method removes the noise point in point cloud data.
Technical solution as a further improvement of that present invention, based on the first control point on XOY plane in the step (4) And digital surface model is established using Delaunay irregular triangle network method of formation specifically:
(4.1), all first control points that will be mapped on XOY plane are put into a concentration, and point set is divided into two points The equal sub- point set of number；
(4.2), construction Delaunay triangulation network is concentrated in sub- point；
(4.3), the baseline and top line of the Delaunay triangulation network that two sub- point sets are constituted are separately connected；
(4.4), merge the Delaunay triangulation network of two sub- point sets from baseline to top line, and utilize Lawson local optimum Algorithm optimizes the triangulation network after merging, obtains the triangulation network for meeting Delaunay rule；
(4.5), the triangulation network that step (4.4) ultimately generates is mapped in O-XYZ three-dimensional coordinate system, obtains number Surface model.
Technical solution as a further improvement of that present invention, based on the second control point on XOY plane in the step (5) And digital terrain model is established using Delaunay irregular triangle network method of formation specifically:
(5.1), all second control points that will be mapped on XOY plane are put into a concentration, and point set is divided into two points The equal sub- point set of number；
(5.2), construction Delaunay triangulation network is concentrated in sub- point；
(5.3), the baseline and top line of the Delaunay triangulation network that two sub- point sets are constituted are separately connected；
(5.4), merge the Delaunay triangulation network of two sub- point sets from baseline to top line, and utilize Lawson local optimum Algorithm optimizes the triangulation network after merging, obtains the triangulation network for meeting Delaunay rule；
(5.5), the triangulation network that step (5.4) ultimately generates is mapped in O-XYZ three-dimensional coordinate system, obtains number Ground model.
The invention has the benefit that this is used for the canopy height on-line water flushing side of the highly automated control of the harvest machinery ceding of Taiwan Method can obtain the ground level in front of harvest machinery by digital terrain model, obtain harvest machinery by canopy height model The canopy height in front provides data foundation for the intelligent control of harvest machinery ceding of Taiwan height, enhances combine harvester to difference Landform, different cultivars, different upgrowth situation crop adaptability.
Detailed description of the invention
Fig. 1 is the work flow diagram of the present embodiment.
Fig. 2 is that the laser radar coordinate system of the present embodiment and harvester coordinate system define schematic diagram.
Fig. 3 is the polar coordinates and cartesian coordinate relation schematic diagram of the laser radar point cloud data of the present embodiment.
Fig. 4 is the region of interest ABCD schematic diagram of the present embodiment.
Fig. 5 is the irregular triangle network schematic diagram of the present embodiment.
Specific embodiment
A specific embodiment of the invention is further illustrated below according to Fig. 1 to Fig. 5:
Canopy height on-line water flushing side of the present embodiment using wheatland as object, for the highly automated control of the harvest machinery ceding of Taiwan Method, step is as shown in Figure 1, specific as follows:
(1), data prediction:
Laser radar is mounted on harvester middle position by bracket, establishes laser radar coordinate system O respectivelyL-XLYLZLWith Harvester coordinate system OV-XVYVZV, as shown in Figure 2.The point cloud data that crop in front of harvester is obtained by laser radar, will swash The point cloud data of optical radar output is transformed into laser radar cartesian coordinate system O from polar coordinatesL-XLYLZLIt is interior, by radar fix system OL-XLYLZLInterior point cloud data is transformed into harvester coordinate system OV-XVYVZVIt is interior, by harvester coordinate system OV-XVYVZVInterior point Cloud data are transformed into world coordinate system O-XYZ；Then region of interest is established, the point cloud other than region of interest is rejected；Finally Remove the noise spot in point cloud data.
