CN111552756A - Mining area high-precision map manufacturing method capable of achieving automatic dynamic updating of pit shoveling and point unloading - Google Patents
Mining area high-precision map manufacturing method capable of achieving automatic dynamic updating of pit shoveling and point unloading Download PDFInfo
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Abstract
The invention discloses a mining area high-precision map manufacturing method capable of automatically and dynamically updating shovel nests and unloading points. And after the boundary acquisition equipment arranged on the electric shovel equipment or the bulldozer equipment finishes periodic scanning, the boundary data of the shovel nest and the unloading point are reported to the platform in real time, the latest reported boundary data and the original data of the platform are fused by the platform in real time, the dynamic updating of the shovel nest and the unloading point is finished, and the real-time performance and the accuracy of the high-precision map are ensured. When the terrain continuously changes, multiple acquisition is not needed, and time and resources are saved.
Description
Technical Field
The invention belongs to the technical field of high-precision map manufacturing, and particularly relates to a mining area high-precision map manufacturing method with automatic dynamic updating of pit shoveling and point unloading.
Background
The high-precision map is an electronic map with higher precision and more data dimensions. The accuracy is higher, and the data dimension is more embodied by the fact that the data dimension comprises surrounding static information which is related to traffic besides road information. The high-precision map is one of key capabilities of realizing automatic driving, and the high-precision map can be an effective supplement to an automatic driving existing sensor and provides more reliable sensing capability for a vehicle.
Compared with the traditional navigation map, the high-precision map serving automatic driving has higher requirements in all aspects, and can provide support for a decision layer by matching with sensors and algorithms. The preferred scheme in the prior art is that multiple sensors such as a laser radar sensor, an IMU sensor and a vision sensor are fused to acquire data so as to draw a high-precision map. The system can achieve accurate acquisition and detailed details when being applied to mining scenes, has better support for unmanned mine cars, and provides convenient conditions for high-precision map drawing due to the appearance of a plurality of current mine production information systems and thematic application systems. However, the process of building the mining area map is difficult to be fully applied to a large amount of collected data, data collection needs to be carried out again and time-consuming manual map drawing needs to be carried out along with certain progress of mining and collecting work, so that great waste of resources such as time, manpower and material resources is caused, and therefore updating iteration of the high-precision map becomes a problem to be solved urgently in the field.
The drawing of high-precision maps requires a large amount of geometric information and spatial information, and the drawing of maps is relatively time-consuming. For a mine application scenario. Along with the continuous progress of mining work, the topography of mining area changes constantly, and the map also needs constantly to be updated, need draw accurate mining area map in real time in order to can the accurate travel route of detecting each unmanned mine car. However, manual collection, editing and release are performed again during each drawing, and a large amount of time, manpower and material resources are wasted, so that a high-precision map drawing method which can ensure accurate data collection and detailed data and can automatically update to reduce the cost of manpower and material resources in a mining area application scene is very important in promoting the development process of unmanned automatic driving of a mining area.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides a mining area high-precision map manufacturing method with automatic dynamic update of pit shoveling and unloading points, and the specific technical scheme of the invention is as follows:
a mining area high-precision map manufacturing method capable of automatically and dynamically updating shoveling and unloading points is characterized in that a bulldozer device, an electric shovel device and a collection vehicle device are all provided with a multi-line laser radar, a millimeter wave radar, a camera, a GPS device and an IMU device, and the method comprises the following steps:
s1: calibrating parameters of a multi-line laser radar arranged on bulldozer equipment, electric shovel equipment and collecting vehicle equipment, and jointly calibrating the multi-line laser radar, a millimeter wave radar and a camera;
s2: preprocessing the acquired multiline laser radar data, removing miscellaneous points and ground information points through filtering, clustering point clouds after filtering to obtain information of a travelable area boundary, and performing edge detection on the point clouds by using a Sobel operator and a Laplacian operator to obtain periodic scanning boundaries of a nest shoveling and point unloading travelable area;
