CN115142513B - Control method and device for excavator, processor and storage medium - Google Patents
Control method and device for excavator, processor and storage medium Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 66
- 239000000463 material Substances 0.000 claims abstract description 79
- 238000012545 processing Methods 0.000 claims abstract description 44
- 238000009412 basement excavation Methods 0.000 claims abstract description 42
- 230000011218 segmentation Effects 0.000 claims description 35
- 230000001788 irregular Effects 0.000 claims description 8
- 230000008569 process Effects 0.000 description 14
- 238000005065 mining Methods 0.000 description 9
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- E—FIXED CONSTRUCTIONS
- E02—HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING
- E02F—DREDGING; SOIL-SHIFTING
- E02F9/00—Component parts of dredgers or soil-shifting machines, not restricted to one of the kinds covered by groups E02F3/00 - E02F7/00
- E02F9/20—Drives; Control devices
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/762—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
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Abstract
The embodiment of the invention provides a control method and device for an excavator, a processor and a storage medium, and belongs to the field of engineering machinery. The control method for the excavator comprises the following steps: acquiring point cloud data of a target area; processing the point cloud data to identify an object to be identified in the target area, wherein the object to be identified comprises a stockpile and a mine card; determining a target excavation point of a material pile and a target loading point of a mine truck; excavating the material pile according to the target excavation point; unloading the excavated material onto the mine truck according to the target loading point. The invention can improve the automation degree of the excavator operation.
Description
Technical Field
The present invention relates to the field of engineering machinery, and in particular, to a control method and apparatus for an excavator, a processor, and a storage medium.
Background
With the development of unmanned technology, industry autopilot is also attracting attention. The excavator is one of the most main machine types in the engineering machinery field, can cope with various working conditions and finish various construction requirements, and has higher requirements on automatic driving technical operation. At present, most of the excavators also need on-site personnel to monitor and control the excavators to perform the actions of digging and discharging, and the problem of low operation automation degree exists.
Disclosure of Invention
The embodiment of the invention aims to provide a control method and device for an excavator, a processor and a storage medium, so as to solve the problem of low automation degree of excavator operation in the prior art.
To achieve the above object, a first aspect of an embodiment of the present invention provides a control method for an excavator, including:
Acquiring point cloud data of a target area;
processing the point cloud data to identify an object to be identified in the target area, wherein the object to be identified comprises a stockpile and a mine card;
determining a target excavation point of a material pile and a target loading point of a mine truck;
excavating the material pile according to the target excavation point;
Unloading the excavated material onto the mine truck according to the target loading point.
In the embodiment of the invention, processing the point cloud data to identify the object to be identified of the target area comprises the following steps: establishing a three-dimensional point cloud map according to the point cloud data; performing clustering segmentation processing on the three-dimensional point cloud map to obtain a processed three-dimensional point cloud map, wherein the processed three-dimensional point cloud map comprises target point cloud sets of corresponding categories of objects to be identified; converting the processed three-dimensional point cloud map into a two-dimensional grid map; and identifying the grid where the target point cloud set is located in the two-dimensional grid map based on a preset identification strategy so as to identify the object to be identified.
In the embodiment of the invention, clustering segmentation processing is performed on the three-dimensional point cloud map to obtain a processed three-dimensional point cloud map, which comprises the following steps: performing ground segmentation processing on the three-dimensional point cloud map to obtain a first point cloud map, wherein the first point cloud map does not comprise the ground; clustering the first point cloud map to obtain a second point cloud map, wherein the second point cloud map comprises a plurality of types of point cloud sets; and carrying out segmentation processing on the second point cloud map to obtain a processed three-dimensional point cloud map.
In the embodiment of the invention, the second point cloud map is subjected to segmentation processing to obtain a processed three-dimensional point cloud map, which comprises the following steps: determining the number of point clouds in each point cloud set in the second point cloud map; and removing the point cloud sets with the number of the point clouds being smaller than a preset number threshold value to obtain the processed three-dimensional point cloud map.
In the embodiment of the invention, based on a preset identification strategy, the identification of the grid where the target point cloud set is located in the two-dimensional grid map to identify the object to be identified comprises the following steps: determining a grid height value corresponding to each grid in the two-dimensional grid map, wherein the grid height value is the maximum height value in point cloud data in the grids; determining relevant information of grid height values of grids where cloud sets of target points are located in a two-dimensional grid map, wherein the relevant information comprises discrete degree parameters and average values; identifying a grid corresponding ore card where the target point cloud set is located under the condition that the discrete degree parameter is smaller than a preset discrete degree threshold value and the average value is larger than a preset average value; and identifying a grid corresponding stockpile where the target point cloud set is located under the condition that the discrete degree parameter is larger than or equal to a preset discrete degree threshold value and/or the average value is smaller than or equal to a preset average value.
