CN116245996A - Point cloud rendering method and system for unstructured road in mining area - Google Patents
Point cloud rendering method and system for unstructured road in mining area Download PDFInfo
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Abstract
The invention belongs to the technical field of unmanned, and particularly discloses a point cloud rendering method and a system for an unstructured road in a mining area, wherein the method comprises the following steps: acquiring track points of a vehicle and point clouds of mining area roads scanned by a laser radar on the vehicle; selecting the track points at equal intervals to obtain track center points; at the same time, calculating a relative elevation value of the point cloud relative to a track center point, and writing the relative elevation value into an attribute of the point cloud; extracting a mining area map according to the point cloud; rendering the point cloud according to the attribute, classifying and rendering the mining area map according to different types, and superposing the rendered mining area map and the point cloud; has the following advantages: by superposing the rendered mining area map and the point cloud together for display, the extracted data precision can be judged according to the superposition result, and meanwhile, accurate point cloud data accordance is provided for subsequent high-precision map data editing.
Description
Technical Field
The invention relates to the technical field of unmanned operation, in particular to a point cloud rendering method and a point cloud rendering system for an unstructured road in a mining area.
Background
The unmanned is an intelligent unmanned control technology that a vehicle senses the road environment through a vehicle-mounted sensing system, and a computer system is used for planning a driving route and achieving a destination, and the intelligent unmanned control technology senses the surrounding environment of the vehicle through the vehicle-mounted sensor and controls the steering and the speed of the vehicle according to the road, the vehicle position and the obstacle information obtained through sensing, so that the vehicle can safely and reliably run on the road; the mining area unmanned driving system integrates a plurality of technologies such as automatic control, an architecture, artificial intelligence, visual computing and the like, is used as one of automatic driving, and is applied to a specific site and a specific vehicle of the mining area, wherein the mining area refers to an open pit mine, such as iron ore, coal mine and the like, and the applied vehicle refers to a large-scale mine truck and a wide vehicle;
the laser radar scanning system can rapidly and accurately acquire high-precision three-dimensional space information. The extraction of high-precision three-dimensional road information from the radar point cloud plays an important role in the fields of 3D navigation, unmanned driving and the like; the vehicle-mounted laser radar point cloud has the defects of uneven density distribution, large data volume, incomplete data, complex scene and the like, so that the integrity and the correctness of road extraction are affected; the point cloud rendering display can be used for performing classified rendering according to different attribute values of the point cloud, such as using elevation attribute rendering of the point cloud to check elevation distribution conditions of the point cloud; RGB rendering can be used for checking geographic information displayed by the point cloud under the real color;
in the existing point cloud classification rendering mode, elevation rendering is used for relatively flat roads or working areas, and road surfaces and road boundaries cannot be separated. The RGB rendering mode is used, under the monotone color of the surface mine, the difficulty exists in distinguishing the road surface from the boundary, for example, under the condition of light shadow coverage, the road boundary is rendered in the RGB rendering mode, and the boundary and the road surface cannot be distinguished;
therefore, a point cloud rendering method and a point cloud rendering system for unstructured roads in mining areas are provided to solve the problems.
Disclosure of Invention
The invention aims to provide a point cloud rendering method and a point cloud rendering system for unstructured roads in mining areas, which are used for solving or improving the problem that road boundaries are identified by classifying and rendering point clouds of surface mines.
In view of the above, a first aspect of the present invention is to provide a point cloud rendering method for unstructured roads in a mining area.
A second aspect of the invention is to provide a point cloud rendering system for unstructured roads in a mine.
The first aspect of the invention provides a point cloud rendering method of an unstructured road in a mining area, which comprises the following steps: s1, acquiring track points of a vehicle and point clouds of mining area roads scanned by a laser radar on the vehicle; s2, selecting the track points at equal intervals to obtain track center points, calculating the slope of the current road according to the positions of the track center points, judging whether the current road is a slope road according to the slope, and if so, re-selecting the track center points at reduced intervals; s3, under the same moment, calculating a relative elevation value of the point cloud relative to the track center point in the S3, and writing the relative elevation value into the attribute of the point cloud; s4, acquiring the position on a known map according to the longitude and latitude of the track center point, and determining the element type of the map where the point cloud is located; and S5, rendering the point cloud according to the attribute, performing color rendering on the map according to the type of the element, performing coordinate projection transformation on the rendered map, and superposing the coordinate projection transformation with the point cloud.