(1.1) coordinate system is converted:
(a) laser radar coordinate system OL-XLYLZLIt is indicated with subscript L, coordinate origin is located in laser radar internal receipt device The heart, XLFor radar front, YLWith XLVertical and direction to the left, ZLPerpendicular to XLOLYLPlane is upward.Harvester coordinate system OV-XVYVZV I.e. vehicle axis system indicates that coordinate origin is overlapped with harvester mass center with subscript V, when harvester is in static on level road When state, XVIt is parallel to ground and is directed toward vehicle front, YVIt is directed toward on the left of driver, ZVPerpendicular to XVOVYVPlane is upward.Laser thunder It is polar form up to output data, as shown in Figure 3, it is therefore desirable to which polar coordinates are converted to by cartesian coordinate by formula (1)；
In formula, R is that sensing point (Data Point) arrives coordinate origin OLDistance, angle ω and α is as shown in figure 3, ω is Sensing point and coordinate origin OLLine and its in XLOLYLThe angle of plane projection, α are sensing point and coordinate origin OLLine exists XLOLYLPlane projection and YLThe angle of axis.
(b) laser radar cartesian coordinate system (i.e. laser radar coordinate system) OL-XLYLZLWith harvester coordinate system OV- XVYVZVIt not being overlapped, there are certain corner, coordinate origin is not also overlapped for each axis of radar fix system and each axis of harvester coordinate system, Laser radar coordinate system needs to convert to harvester coordinate system.According to installation site of the laser radar on harvester, laser thunder It is transformed into harvester coordinate system up to any point cloud P in coordinate system using formula (2):
In formula, Q is spin matrix, and T is translation matrix, and the method that R and T pass through calibration acquires.
(c) according to harvester driving path, by harvester coordinate system OV-XVYVZVInterior point cloud data is transformed into world's seat In mark system, world coordinate system is indicated with O-XYZ.
(1.2) region of interest is established, the point cloud data other than region of interest is rejected:
Laser radar has certain scanning angle γ, i.e. angle EOF in Fig. 4, such as SICK LMS151 scanning angle γ =270 °, so laser can be irradiated to the object of harvester two sides, it is wide according to the ceding of Taiwan in order to improve accuracy and reduce calculation amount Degree and laser radar installation site, establish region of interest.System only needs to detect harvester direction of advance a certain range of small Wheat canopy height, so removing two side point cloud of the ceding of Taiwan according to cloud coordinate.Due to blocking for the ceding of Taiwan, have one between CD line and the ceding of Taiwan Partial region can not be irradiated to (this partial region has been disposed before harvester drives to the position), so interested Area is set as in the ABCD rectangle in front of harvester.
(1.3) low intensity points and noise spot are removed:
In received point cloud data, a part point cloud intensity is very weak, this partial dot cloud Producing reason is mainly The bad or hypertelorism of detected object reflectivity.It is when the more remote object of laser irradiation there are also a kind of cloud, hot spot becomes larger, Beam of laser has been irradiated on different objects at (or on edge of the same object), and same light beam is reflected back by two objects Coming, radar is by two-beam institute's ranging from being averaged as cloud, however this reflection point not actually exists, therefore this Class point cloud is also noise.In order to filter out the excessively weak point cloud of intensity, an intensity threshold is set separately, it is low to filter out reflected intensity respectively In the point cloud data of intensity threshold.
Make object point cloud and unordered state at random is presented, in order to quickly remove noise, the space between point cloud data need to be established Topological relation.The present embodiment uses KD-Tree method division points cloud, searches the point of proximity of certain point later, will be with point of proximity apart from mistake Remote point is labeled as noise spot, is removed.Algorithm description is as follows:
(a), three dimensional point cloud is read in；
(b), point cloud data is launched into KD-Tree；
(c), after the completion of point cloud space divides, wherein any point p is searchediK nearest neighborWhereinFor piArest neighbors K node set；
(d), p is calculatediWith its k nearest neighborThe average value of interior each point distance
(e), willWith the threshold value D of settingσIt compares, ifThen think point piFor noise spot, it is deleted, it is no Then retain.
(f), above step is repeated, until point all in processing fixed point cloud.
By above step, the noise spot in a cloud space can be quickly removed.
(2), canopy height model foundation:
Canopy height model is the function to calculate each plant height, passes through irregular three using a small amount of critical control point Angle net establishes digital surface model and digital terrain model, using the difference of digital surface model and digital terrain model, establishes Canopy height model.