In the embodiment of the invention, processing the point cloud data to identify the object to be identified of the target area comprises the following steps: establishing a three-dimensional point cloud map according to the point cloud data; and dividing the three-dimensional point cloud map based on the deep learning point cloud dividing algorithm to identify the object to be identified.
In an embodiment of the invention, determining a target excavation point of a stockpile and a target loading point of a mine truck comprises: establishing a material pile three-dimensional geometric model corresponding to the material pile according to the point cloud data corresponding to the material pile; establishing a three-dimensional geometric model of the mine card corresponding to the material pile according to the point cloud data corresponding to the mine card; respectively carrying out gridding treatment on the material pile three-dimensional geometric model and the mine card three-dimensional geometric model to obtain a plurality of first regular geometric bodies corresponding to the material pile three-dimensional geometric model and a plurality of second regular geometric bodies corresponding to the mine card three-dimensional geometric model; determining a center point of the first regular geometric body as a target excavation point of the stockpile; a target loading point for the mine card is determined based on the center point of the second regular geometric body.
In an embodiment of the present invention, determining a target loading point of the mine card according to a center point of the second regular geometric body includes: determining a loading area of the ore card; and determining the central point of the second irregular geometric body corresponding to the loading area as a target loading point of the ore card.
In the embodiment of the invention, the control method further comprises the following steps: the center point of the first plurality of regular geometric shapes having the greatest height is determined as the initial target excavation point of the stockpile.
In the embodiment of the invention, the control method further comprises the following steps: and determining the center point farthest from the excavator among the center points of the second irregular geometries corresponding to the loading areas as an initial target loading point of the ore card.
In the embodiment of the invention, excavating the material pile according to the target excavation point comprises the following steps: excavating the material pile according to the target excavation point based on a preset excavation strategy; unloading the excavated material onto the mine truck according to the target loading point, including: and unloading the excavated material to the ore card according to the target loading point based on a preset unloading strategy.
In the embodiment of the invention, acquiring the point cloud data of the target area comprises the following steps: and acquiring point cloud data of the target area by at least one mode of a laser radar, a global navigation satellite system and an information network system.
A second aspect of an embodiment of the present invention provides a processor configured to perform the control method for an excavator according to the above.
A third aspect of an embodiment of the present invention provides a control apparatus for an excavator, including: the point cloud data acquisition equipment is used for acquiring point cloud data of the target area; and a processor according to the above.
In an embodiment of the present invention, the point cloud data acquisition device includes at least one of: lidar, global navigation satellite systems, and information network systems.
A fourth aspect of an embodiment of the present invention provides an excavator, including: the control device for an excavator according to the above.
A fifth aspect of an embodiment of the present invention provides a machine-readable storage medium having stored thereon instructions that, when executed by a processor, cause the processor to perform a control method for an excavator according to the above.
According to the control method for the excavator, the point cloud data of the target area are obtained, the point cloud data are processed to identify the object to be identified of the target area, so that the target excavation point of the material pile and the target loading point of the ore card are determined, the material pile is excavated according to the target excavation point, and the excavated materials are unloaded onto the ore card according to the target loading point. According to the method, on-site personnel are not required to monitor and control the excavator to perform the actions of digging and unloading, the identification of the object to be identified in the target area is realized by acquiring the point cloud data of the target area, so that the positions of a material pile and a mine card are accurately determined, automatic path planning and automatic operation planning can be realized, the accurate positioning of the digging working position and the unloading working position of the excavator can be realized by determining the target digging point of the material pile and the target loading point of the mine card, the working efficiency of the excavator can be improved according to the target digging point and the target loading point, the automation degree of the excavator operation is improved, the working effectiveness of the excavator is ensured, and the production efficiency is further improved.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain, without limitation, the embodiments of the invention. In the drawings:
FIG. 1 schematically illustrates a flow chart of a control method for an excavator in one embodiment of the present invention;
FIG. 2 schematically illustrates a flowchart of a cloud data processing step in accordance with an embodiment of the present invention;
FIG. 3 schematically illustrates a communication schematic of an excavator in an embodiment of the present invention;
fig. 4 schematically shows a flow chart of the step of identifying an object to be identified in an embodiment of the invention.
Detailed Description
The following describes the detailed implementation of the embodiments of the present invention with reference to the drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the invention, are not intended to limit the invention.
Fig. 1 schematically shows a flow chart of a control method for an excavator in an embodiment of the present invention. As shown in fig. 1, in an embodiment of the present invention, a control method for an excavator is provided, and the method is applied to a processor for explanation, and the method may include the following steps:
Step S102, obtaining point cloud data of a target area.