According to the point cloud rendering method for the unstructured road in the mining area, the rendered mining area map and the point cloud are overlapped together for display, so that the extracted data precision can be judged according to the overlapped result, and whether the current high-precision map needs to be updated or not is judged according to the difference between the overlapped map and the real-time point cloud; meanwhile, as the point cloud is rendered, the difference between the map and the point cloud can be clearly seen after the point cloud is displayed on the map, accurate point cloud data is provided for subsequent high-precision map data editing, and manual accurate editing and updating can be performed;
the relative elevation value of the point cloud relative to the track center point is used as the attribute of the point cloud data, so that the actual geographic information represented by the surface mine point cloud data can be clearly identified;
the road point cloud boundary can be judged through the calculated relative height, an accuracy check basis can be provided for boundary data extracted according to the point cloud, and the clear boundary can be seen due to clear color difference between the ground point and the retaining wall point due to the relative height;
and finally, displaying the rendered result, and providing accurate data updating conformity for the mine high-precision map data, so that more accurate high-precision map data can be manufactured, and safe and accurate data guarantee is provided for unmanned mining.
In addition, the technical scheme provided by the embodiment of the invention can also have the following additional technical characteristics:
in any of the above solutions, the step of outputting 20 track points per second by the vehicle, and the step of S2 specifically includes: s21, screening the track points output every second at intervals of 2m, and calculating the slope between adjacent track points; s22, judging whether the current road where the track point is located is inclined or not through the slope, if so, performing S23, clearing the screening result, and if not, performing S24; s23, screening the track points at the current moment by adopting an interval of 1 m; s24, outputting the track point screened currently as a track center point.
In the technical scheme, the equidistant segmentation screening is carried out on the corresponding track points on the road at the same moment according to the number of the track points actually output by the laser radar on the vehicle per second, so that the horizontal distances of the selected adjacent track points are ensured to be similar, and the overlarge error of individual data is avoided;
the road in the inclined state is required to be rescreened by judging whether the slope current road is inclined or not, and a smaller screening gap is selected to reduce the height Cheng Chazhi between adjacent track points so as to be in a reasonable color value range in subsequent rendering and ensure the normal output of rendering results;
before the relative elevation value of the point cloud data is written, whether the road is inclined or not is judged through the slope between adjacent track points, so that screening modes with different intervals are adopted, different roads are adapted, and the stability of the result of detecting the road in the mining area is ensured.
In any of the above solutions, the following rule is adopted as to whether the inclination in S22: and comparing the slope with 5 degrees, if the slope is larger than the slope, judging that the road where the track point is located is inclined, and if the slope is smaller than the slope, judging that the road where the track point is located is not inclined.
In the technical scheme, the slope is judged by setting the value of 5 degrees, the road corresponding to the slope of more than 5 degrees is the inclined road, and the judgment standard of whether the final road is inclined can be more suitable for the actual condition of the mining area road by setting the judgment value of the slope in a targeted manner.
In any of the above solutions, the data of the track point and the point cloud respectively include: longitude, latitude and elevation.
In the technical scheme, longitude and latitude and elevation are respectively provided in the data of the recorded track points and the point cloud, so that the road condition of the mining area can be better understood, and the final rendering precision is ensured.
In any of the above technical solutions, the step of S3 specifically includes: s31, calculating a relative elevation value of the elevation of the point cloud relative to the track center point according to the elevation of the track center point at the same time; s32, stretching conversion is carried out on the relative elevation value, and the relative elevation value is written into the attribute userdata of the point cloud.
In the technical scheme, the screened elevation of the track center point and the elevation of the point cloud are calculated at the same moment to obtain the relative elevation value, so that calculation errors caused by different mapping places due to time errors are avoided, the actual correspondence of the track center point and the point cloud is ensured, dislocation errors are reduced, the relative elevation value is written into the attribute of the point cloud and corresponds to a subsequent rendering color value, the actual elevation can be represented in the subsequent rendering color, and the difficulty of distinguishing the road surface from the boundary of RGB rendering under the monotone color of an open-air mine is avoided.
Specifically, calculating a relative elevation value of the elevation of the point cloud relative to the track center point, and adopting the following calculation formula:
Δh＝h point cloud -h Track center point ；
Wherein Δh is a relative elevation value, h Point cloud Is the elevation, h of the point cloud Track center point Is the elevation of the center point of the track.
In any of the above solutions, the stretching conversion of the relative elevation value is calculated by the following formula: x is x 1 ＝(x 2 +0.5) x 100 wherein x 1 For the relative elevation value, x of the point cloud after stretching conversion 2 Is the relative elevation value of the point cloud before the stretch conversion.
In the technical scheme, the formula is adopted to correspondingly calculate the relative elevation value and the color value of the subsequent rendering, so that stable correspondence between different values is ensured, the picture error after rendering is reduced, meanwhile, the specific range of the color value interval of the subsequent rendering can be screened in advance for the point clouds of different relative elevation value ranges, and the color value range required by the rendering is not exceeded during the subsequent rendering.