(2.1) digital surface model is established:
It is consistent that the XOY horizontal plane for the point cloud data place coordinate system O-XYZ that step (1.3) obtain is divided into multiple sizes Cell establish multiple histogram bodies using each cell as the bottom surface of histogram body, found respectively wherein in each histogram body Maximum cloud of height value (i.e. maximum cloud of Z axis value), regard all the points cloud searched out as the first control point, will own On first mapping of control points to XOY plane, not based on the first control point on XOY plane and using the Delaunay to divide and rule Regular triangular net method of formation establishes digital surface model；Specific step is as follows:
(2.1.1), it all first control points on XOY plane be will be mapped to is put into and concentrate, point set is divided into two It counts approximately equal sub- point set；
(2.1.2), recursively in sub- point concentration construction Delaunay triangulation network；
(2.1.3), the baseline and top line for being separately connected the Delaunay triangulation network that two sub- point sets are constituted；
(2.1.4), the Delaunay triangulation network for merging two sub- point sets from baseline to top line, and it is locally excellent using Lawson Change algorithm to optimize the triangulation network after merging, obtains the triangulation network for meeting Delaunay rule；
(2.1.5), the triangulation network that step (2.1.4) ultimately generates is mapped in O-XYZ three-dimensional coordinate system and (i.e. will The first control point on the triangulation network re-maps in O-XYZ three-dimensional coordinate system), obtain digital surface model s.
By above step, that is, produce the irregular triangle network s of characterization plant height as shown in Figure 5.
(2.2) digital terrain model is established:
Since ground point cloud is than sparse, so the wheatland point cloud data scanned is divided into smaller size in horizontal plane Cell, it may be assumed that the XOY horizontal plane for the point cloud data place coordinate system O-XYZ that step (1.3) obtain is divided into multiple sizes one It causes and the lesser cell of size using each cell as the bottom surface of histogram body establishes multiple histogram bodies, in each histogram body The smallest cloud of height value (i.e. the smallest cloud of Z axis value) is found respectively, using the point cloud searched out as the second control point, by institute Have on the second mapping of control points to XOY plane, based on the second control point on XOY plane and using the Delaunay to divide and rule Irregular triangle network method of formation establishes digital terrain model；The establishment process and digital surface model of digital terrain model were established Journey is similar, the specific steps are as follows:
(2.2.1), it all second control points on XOY plane be will be mapped to is put into and concentrate, point set is divided into two It counts equal sub- point set；
(2.2.2), construction Delaunay triangulation network is concentrated in sub- point；
(2.2.3), the baseline and top line for being separately connected the Delaunay triangulation network that two sub- point sets are constituted；
(2.2.4), the Delaunay triangulation network for merging two sub- point sets from baseline to top line, and it is locally excellent using Lawson Change algorithm to optimize the triangulation network after merging, obtains the triangulation network for meeting Delaunay rule；
(2.2.5), the triangulation network that step (2.2.4) ultimately generates is mapped in O-XYZ three-dimensional coordinate system, is obtained Digital terrain model.
(2.3) canopy height model foundation:
Digital terrain model is subtracted using digital surface model, obtains canopy height model, difference characterizes the exhausted of crop To height.
The ground level of the present embodiment indicates that canopy height is indicated by canopy height model by digital terrain model, Intelligent control for harvest machinery ceding of Taiwan height provides data foundation.
Protection scope of the present invention includes but is not limited to embodiment of above, and protection scope of the present invention is with claims Subject to, replacement, deformation, the improvement that those skilled in the art that any pair of this technology is made is readily apparent that each fall within of the invention Protection scope.