It is understood that the target area is a work area of the excavator.
Specifically, the processor may acquire point cloud data of a target area of the excavator, where a specific acquisition mode of the point cloud data may be laser measurement, a laser measurement device such as a laser radar, or photogrammetry, a photogrammetry device such as a photographic scanner, or a global navigation satellite system (Global Navigation SATELLITE SYSTEM, GNSS).
Step S104, processing the point cloud data to identify an object to be identified in the target area, wherein the object to be identified comprises a stockpile and a mine card.
It will be appreciated that the objects to be identified are objects in the target area that need to be identified, including stockpiles and mine cards.
Specifically, the processor may process the point cloud data, for example, identify the point cloud data through an identification algorithm or model, so as to determine that the object to be identified is a stockpile or a mine card.
Fig. 2 schematically illustrates a flowchart of the point cloud data processing steps according to an embodiment of the present invention, in which the point cloud data is processed to identify an object to be identified in a target area, including the following steps S202 to S208:
Step S202, a three-dimensional point cloud map is built according to the point cloud data.
Specifically, after obtaining the point cloud data of the target area, the processor may establish a corresponding three-dimensional point cloud map according to the point cloud data, for example, may establish the three-dimensional point cloud map through a map construction algorithm (e.g., a laser SLAM algorithm). In one embodiment, the processor may build a three-dimensional point cloud map (i.e., a three-dimensional environment map) by fusing laser radar data, global navigation satellite system data, or an information network system (INS, information Network System) with a map building algorithm.
Step S204, performing clustering segmentation processing on the three-dimensional point cloud map to obtain a processed three-dimensional point cloud map, wherein the processed three-dimensional point cloud map comprises target point cloud sets of corresponding categories of objects to be identified.
It is to be understood that the clustering segmentation process may include two processing modes, namely, clustering process and segmentation process, the sequence of which is not limited, and it is to be understood that the clustering process is used for classification, and specifically, a label may be set for classification, and the segmentation process, namely, removal process, is used for removing or segmenting unnecessary data. The processed three-dimensional point cloud map, namely the three-dimensional point cloud map after cluster segmentation processing, comprises a target point cloud set of the corresponding class of the object to be identified (comprising a stockpile and a mine card), namely the target point cloud set is a set of point cloud data of the corresponding class of the object to be identified, and the target point cloud set is a set of point cloud data corresponding to the stockpile, for example, a set of point cloud data corresponding to the mine card.
Specifically, the processor may perform cluster segmentation processing on the three-dimensional point cloud map to obtain a processed three-dimensional point cloud map including a target point cloud set of a corresponding class of the object to be identified (including a stockpile and an ore card), and the clustering segmentation processing may be performed by a clustering algorithm and/or a segmentation algorithm, and the specific algorithm is not described herein.
Step S206, converting the processed three-dimensional point cloud map into a two-dimensional grid map.
Specifically, the processor may perform two-dimensional projection and rasterization on the three-dimensional point cloud map, so that a two-dimensional grid map may be obtained, and it is understood that the two-dimensional grid map also includes a set of target point clouds of a class corresponding to the object to be identified.
Step S208, based on a preset identification strategy, the grids where the target point cloud set is located in the two-dimensional grid map are identified, so that the object to be identified is identified.
It will be appreciated that the identification strategy is a preset identification method for identifying objects to be identified (including stockpiles and mine cards).
Specifically, the processor can identify the grid where the target point cloud set is located in the two-dimensional grid map based on a preset identification strategy, so that whether the object to be identified is a stockpile or a mine card is identified. Further, the preset identification policy can identify whether the category corresponding to the target point cloud set is a stockpile or a mine card according to the related information (such as the gravity center position) of the two-dimensional grid map.
Step S104 is followed by step S106 of determining a target excavation point of the stockpile and a target loading point of the mine truck.
It will be appreciated that the target excavation point is the location where the excavator excavates the pile. The target loading point is the position where the excavator loads material on the mine truck after the excavating action.
Specifically, the processor may determine the target mining point of the stockpile and the target loading point of the mine card based on a preset target mining point determination algorithm and target loading point determination algorithm, respectively.
And S108, excavating the material pile according to the target excavation point.
Specifically, the processor may control the working device of the excavator to excavate the target excavation point of the stockpile after determining the target excavation point.
And step S110, unloading the excavated material onto the ore card according to the target loading point.
Specifically, after determining the target loading point, the processor may control a work device of the excavator to unload the excavated material onto the mine truck based on the target loading point.