In any of the above solutions, before said stretching converting the relative elevation value, S32 further includes: screening the point cloud according to the relative elevation value; wherein the screening range is [ -0.5,2].
In the technical scheme, since the color value has a specific range requirement in the subsequent rendering, a certain range of screening is required to be performed on the point cloud written with the relative elevation value attribute before the rendering, so that the point cloud can be completely processed by the subsequent rendering step, and the specific screening range is [ -0.5,2].
In any of the above solutions, the mining area map includes: road surface and data of corresponding boundary, working area surface and data of corresponding boundary; the working area is a field for working in mining area mining.
In the technical scheme, in the data extraction of the mining area map, the data of the road surface of the mining area and the corresponding boundary thereof and the data of the working area surface and the corresponding boundary thereof are specifically required to be included, and the data calculation is performed as much as possible, and meanwhile, the data calling of the mining card under unmanned driving is ensured at the necessary part of the final road analysis.
In any of the above solutions, the rendering in S5 adopts a WebGL rendering engine, and the point cloud is a las point cloud.
A second aspect of the present invention is to provide a point cloud rendering system of an unstructured road of a mining area, comprising: the data acquisition module is used for acquiring vehicle-mounted laser radar point cloud data and longitude and latitude position data; the key point selection module is used for selecting a track center point; the sampling interval is 2m, if the current road is in an inclined state, the sampling interval is reduced, and dense sampling is performed; the characteristic calculation module is used for calculating the relative elevation value of the point cloud and storing the relative elevation value as the attribute characteristic of the point cloud; wherein, the relative elevation value is stretched to a certain extent to distinguish the relative elevation; the map projection module is used for unifying the real-time frame point cloud and the known map into the same coordinate system and determining a working section where the current point cloud is located according to longitude and latitude data; the rendering module is used for performing point cloud rendering, classifying and rendering the current point cloud according to the point cloud space attribute acquired by the map projection module and the point cloud relative elevation attribute acquired by the feature calculation module, and displaying the current point cloud on the high-precision map so as to judge whether the map needs to be updated or not; wherein the system is for implementing the method of any one of the first aspects.
The invention provides a point cloud rendering system of an unstructured road in a mining area, which implements the steps of the point cloud rendering method of the unstructured road in the mining area in any technical scheme. Therefore, the point cloud rendering system for the unstructured road in the mining area provided by the technical scheme has all the beneficial effects of the point cloud rendering method for the unstructured road in the mining area in any one of the technical schemes, and is not repeated herein.
Compared with the prior art, the invention has the following beneficial effects:
the height difference in the geographic information can be amplified by calculating the relative elevation and stretching the relative elevation, and the actual geographic information represented by the surface mine point cloud data can be clearly identified; meanwhile, the road area and the retaining wall area have obvious color distinction, so that the boundary position can be clearly judged, and an accuracy check basis can be provided for boundary data extracted according to the point cloud;
and by corresponding to the high-precision map, the real-time point cloud and the high-precision map difference are compared, so that accurate accordance of the update data provided by the mine high-precision map data can be provided.
Additional aspects and advantages of embodiments according to the invention will be apparent from the description which follows, or may be learned by practice of embodiments according to the invention.
Drawings
The foregoing and/or additional aspects and advantages of the invention will become apparent and may be better understood from the following description of embodiments taken in conjunction with the accompanying drawings in which:
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of the road-level acquisition frequency of the present invention;
FIG. 3 is a graph showing ramp acquisition frequency according to the present invention;
FIG. 4 is a flow chart of the relative elevation difference calculation of the present invention;
FIG. 5 is a rendering effect diagram of a conventional method;
FIG. 6 is a graph of a relative elevation rendering effect of the present invention;
fig. 7 is a system logic block diagram of the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
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 described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
Referring to fig. 1-7, a method and a system for rendering point clouds of unstructured roads in mining areas according to some embodiments of the present invention are described below.
The embodiment of the first aspect of the invention provides a point cloud rendering method of an unstructured road in a mining area. In some embodiments of the present invention, as shown in fig. 1 to 6, there is provided a point cloud rendering method of an unstructured road of a mining area, the point cloud rendering method of the unstructured road of the mining area including:
firstly, generating point cloud data according to vehicle-mounted laser radar scanning:
after acquiring the collected mining area road las point cloud data, acquiring track data of a collection vehicle, wherein the data comprises longitude and latitude and actual elevation;
the vehicle-mounted laser radar is used for collecting point cloud data of the mine.