s3: automatically storing the acquisition result as a file, uploading the file to a terrain data acquisition server platform, adopting a storage road feasible region boundary point (MULTIPOLYGON format) as a feasible region file storage format,
xi＝x1,x2,x3，
yi＝{E,lot,lat}，
wherein x isiBoundary information of a driving area where shoveling nests and unloading points can drive is obtained after filtering, clustering and edge detection are carried out on the multiline laser radar; x is the number of1,x2,x3Point clouds obtained for incremental updatingCoordinates of the data in three directions of XYZ; y isiThe method comprises the steps that information of a corresponding area obtained by GPS equipment, including elevation information E, longitude information lot and latitude information lat, is uploaded to a topographic data acquisition server platform;
s4: when the multi-line laser radar performs periodic scanning, full scanning cannot be performed, due to the sight distance and the scanning angle, the really scanned boundary can lack a place which is partially shielded by gullies or soil piles, and is collectively called a 'blind area', but the blind area is also the boundary part of the driving-possible area for shoveling nests and unloading points, so that the 'incremental updating' is performed on the whole driving-possible area, and the actual physical boundary is prevented from being updated mistakenly;
the terrain data acquisition server platform uses a ReLU function to perform fusion calculation on an incrementally updated physical boundary and an original physical boundary, a boundary change part is screened out through vector subtraction, the change part is reserved if 3 frames or more of boundary change continuously exists, automatic dynamic updating of pit shoveling unloading points is achieved, namely range information of a drivable area is updated in real time, meanwhile, actual physical boundary points and retaining wall obstacle information points are separated through a laser point cloud clustering algorithm, the drivable area of the unmanned mine car is recalculated, and accurate automatic dynamic updating of information is achieved.
The invention has the beneficial effects that:
1. after periodic scanning is completed, the acquisition equipment transmits the boundary data of the feasible region to a terrain data acquisition server platform in real time, and performs real-time fusion on the latest reported data and the original data to complete real-time updating of the area where the shovel nest and the unloading point can drive.
2. The multi-sensor such as the multi-line laser radar and the like placed on each mine car are used for continuously acquiring and updating data, so that more accurate and real-time updated environment information is acquired, the high-precision map building method is superior to the traditional information acquisition mode when a driving area is used for building a map, and the consumption of manpower and material resources is reduced.
3. When the multi-line laser radar performs periodic scanning, full scanning cannot be achieved, due to the sight distance and the scanning angle, the really scanned boundary can lack a place which is partially shielded by gullies or soil piles, and is generally called a blind area, but the blind area is also the boundary part of the driving-possible area for shoveling nests and unloading points, so that the whole driving-possible area is subjected to incremental updating, and the actual physical boundary is prevented from being updated mistakenly.
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In order to illustrate embodiments of the present invention or technical solutions in the prior art more clearly, the drawings which are needed in the embodiments will be briefly described below, so that the features and advantages of the present invention can be understood more clearly by referring to the drawings, which are schematic and should not be construed as limiting the present invention in any way, and for a person skilled in the art, other drawings can be obtained on the basis of these drawings without any inventive effort. Wherein:
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a schematic view of the scoop and dump point of the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments of the present invention and features of the embodiments may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
The invention relates to a method for integrating full scanning and incremental scanning information of environmental information acquisition aiming at special environments of mining areas based on acquisition and integration of data information. Firstly, installing a multi-line laser radar device on a collection vehicle, an electric shovel device and a bulldozer device, sensing and collecting environmental information of a current transportation road area which is relatively fixed or dynamically changed, and then generating a drivable area in real time by a road collection device; and after the collection is finished, automatically storing the collection result into a file mode, and uploading the file mode to a topographic data collection server platform to serve as a material for subsequent high-precision map processing. The file storage format of the feasible region is considered to adopt a mode of storing the boundary points (multi-level, which require longitude and latitude and elevation information) of the feasible region of the road, so as to be convenient for the post data processing. The process flow of the present invention is shown in FIG. 1.