According to the control method for the excavator, the point cloud data of the target area are obtained, the point cloud data are processed to identify the object to be identified of the target area, so that the target excavation point of the material pile and the target loading point of the ore card are determined, the material pile is excavated according to the target excavation point, and the excavated materials are unloaded onto the ore card according to the target loading point. According to the method, on-site personnel are not required to monitor and control the excavator to perform the actions of digging and unloading, the identification of the object to be identified in the target area is realized by acquiring the point cloud data of the target area, so that the positions of a material pile and a mine card are accurately determined, automatic path planning and automatic operation planning can be realized, the accurate positioning of the digging working position and the unloading working position of the excavator can be realized by determining the target digging point of the material pile and the target loading point of the mine card, the working efficiency of the excavator can be improved according to the target digging point and the target loading point, the automation degree of the excavator operation is improved, the working effectiveness of the excavator is ensured, and the production efficiency is further improved.
In one embodiment, performing cluster segmentation processing on the three-dimensional point cloud map to obtain a processed three-dimensional point cloud map, including: performing ground segmentation processing on the three-dimensional point cloud map to obtain a first point cloud map, wherein the first point cloud map does not comprise the ground; clustering the first point cloud map to obtain a second point cloud map, wherein the second point cloud map comprises a plurality of types of point cloud sets; and carrying out segmentation processing on the second point cloud map to obtain a processed three-dimensional point cloud map.
It is understood that the ground segmentation process is used to segment ground point cloud data in a three-dimensional point cloud map, the first point cloud map being a three-dimensional point cloud map that does not include ground point cloud data.
Specifically, the processor may perform ground segmentation processing on the three-dimensional point cloud map to remove the ground, so as to obtain a first point cloud map that does not include the ground, and further perform clustering processing on the first point cloud map to obtain a second point cloud map that includes a plurality of class point cloud sets, where each class point cloud set may include a plurality of class labels such as a material pile, a mine card, a tree, and a sundries, for example, the second point cloud map may perform segmentation processing on the second point cloud map to remove a point cloud set that is not an object to be identified (including the material pile and the mine card), so as to obtain a processed three-dimensional point cloud map, and the processed three-dimensional point cloud map includes a target point cloud set of a class corresponding to the object to be identified.
In the embodiment of the invention, the ground is removed by the ground segmentation processing, and then the clustering processing is performed, so that the classification of the point cloud data except the ground is realized, and then the segmentation processing is performed, so that the point cloud data set comprising the stockpiles and the mine cards can be obtained, and the recognition efficiency and the recognition accuracy of the object to be recognized can be improved.
Further, in an embodiment, performing ground segmentation processing on the three-dimensional point cloud map to obtain a first point cloud map may include obtaining a height value of the point cloud, and removing point cloud data with the height value smaller than a preset threshold, that is, removing ground point cloud data, so as to implement ground segmentation processing. By comparing the height values, the ground segmentation processing can be rapidly realized, and the recognition efficiency is improved.
In one embodiment, the segmentation processing is performed on the second point cloud map to obtain a processed three-dimensional point cloud map, including: determining the number of point clouds in each point cloud set in the second point cloud map; and removing the point cloud sets with the number of the point clouds being smaller than a preset number threshold value to obtain the processed three-dimensional point cloud map.
It can be understood that the preset number threshold is a reference threshold of the number of point clouds in the preset point cloud set, and only the point cloud set reaching the preset number threshold corresponds to the stockpile or the mine card.
It can be appreciated that the processor may determine the number of point clouds in each of the point cloud sets in the second point cloud map, compare the number of point clouds with a preset number threshold, and remove the point cloud sets with the number of point clouds less than the preset number threshold, thereby obtaining a three-dimensional point cloud map including the target point cloud sets of the corresponding class of the object to be identified.
In the embodiment of the invention, the point cloud set of the non-object to be identified can be removed by comparing the number of the point clouds, so that the identification efficiency of the object to be identified is improved, and the identification time is shortened.
In one embodiment, based on a preset recognition policy, recognizing a grid where a target point cloud set is located in a two-dimensional grid map to recognize an object to be recognized includes: determining a grid height value corresponding to each grid in the two-dimensional grid map, wherein the grid height value is the maximum height value in point cloud data in the grids; determining relevant information of grid height values of grids where cloud sets of target points are located in a two-dimensional grid map, wherein the relevant information comprises discrete degree parameters and average values; identifying a grid corresponding ore card where the target point cloud set is located under the condition that the discrete degree parameter is smaller than a preset discrete degree threshold value and the average value is larger than a preset average value; and identifying a grid corresponding stockpile where the target point cloud set is located under the condition that the discrete degree parameter is larger than or equal to a preset discrete degree threshold value and/or the average value is smaller than or equal to a preset average value.