The method comprises the steps that 20 track points exist in a track of a laser radar of a point cloud acquisition vehicle, wherein the track points are normally left and right navigation output in one second, firstly, the track points are selected according to a spacing distance of 2m, a slope is calculated according to the selected front track point and the selected rear track point, and if the angle is larger than 5 degrees, the slope is defined; less than 5 ° is defined as a level road; if it is a slope, the trajectory points are re-selected with a separation distance of 1 m.
Secondly, carrying out elevation difference processing on the scanned point cloud data relative to the track point elevation at the same moment, and writing the elevation difference processing into the attribute of the point cloud data:
and calculating the relative elevation value of the mining area road point cloud data acquired at the current moment relative to the elevation of the track point at the elevation of the track center point at the same moment. And cutting out the point cloud data with the relative elevation value range within the range of minus 0.5, 2.
For the range of point cloud data, the relative elevation value is converted into a range of color values [0, 255] required for WebGL rendering according to a formula.
x 1 ＝(x 2 +0.5)*100
Wherein x is 1 For the relative elevation value, x of the point cloud after stretching conversion 2 Is the relative elevation value of the point cloud before the stretch conversion.
And writing the converted relative elevation value data into userdata attributes of the point cloud.
Thirdly, extracting high-precision map elements according to the point cloud:
and extracting basic high-precision map data according to the point cloud data as a reference. The types of elements of the extracted high-precision map data mainly include: loading area, unloading area, parking area, temporary parking area.
And fourthly, loading the rendering point cloud data by the WebGL rendering engine, and converting the extracted high-precision map data into a coordinate system identical to the point cloud through coordinate projection transformation. Then, classifying and rendering according to different map element types, and superposing the classified and rendered map element types on the point cloud;
the WebGL rendering engine performs classified rendering on the processed point cloud data, loads the extracted high-precision map data, and displays the two data in a superposition mode.
And finally, loading the point cloud data by using a WebGL rendering engine, and rendering according to userdata attributes of the point cloud. The extracted relevant high-precision map data is then loaded. And stacking and displaying together. And judging the extracted data precision according to the superposition result, and providing accurate point cloud data for subsequent high-precision map data editing according to the extracted data precision, so that manual accurate editing and updating can be performed.
According to the point cloud rendering method for the unstructured road in the mining area, point cloud data of the road in the mining area, the working area and the like are rendered according to the elevation difference value of the point cloud relative to the central line of a lane of an acquisition vehicle, so that the road surface and the road boundary are distinguished. For verifying the accuracy of road boundary data extracted according to the vehicle-mounted lidar. The method is particularly used for verifying the accuracy of point cloud road extraction, providing a basis for updating boundary data and providing accurate and efficient base map data for unmanned mining areas.
An embodiment of the second aspect of the invention provides a point cloud rendering system for unstructured roads in a mining area. In some embodiments of the present invention, as shown in fig. 7, there is provided a point cloud rendering system of a mining area unstructured road, the point cloud rendering system of a mining area unstructured road comprising:
the data acquisition module is used for acquiring vehicle-mounted laser radar point cloud data and longitude and latitude position data.
The key point selection module is used for selecting key track points; the sampling interval is 2m, if the current road is in an inclined state, the sampling interval is reduced, and dense sampling is performed;
the characteristic calculation module is used for calculating the relative elevation value of the point cloud and storing the relative elevation value as the attribute characteristic of the point cloud; wherein, the relative elevation value is stretched to a certain extent to distinguish the relative elevation;
the map projection module is used for unifying the real-time frame point cloud and the high-precision map into the same coordinate system and determining a working section where the current point cloud is located according to longitude and latitude data;
the rendering module is used for performing point cloud rendering, classifying and rendering the current point cloud according to the point cloud space attribute acquired by the map projection module and the point cloud relative elevation attribute acquired by the feature calculation module, and displaying the current point cloud on the high-precision map so as to judge whether the map needs to be updated or not;
wherein the system is for implementing the method of any one of the first aspects.
The invention provides a point cloud rendering system of an unstructured road in a mining area, which implements the steps of the point cloud rendering method of the unstructured road in the mining area in any embodiment. Therefore, the point cloud rendering system for the unstructured road in the mining area provided by the embodiment has all the beneficial effects of the point cloud rendering method for the unstructured road in the mining area in any one of the above embodiments, and is not described herein again.
In the description of the present invention, it should be understood that the terms "longitudinal," "transverse," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate or are based on the orientation or positional relationship shown in the drawings, merely to facilitate description of the present invention, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the present invention.