Claims (5)

1. a kind of canopy height on-line water flushing method for the highly automated control of the harvest machinery ceding of Taiwan, which is characterized in that including Following steps:
(1), the point cloud data that crop in front of harvester is obtained by laser radar, by the point cloud data of laser radar output from pole Coordinate is transformed into world coordinate system；
(2), region of interest is established, the point cloud data other than region of interest is rejected；
(3), the noise point in point cloud data is removed；
(4), that the XOY horizontal plane of coordinate system O-XYZ where point cloud data that step (3) obtain is divided into multiple sizes is consistent Cell establishes multiple histogram bodies using each cell as bottom surface, finds the maximum point of height value respectively in each histogram body Cloud regard the point cloud searched out as the first control point, by all first mapping of control points to XOY plane, is based on XOY plane On the first control point and digital surface model is established using Delaunay irregular triangle network method of formation；
(5), that the XOY horizontal plane of coordinate system O-XYZ where point cloud data that step (3) obtain is divided into multiple sizes is consistent Cell establishes multiple histogram bodies using each cell as bottom surface, finds the smallest point of height value respectively in each histogram body Cloud is based on XOY plane using the point cloud searched out as the second control point by all second mapping of control points to XOY plane The second control point and digital terrain model is established using Delaunay irregular triangle network method of formation；
(6), digital surface model is subtracted into digital terrain model, obtains canopy height model, and then obtain canopy height.
2. the canopy height on-line water flushing method according to claim 1 for the highly automated control of the harvest machinery ceding of Taiwan, It is characterized by:
The step (1) specifically includes:
Laser radar is mounted on harvester middle position, establishes laser radar coordinate system O respectivelyL-XLYLZLWith harvester coordinate It is OV-XVYVZV；
The point cloud data that crop in front of harvester is obtained by laser radar, by the point cloud data of laser radar output from polar coordinates It is transformed into laser radar cartesian coordinate system OL-XLYLZLIt is interior, by laser radar coordinate system OL-XLYLZLInterior point cloud data conversion To harvester coordinate system OV-XVYVZVIt is interior；By harvester coordinate system OV-XVYVZVInterior point cloud data is transformed into world coordinate system O- In XYZ.
3. the canopy height on-line water flushing method according to claim 2 for the highly automated control of the harvest machinery ceding of Taiwan, It is characterized by:
The step (2) specifically includes:
A certain range in front of harvester is set as region of interest, rejects the point cloud data other than region of interest；
The step (3) specifically includes:
Intensity threshold is set, filters out the point cloud data that reflected intensity in point cloud data is less than intensity threshold, and use KD-Tree point Cloud denoising method removes the noise point in point cloud data.
4. the canopy height on-line water flushing method according to claim 1 for the highly automated control of the harvest machinery ceding of Taiwan, It is characterized by:
It is built based on the first control point on XOY plane in the step (4) and using Delaunay irregular triangle network method of formation Vertical digital surface model specifically:
(4.1), all first control points that will be mapped on XOY plane are put into a concentration, and point set is divided into two points phases Deng sub- point set；
(4.2), construction Delaunay triangulation network is concentrated in sub- point；
(4.3), the baseline and top line of the Delaunay triangulation network that two sub- point sets are constituted are separately connected；
(4.4), merge the Delaunay triangulation network of two sub- point sets from baseline to top line, and utilize Lawson Local Optimization Algorithm The triangulation network after merging is optimized, the triangulation network for meeting Delaunay rule is obtained；
(4.5), the triangulation network that step (4.4) ultimately generates is mapped in O-XYZ three-dimensional coordinate system, obtains digital surface Model.
5. the canopy height on-line water flushing method according to claim 1 for the highly automated control of the harvest machinery ceding of Taiwan, It is characterized by:
It is built based on the second control point on XOY plane in the step (5) and using Delaunay irregular triangle network method of formation Vertical digital terrain model specifically:
(5.1), all second control points that will be mapped on XOY plane are put into a concentration, and point set is divided into two points phases Deng sub- point set；
(5.2), construction Delaunay triangulation network is concentrated in sub- point；
(5.3), the baseline and top line of the Delaunay triangulation network that two sub- point sets are constituted are separately connected；
(5.4), merge the Delaunay triangulation network of two sub- point sets from baseline to top line, and utilize Lawson Local Optimization Algorithm The triangulation network after merging is optimized, the triangulation network for meeting Delaunay rule is obtained；
(5.5), the triangulation network that step (5.4) ultimately generates is mapped in O-XYZ three-dimensional coordinate system, obtains digital ground Model.
CN201910632739.7A 2019-07-14 2019-07-14 Canopy height on-line water flushing method for the highly automated control of the harvest machinery ceding of Taiwan Pending CN110262287A (en)

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