It is understood that the relevant information of the grid height values may include a degree of discretization parameter and a mean value, wherein the degree of discretization parameter is the degree of discretization information characterizing the grid height values of a plurality of grids, and a specific degree of discretization parameter may include, but is not limited to, a variance or a standard deviation, etc. The preset discrete degree threshold value is a preset discrete degree parameter threshold value. The preset average value is an average value threshold value which is preset and used for representing grid height values of a plurality of grids.
Specifically, the processor may determine a maximum height value of point cloud data in each grid in the two-dimensional grid map, determine the maximum height value as a grid height value corresponding to the grid, determine relevant information (including a discrete degree parameter and an average value) of the grid height values of the grids covered by each target point cloud set, further compare the discrete degree parameter with a preset discrete degree threshold, compare the average value with the preset average value, and if the discrete degree parameter is smaller than the preset discrete degree threshold and the average value is greater than the preset average value, identify that the grid where the target point cloud set is located corresponds to a mine card, otherwise, identify that the grid where the target point cloud set is located is a stockpile.
In the embodiment of the invention, the grid height value of each grid in the two-dimensional grid map is determined, the discrete degree parameter and the average value of the grid height value of the grid where the target point cloud set is positioned are compared, and the discrete degree parameter and the average value are respectively compared with the preset discrete degree threshold value and the preset average value, so that whether the target point cloud set corresponds to the mine card or the material pile is judged, the identification accuracy of the object to be identified can be improved, the identification rate can be accelerated, and the identification efficiency can be improved.
In one embodiment, processing the point cloud data to identify an object to be identified of a target area includes: establishing a three-dimensional point cloud map according to the point cloud data; and dividing the three-dimensional point cloud map based on the deep learning point cloud dividing algorithm to identify the object to be identified.
Specifically, after the processor obtains the point cloud data of the target area, a corresponding three-dimensional point cloud map can be established according to the point cloud data, the three-dimensional point cloud map can be segmented through a deep learning point cloud segmentation algorithm, for example, pointNet, voxelNet and the like, the material pile and the ore card can be directly segmented from the three-dimensional point cloud map through the deep learning point cloud segmentation algorithm, the recognition precision of the object to be recognized is improved, the error rate of the segmentation of the three-dimensional point cloud map is greatly reduced, the object to be recognized can be segmented from the three-dimensional point cloud map more accurately, and the purpose of accurately recognizing the material pile and the ore card is achieved. Further, the specific process of building the three-dimensional point cloud map according to the point cloud data is described in the above embodiment, and will not be described herein.
In one embodiment, determining a target excavation point of a stockpile and a target loading point of a mine truck comprises: establishing a material pile three-dimensional geometric model corresponding to the material pile according to the point cloud data corresponding to the material pile; establishing a three-dimensional geometric model of the mine card corresponding to the material pile according to the point cloud data corresponding to the mine card; respectively carrying out gridding treatment on the material pile three-dimensional geometric model and the mine card three-dimensional geometric model to obtain a plurality of first regular geometric bodies corresponding to the material pile three-dimensional geometric model and a plurality of second regular geometric bodies corresponding to the mine card three-dimensional geometric model; determining a center point of the first regular geometric body as a target excavation point of the stockpile; a target loading point for the mine card is determined based on the center point of the second regular geometric body.
It will be appreciated that the stockpile three-dimensional geometric model is an circumscribed regular geometric body, such as a cube, comprising the set of point clouds in which the stockpile is located, and the mine card three-dimensional geometric model is an circumscribed regular geometric body, such as a cube, comprising the set of point clouds to which the mine card corresponds.
Specifically, the processor may establish a material pile three-dimensional geometric model corresponding to the material pile according to point cloud data corresponding to the material pile, establish a mine card three-dimensional geometric model corresponding to the material pile according to point cloud data corresponding to the mine card, and respectively perform gridding treatment on the material pile three-dimensional geometric model and the mine card three-dimensional geometric model to divide the material pile three-dimensional geometric model and the mine card three-dimensional geometric model into a plurality of first regular geometric bodies and a plurality of second regular geometric bodies respectively, determine center point coordinates of the first regular geometric bodies and center point coordinates of the second regular geometric bodies, determine the center point coordinates of the first regular geometric bodies as target mining points of the material pile, and determine the center point coordinates of the second regular geometric bodies as target loading points of the mine card.
In one embodiment, determining a target loading point for the mine card from the center point of the second regular geometric body comprises: determining a loading area of the ore card; and determining the central point of the second irregular geometric body corresponding to the loading area as a target loading point of the ore card.
It will be appreciated that the loading area of the mine truck is the area of the mine truck that is used to load material excavated by the excavator.
Specifically, the processor may determine a loading area of the mine card, and the specific method for determining the loading area may determine a second regular geometric body that the height value is above a preset height threshold and that the second regular geometric body does not include point cloud data or includes less point cloud data than a smaller threshold as the loading area, and determine a center point of the second regular geometric body corresponding to the loading area as a target loading point of the mine card.