The above embodiments are only illustrative of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention, and various modifications and improvements made by those skilled in the art to the technical solutions of the present invention should fall within the protection scope defined by the claims of the present invention without departing from the design spirit of the present invention.
Claims (10)
1. The point cloud rendering method for the unstructured road in the mining area is characterized by comprising the following steps of:
s1, acquiring track points of a vehicle and point clouds of mining area roads scanned by a laser radar on the vehicle;
s2, selecting the track points at equal intervals to obtain track center points; calculating the slope of the current road according to the position of the track center point, judging whether the current road is a slope road or not according to the slope, and if so, re-selecting the track center point at reduced intervals;
s3, under the same moment, calculating a relative elevation value of the point cloud relative to the track center point in the S3, and writing the relative elevation value into the attribute of the point cloud;
s4, acquiring the position on a known map according to the longitude and latitude of the track center point, and determining the element type of the map where the point cloud is located;
and S5, rendering the point cloud according to the attribute, performing color rendering on the map according to the type of the element, performing coordinate projection transformation on the rendered map, and superposing the coordinate projection transformation with the point cloud.
2. The method for rendering the point cloud of the unstructured road of the mining area according to claim 1, wherein the vehicle outputs 20 track points per second, and the step of S2 specifically comprises:
s21, screening the track points output every second at intervals of 2m, and calculating the slope between adjacent track points after screening;
s22, judging whether the current road where the track point is located is inclined or not through the slope, if so, performing S23, clearing the screening result, and if not, performing S24;
s23, screening the track points at the current moment by adopting an interval of 1 m;
s24, outputting the track point screened currently as a track center point.
3. The method for rendering the point cloud on the unstructured road in the mining area according to claim 2, wherein whether the road is inclined in S22 adopts the following rule:
and comparing the slope with 5 degrees, if the slope is larger than the slope, judging that the road where the track point is located is inclined, and if the slope is smaller than the slope, judging that the road where the track point is located is not inclined.
4. The method for rendering the point cloud of the unstructured road in the mining area according to claim 1, wherein the data of the track points and the point cloud respectively comprise: longitude, latitude and elevation.
5. The method for rendering the point cloud on the unstructured road in the mining area according to claim 4, wherein the step S3 specifically includes:
s31, calculating a relative elevation value of the elevation of the point cloud relative to the track center point according to the elevation of the track center point at the same time;
s32, stretching and converting the relative elevation value and writing the relative elevation value into the attribute of the point cloud.
6. The method for rendering the point cloud on the unstructured road of the mining area according to claim 5, wherein the stretching conversion of the relative elevation value is calculated by adopting the following formula:
x 1 ＝(x 2 +0.5)*100
wherein x is 1 For the relative elevation value, x of the point cloud after stretching conversion 2 Is the relative elevation value of the point cloud before the stretch conversion.
7. The method for point cloud rendering of unstructured roads of claim 5, wherein said S32 further comprises, prior to said stretch converting said relative elevation values:
screening the point cloud according to the relative elevation value;
wherein the screening range is [ -0.5,2].
8. A method of point cloud rendering of unstructured roads of a mine site according to claim 1, wherein said mine site map comprises: road surface and data of corresponding boundary, working area surface and data of corresponding boundary;
the working area is a field for working in mining area mining.
9. The method for rendering the point cloud of the unstructured road of the mining area according to claim 1, wherein the rendering in the step S5 adopts a WebGL rendering engine, and the point cloud is a las point cloud.
10. A point cloud rendering system for unstructured roads in a mining area, comprising:
the data acquisition module is used for acquiring vehicle-mounted laser radar point cloud data and longitude and latitude position data;
the key point selection module is used for selecting a track center point; the sampling interval is 2m, if the current road is in an inclined state, the sampling interval is reduced, and dense sampling is performed;
the characteristic calculation module is used for calculating the relative elevation value of the point cloud and storing the relative elevation value as the attribute characteristic of the point cloud; wherein, the relative elevation value is stretched to a certain extent to distinguish the relative elevation;
the map projection module is used for unifying the real-time frame point cloud and the known map into the same coordinate system and determining a working section where the current point cloud is located according to longitude and latitude data;
the rendering module is used for performing point cloud rendering, classifying and rendering the current point cloud according to the point cloud space attribute acquired by the map projection module and the point cloud relative elevation attribute acquired by the feature calculation module, and displaying the current point cloud on the high-precision map so as to judge whether the map needs to be updated or not;
wherein the system is for implementing the method of any one of claims 1-9.
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| CN117111089B (en) * | 2023-10-24 | 2024-02-02 | 青岛慧拓智能机器有限公司 | Method, system, equipment and storage medium for identifying availability state of ore card unloading point |
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