After the geometric information and the property of the mine road are obtained, the environment information of the road driving area is collected after the integration of a millimeter wave radar, a multi-line laser radar and the like, and the high-definition map of the mine is drawn. The map collection needs the cooperation of sensors such as a laser radar, a camera, a GPS (global positioning system), an IMU (inertial measurement unit) and the like, and after the boundary collection equipment arranged on the electric shovel equipment or the bulldozer equipment finishes periodic scanning, the boundary data of the shovel pit and the unloading point are reported to a terrain data collection server platform in real time, the newly reported boundary data and the original data are fused in real time, the dynamic update of the shovel pit and the unloading point is finished, and the real-time performance and the accuracy of a high-precision map are guaranteed. When the terrain continuously changes, multiple acquisition is not needed, and time and resources are saved.
Considering that the terrain of a mining area can change continuously along with the progress of excavation work and the characteristics of dust in the mining area and working day and night, a camera is easily influenced by environmental illumination, so that the imaging performance and the visual field of the camera can be deteriorated. Therefore, in the invention, sensors such as a multi-line laser radar and a millimeter wave radar are selected as data acquisition tools, point cloud data are transmitted to a terrain data acquisition server platform in real time, distortion correction, point cloud filtering and the like are carried out, a high-precision mine map is further constructed, and a dynamically changed transportation road area can be timely fed back to map making and automatically updated.
The mining area high-precision map manufacturing method is characterized in that a bulldozer device, an electric shovel device and a collection vehicle device are respectively provided with a multi-line laser radar, a millimeter wave radar, a camera, a GPS device and an IMU device, and the method comprises the following steps:
s1: calibrating parameters of a multi-line laser radar arranged on bulldozer equipment, electric shovel equipment and collecting vehicle equipment, and jointly calibrating the multi-line laser radar, a millimeter wave radar and a camera;
s2: preprocessing the acquired multiline laser radar data, removing miscellaneous points and ground information points through filtering, clustering point clouds after filtering to obtain information of a travelable area boundary, and performing edge detection on the point clouds by using a Sobel operator and a Laplacian operator to obtain periodic scanning boundaries of a nest shoveling and point unloading travelable area;
s3: automatically storing the acquisition result as a file, uploading the file to a terrain data acquisition server platform, adopting a storage road feasible region boundary point (MULTIPOLYGON format) as a feasible region file storage format,
xi＝x1,x2,x3，
yi＝{E,lot,lat}，
wherein x isiBoundary information of a driving area where shoveling nests and unloading points can drive is obtained after filtering, clustering and edge detection are carried out on the multiline laser radar; x is the number of1,x2,x3Coordinates of point cloud data acquired during incremental updating in the XYZ three directions; y isiThe method comprises the steps that information of a corresponding area obtained by GPS equipment, including elevation information E, longitude information lot and latitude information lat, is uploaded to a topographic data acquisition server platform;
s4: when the multi-line laser radar performs periodic scanning, full scanning cannot be performed, due to the sight distance and the scanning angle, the really scanned boundary can lack a place which is partially shielded by gullies or soil piles, and is collectively called a 'blind area', but the blind area is also the boundary part of the driving-possible area for shoveling nests and unloading points, so that the 'incremental updating' is performed on the whole driving-possible area, and the actual physical boundary is prevented from being updated mistakenly;
the terrain data acquisition server platform uses a ReLU function to perform fusion calculation on an incrementally updated physical boundary and an original physical boundary, a boundary change part is screened out through vector subtraction, the change part is reserved if 3 frames or more of boundary change continuously exists, automatic dynamic updating of pit shoveling unloading points is achieved, namely range information of a drivable area is updated in real time, meanwhile, actual physical boundary points and retaining wall obstacle information points are separated through a laser point cloud clustering algorithm, the drivable area of the unmanned mine car is recalculated, and accurate automatic dynamic updating of information is achieved.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (1)