In one embodiment, the control method for an excavator further includes: the center point of the first plurality of regular geometric shapes having the greatest height is determined as the initial target excavation point of the stockpile.
It can be appreciated that the center point with the greatest height among the center points of the first regular geometric bodies is determined and used as the initial target excavation point of the material pile, so that the excavator can excavate from the highest position of the material pile, and the regular excavation can be realized, and the excavation effect of the excavator is improved.
In one embodiment, the control method for an excavator further includes: and determining the center point farthest from the excavator among the center points of the second irregular geometries corresponding to the loading areas as an initial target loading point of the ore card.
It can be understood that the initial target loading point is the position where the loading of the material begins first, the center point farthest from the excavator among the center points of the second irregular geometric bodies corresponding to the loading area is determined and used as the initial target loading point of the mine truck, and the material is unloaded from the position farthest from the excavator in the loading area, so that the unloading is performed regularly, and the working efficiency of the excavator is improved.
In other embodiments, the processor may also determine a center point of the first irregular geometry corresponding to the loading area, which is the most left or right of the center points with the highest height value, as the initial target loading point of the mining card.
In one embodiment, excavating a pile according to a target excavation point includes: and excavating the material pile according to the target excavation point based on a preset excavation strategy.
It will be appreciated that the preset excavation strategy is a preset strategy for excavating a pile by an excavator, for example, excavating in order of the position of the target excavation point from left to right and/or from top to bottom, and the like.
Specifically, the processor may excavate the stockpile in the order of left-to-right and top-to-bottom of the location of the target excavation point based on a preset excavation strategy (e.g., left-to-right and top-to-bottom).
In the embodiment of the invention, the excavator can work more efficiently by setting the corresponding excavating strategy, and the excavating operation efficiency is further improved.
In one embodiment, unloading mined material onto a mine card in accordance with a target loading point includes: and unloading the excavated material to the ore card according to the target loading point based on a preset unloading strategy.
It will be appreciated that the preset unloading strategy is a preset strategy for unloading the pile by the excavator, for example, unloading according to the position of the target loading point from left to right and/or from top to bottom, etc.
Specifically, the processor may unload the materials in a left-to-right and top-to-bottom order of the location of the target loading point based on a pre-set unloading strategy (e.g., left-to-right and top-to-bottom).
In the embodiment of the invention, the excavator can work more efficiently by setting the corresponding unloading strategy, and the efficiency of the excavating operation is further improved.
In one embodiment, acquiring point cloud data of a target area includes: and acquiring point cloud data of the target area by at least one mode of a laser radar, a global navigation satellite system and an information network system.
It can be understood that the processor can acquire the point cloud data of the target area obtained by any one mode or combination of multiple modes of the laser radar, the global navigation satellite system and the information network system, the positioning data acquired by the global navigation satellite system and/or the information network system can be used for increasing precision and positioning functions, and when the point cloud data of the target area are obtained by combination of multiple modes, the point cloud data obtained by multiple modes can be fused through an algorithm, so that the mining working position and the unloading working position of the excavator can be accurately positioned, the mapping efficiency is improved, and the working effectiveness of the excavator is ensured.
In a specific embodiment, a control method for an excavator is provided, which specifically includes the following steps:
(1) The laser radar, the GNSS (Global Navigation SATELLITE SYSTEM, global satellite navigation system) and/or the INS (Information Network System ) are deployed in front of the excavator, and meanwhile, the vehicle-mounted computer is deployed in the cab, so that the vehicle-mounted computer can be used as a hardware condition for realizing the positioning of a working area of the excavator, and particularly can be shown in figure 3.
(2) The excavator establishes a three-dimensional point cloud map (namely a three-dimensional environment map) by fusing GNSS/INS data through laser SLAM, and identifies a working object ore card and a soil pile in the three-dimensional point cloud map by adopting a point cloud data processing algorithm, and the method specifically comprises the following steps of:
① . Front point cloud data acquisition;
② . Establishing a three-dimensional point cloud map according to the point cloud data;
③ . Performing ground removal (namely ground segmentation), clustering, segmenting classes with smaller number of point clouds to obtain two types of point cloud sets of a material pile and a mine card, and converting a three-dimensional point cloud map into a two-dimensional grid map;
④ . Two objects are identified and position marked in the map by means of a classification strategy.
Specifically, as shown in fig. 4, the specific steps of identifying the object to be identified may be: converting the three-dimensional point cloud map into a two-dimensional grid map, taking the maximum value of the point cloud height in each grid as the height value corresponding to the grid, calculating the variance and average value of the grid height value of the grid corresponding to each cluster (i.e. the point cloud set of each category), and identifying the cluster as an ore card if the average value of the height values is larger than a height threshold value and the variance of the height value is smaller than a variance threshold value, otherwise, identifying the cluster as a soil pile (i.e. a material pile).
(3) And after the object to be identified is identified, a three-dimensional geometric model of the object to be identified is established for gridding, and a target mining point and a target loading point are extracted from the three-dimensional geometric model.
Specifically, the extraction procedure for the target mining point may be as follows: firstly, fitting three-dimensional point cloud information of a soil pile environment by means of soil pile point cloud data acquired by a laser radar and a three-dimensional reconstruction method; then, a soil pile point cloud part is segmented from the three-dimensional point cloud by utilizing a segmentation algorithm, and a three-dimensional geometric model (for example, the shape is a cube) is established; finally, meshing a plurality of small cubes on a three-dimensional geometric model of the soil pile, recording the center point coordinates of the small cubes in real time, and outputting the cube center coordinates with the highest height as initial excavation target points; the mining target points are then ordered in left-to-right, top-to-bottom order as the following mining target points.
The extraction procedure for the target loading point may be as follows: firstly, acquiring mine stuck point cloud data by using a laser radar, and then fitting out three-dimensional point cloud information of a mine truck body by using a three-dimensional reconstruction method; then, separating out the mine stuck point cloud part by utilizing a segmentation algorithm, and establishing a three-dimensional geometric model (for example, the shape is a cube); finally, intercepting a plurality of small cubes of point cloud grid-connected grids in a loading area on a three-dimensional geometric model of the mine card, recording the center point coordinates of the small cubes in real time, and outputting the center coordinates of the cubes with the highest height and the leftmost side as initial loading target points; the individual loading points are then ordered in a left-to-right, top-to-bottom order as subsequent loading target points.
In the embodiment of the invention, the surrounding environment sensing of the excavator is carried out by carrying a laser radar and/or a global navigation satellite system and/or an information network system on the excavator, wherein radar acquisition point cloud data can be used for environment map building and work object identification, the data of the global navigation satellite system and/or the information network system can be used for increasing precision and positioning functions, the working position and unloading working position of the excavator can be accurately positioned by processing fusion data through an algorithm, the map building efficiency is improved, and the working effectiveness of the excavator is ensured; perceived meshing of maps and work pairs may support implementation of automatic path planning and automatic work planning.
An embodiment of the present invention provides a processor configured to perform the control method for an excavator according to the above-described embodiment.
The embodiment of the invention provides a control device for an excavator, which comprises the following components: the point cloud data acquisition equipment is used for acquiring point cloud data of the target area; and a processor configured to: acquiring point cloud data of a target area; processing the point cloud data to identify an object to be identified in the target area, wherein the object to be identified comprises a stockpile and a mine card; determining a target excavation point of a material pile and a target loading point of a mine truck; excavating the material pile according to the target excavation point; unloading the excavated material onto the mine truck according to the target loading point.
According to the technical scheme, the point cloud data of the target area are acquired through the point cloud data acquisition equipment, the processor processes the point cloud data to identify the object to be identified of the target area, so that the target excavation point of the material pile and the target loading point of the ore card are determined, the material pile is excavated according to the target excavation point, and the excavated material is unloaded onto the ore card according to the target loading point. According to the method, on-site personnel are not required to monitor and control the excavator to perform the actions of digging and unloading, the identification of the object to be identified in the target area is realized by acquiring the point cloud data of the target area, so that the positions of a material pile and a mine card are accurately determined, automatic path planning and automatic operation planning can be realized, the accurate positioning of the digging working position and the unloading working position of the excavator can be realized by determining the target digging point of the material pile and the target loading point of the mine card, the working efficiency of the excavator can be improved according to the target digging point and the target loading point, the automation degree of the excavator operation is improved, the working effectiveness of the excavator is ensured, and the production efficiency is further improved.
In one embodiment, the point cloud data acquisition device comprises at least one of: lidar, global navigation satellite systems, and information network systems.
The embodiment of the invention provides an excavator, which comprises the following components: the control device for an excavator according to the above embodiment.