1. A mining area high-precision map manufacturing method capable of automatically and dynamically updating shoveling and unloading points is characterized in that a bulldozer device, an electric shovel device and a collection vehicle device are all provided with a multi-line laser radar, a millimeter wave radar, a camera, a GPS device and an IMU device, and the method comprises the following steps:
s1: calibrating parameters of a multi-line laser radar arranged on bulldozer equipment, electric shovel equipment and collecting vehicle equipment, and jointly calibrating the multi-line laser radar, a millimeter wave radar and a camera;
s2: preprocessing the acquired multiline laser radar data, removing miscellaneous points and ground information points through filtering, clustering point clouds after filtering to obtain information of a travelable area boundary, and performing edge detection on the point clouds by using a Sobel operator and a Laplacian operator to obtain periodic scanning boundaries of a nest shoveling and point unloading travelable area;
s3: automatically storing the acquisition result as a file, uploading the file to a terrain data acquisition server platform, adopting a storage road feasible region boundary point (MULTIPOLYGON format) as a feasible region file storage format,
xi＝x1,x2,x3，
yi＝{E,lot,lat}，
wherein x isiBoundary information x of driving areas where shoveling nests and unloading points can drive is obtained after clustering and edge detection after multi-line laser radar filtering1,x2,x3Coordinates of point cloud data acquired during incremental updating in the XYZ three directions; y isiIs a GPS deviceThe obtained information of the corresponding area, including elevation information E, longitude information lot and latitude information lat, is uploaded to a topographic data acquisition server platform;
s4: when the multi-line laser radar performs periodic scanning, full scanning cannot be performed, due to the sight distance and the scanning angle, the really scanned boundary can lack a place which is partially shielded by gullies or soil piles, and is collectively called a 'blind area', but the blind area is also the boundary part of the driving-possible area for shoveling nests and unloading points, so that the 'incremental updating' is performed on the whole driving-possible area, and the actual physical boundary is prevented from being updated mistakenly;
the terrain data acquisition server platform uses a ReLU function to perform fusion calculation on an incrementally updated physical boundary and an original physical boundary, a boundary change part is screened out through vector subtraction, the change part is reserved if 3 frames or more of boundary change continuously exists, automatic dynamic updating of pit shoveling unloading points is achieved, namely range information of a drivable area is updated in real time, meanwhile, actual physical boundary points and retaining wall obstacle information points are separated through a laser point cloud clustering algorithm, the drivable area of the unmanned mine car is recalculated, and accurate automatic dynamic updating of information is achieved.
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| CN112556654A (en) * | 2020-12-17 | 2021-03-26 | 武汉中海庭数据技术有限公司 | High-precision map data acquisition device and method |
| CN113282090A (en) * | 2021-05-31 | 2021-08-20 | 三一专用汽车有限责任公司 | Unmanned control method and device for engineering vehicle, engineering vehicle and electronic equipment |
| CN113219933A (en) * | 2021-07-08 | 2021-08-06 | 北京踏歌智行科技有限公司 | Strip mine unmanned truck dispatching system and method based on digital twin prediction |
| CN114322983A (en) * | 2021-12-17 | 2022-04-12 | 清华大学苏州汽车研究院（吴江） | Light-weight map manufacturing method and device for automatic driving of mine |
| CN114322983B (en) * | 2021-12-17 | 2024-04-26 | 清华大学苏州汽车研究院（吴江） | Lightweight map making method and device for mine automatic driving |
| CN114706094A (en) * | 2022-06-07 | 2022-07-05 | 青岛慧拓智能机器有限公司 | Unloading available state detection method and device for unloading point position and computer equipment |
| CN114706094B (en) * | 2022-06-07 | 2022-08-23 | 青岛慧拓智能机器有限公司 | Unloading available state detection method and device for unloading point location and computer equipment |
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