Embodiments of the present invention provide a machine-readable storage medium having stored thereon instructions that, when executed by a processor, cause the processor to perform a control method for an excavator according to the above-described embodiments.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (14)
1. A control method for an excavator, comprising:
Acquiring point cloud data of a target area;
Processing the point cloud data to identify an object to be identified of the target area, wherein the object to be identified comprises a stockpile and a mine card;
determining a target excavation point of the stockpile and a target loading point of the mine truck;
Excavating the material pile according to the target excavation point;
unloading the excavated material onto the mine truck according to the target loading point;
The processing the point cloud data to identify the object to be identified of the target area includes:
establishing a three-dimensional point cloud map according to the point cloud data;
Performing clustering segmentation processing on the three-dimensional point cloud map to obtain a processed three-dimensional point cloud map, wherein the processed three-dimensional point cloud map comprises a target point cloud set of a class corresponding to the object to be identified;
converting the processed three-dimensional point cloud map into a two-dimensional grid map;
Based on a preset identification strategy, identifying a grid where the target point cloud set is located in the two-dimensional grid map so as to identify the object to be identified;
The identifying, based on a preset identification policy, the grid where the target point cloud set is located in the two-dimensional grid map to identify the object to be identified includes:
determining a grid height value corresponding to each grid in the two-dimensional grid map, wherein the grid height value is the maximum height value in point cloud data in the grids;
Determining relevant information of grid height values of grids where the target point cloud sets are located in the two-dimensional grid map, wherein the relevant information comprises discrete degree parameters and average values;
Identifying that the grid where the target point cloud set is located corresponds to the mine card under the condition that the discrete degree parameter is smaller than a preset discrete degree threshold value and the average value is larger than a preset average value;
And identifying that the grid where the target point cloud set is located corresponds to the stockpile under the condition that the discrete degree parameter is greater than or equal to the preset discrete degree threshold value and/or the average value is smaller than or equal to the preset average value.
2. The control method according to claim 1, wherein the performing cluster segmentation processing on the three-dimensional point cloud map to obtain a processed three-dimensional point cloud map includes:
performing ground segmentation processing on the three-dimensional point cloud map to obtain a first point cloud map, wherein the first point cloud map does not comprise the ground;
Clustering the first point cloud map to obtain a second point cloud map, wherein the second point cloud map comprises a plurality of class point cloud sets;
and carrying out segmentation processing on the second point cloud map to obtain the processed three-dimensional point cloud map.
3. The control method according to claim 2, wherein the dividing the second point cloud map to obtain the processed three-dimensional point cloud map includes:
Determining the number of point clouds in each point cloud set in the second point cloud map;
And removing the point cloud sets with the number of the point clouds being smaller than a preset number threshold value to obtain the processed three-dimensional point cloud map.
4. The control method of claim 1, wherein the determining the target excavation point of the stockpile and the target loading point of the mine truck comprises:
Establishing a material pile three-dimensional geometric model corresponding to the material pile according to the point cloud data corresponding to the material pile;
establishing a three-dimensional geometric model of the mine card corresponding to the material pile according to the point cloud data corresponding to the mine card;
Respectively carrying out gridding treatment on the material pile three-dimensional geometric model and the mine card three-dimensional geometric model to obtain a plurality of first regular geometric bodies corresponding to the material pile three-dimensional geometric model and a plurality of second regular geometric bodies corresponding to the mine card three-dimensional geometric model;
Determining a center point of the first regular geometric body as a target excavation point of the stockpile;
And determining a target loading point of the ore card according to the center point of the second regular geometric body.
5. The control method of claim 4, wherein the determining the target loading point of the mine card from the center point of the second regular geometric body comprises:
Determining a loading area of the mine card;
and determining the central point of the second irregular geometric body corresponding to the loading area as a target loading point of the ore card.
6. The control method according to claim 4, characterized in that the control method further comprises:
Determining the center point with the largest height among the center points of the first regular geometric bodies as the initial target digging point of the material pile.
7. The control method according to claim 5, characterized in that the control method further comprises:
And determining the center point farthest from the excavator among the center points of the second irregular geometric bodies corresponding to the loading areas as an initial target loading point of the ore card.
8. The control method according to claim 1, wherein the excavating the pile according to the target excavation point includes:
digging the material pile according to the target digging point based on a preset digging strategy;
Unloading the excavated material onto the mine truck according to the target loading point, including:
And unloading the excavated materials to the ore card according to the target loading point based on a preset unloading strategy.
9. The control method according to claim 1, wherein the acquiring the point cloud data of the target area includes:
And acquiring point cloud data of the target area through at least one mode of a laser radar, a global navigation satellite system and an information network system.
10. A processor configured to perform the control method for an excavator according to any one of claims 1 to 9.
11. A control device for an excavator, comprising:
The point cloud data acquisition equipment is used for acquiring point cloud data of the target area; and
The processor of claim 10.
12. The control apparatus of claim 11, wherein the point cloud data acquisition device comprises at least one of:
lidar, global navigation satellite systems, and information network systems.
13. An excavator, comprising:
The control device for an excavator according to claim 11.
14. A machine readable storage medium having instructions stored thereon, which when executed by a processor cause the processor to perform the control method for an excavator according to any one of claims 1 to 9.
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