CN111880196A - Unmanned mine car anti-interference method, system and computer equipment - Google Patents
Unmanned mine car anti-interference method, system and computer equipment Download PDFInfo
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Abstract
The invention discloses an anti-interference method, a system and computer equipment for an unmanned mine car, which are used for eliminating the interference of dense dust or heavy raindrops in a mining area on a laser radar on the unmanned mine car and identifying the obstacle information of a real obstacle, and comprise the following steps: detecting obstacles in the running process of the unmanned mine car through a laser radar, generating point cloud data, and processing the point cloud data to generate laser radar obstacle information; detecting obstacles in the running process of the unmanned mine car through millimeter waves and generating millimeter wave obstacle information; calculating correlation coefficients of the laser radar obstacle information and the millimeter wave obstacle information, and keeping the laser radar obstacle information corresponding to the correlation coefficients larger than or equal to a threshold value as obstacle information of a real obstacle; and removing the laser radar obstacle information corresponding to the correlation coefficient smaller than the threshold value as the information generated by dust or raindrops.
Description
Technical Field
The invention relates to the field of unmanned mine cars, in particular to an anti-interference method and system for an unmanned mine car and computer equipment.
Background
At present, a laser radar generates a large amount of false detections in a mining area scene, and the false detections are mainly caused by dense dust and heavy raindrops; dense dust can form the dust point cloud on laser radar, and big raindrop can form the rainwater point cloud on laser radar.
The millimeter wave radar does not falsely detect large raindrops and dense dust as obstacles.
In the prior art, the laser radar mainly removes dust point cloud and rainwater point cloud by the following two methods: the dust point cloud and the rainwater point cloud are filtered by a 1550nm laser, or removed by a multi-echo technique.
When the dust point cloud and the rainwater point cloud are filtered by 1550nm laser, only a part of small-concentration dust and small raindrops can be filtered out due to short wavelength, but the point cloud formed by large raindrops and dense dust cannot be removed.
When the dust point cloud and the rainwater point cloud are removed through the multi-echo technology, because dense dust and large raindrops can shield obstacles behind the laser, multi-echo cannot be formed, and only the last echo in the multi-echo can be selected to filter out a part of point cloud formed by small particle dust and small raindrops.
Disclosure of Invention
In order to solve the technical problems, the invention provides an anti-interference method and system for an unmanned mine car and computer equipment.
In order to solve the technical problems, the invention adopts the following technical scheme:
an unmanned mine car anti-interference method is used for eliminating interference of dense dust or heavy raindrops in a mine area to a laser radar on the unmanned mine car and identifying obstacle information of a real obstacle, and comprises the following steps:
the method comprises the following steps: detecting obstacles in the running process of the unmanned mine car through a laser radar, generating point cloud data, and processing the point cloud data to generate laser radar obstacle information;
step two: detecting obstacles in the running process of the unmanned mine car through millimeter waves and generating millimeter wave obstacle information;
step three: calculating correlation coefficients of the laser radar obstacle information and the millimeter wave obstacle information, and keeping the laser radar obstacle information corresponding to the correlation coefficients larger than or equal to a threshold value as obstacle information of a real obstacle; and removing the laser radar obstacle information corresponding to the correlation coefficient smaller than the threshold value as the information generated by dust or raindrops.
Specifically, in the first step, when point cloud data are processed to generate laser radar obstacle information, the point cloud data are preprocessed to generate preprocessed point cloud data; and after the ground features in the preprocessed point cloud data are removed, extracting obstacle information of the preprocessed point cloud data to generate laser radar obstacle information.
Specifically, when the ground features in the pre-processed point cloud data are removed, the ground point cloud in the pre-processed point cloud data is removed by adopting an elevation difference filtering method, an elevation straight-through filtering method or a morphological filtering method.
Specifically, when the point cloud data is preprocessed to generate preprocessed point cloud data, the preprocessed point cloud data is generated after time synchronization, space synchronization, noise filtering and point cloud sampling are sequentially performed on the point cloud data.
Specifically, the lidar obstacle information and the millimeter wave obstacle information both include an obstacle position and obstacle features, the obstacle position refers to an obstacle center point position, and the obstacle features include a length, a width, a height, a yaw angle, and a category of the obstacle.
Specifically, when obstacle information is carried out on the preprocessed point cloud data, point cloud segmentation is carried out on the preprocessed point cloud data by using a point cloud segmentation algorithm to generate segmented point cloud data, and laser radar obstacle information is extracted from the segmented point cloud data.
Specifically, the point cloud segmentation algorithm comprises an Euclidean clustering algorithm, a conditional Euclidean clustering algorithm, a region growing method and a deep learning algorithm.
Specifically, when the correlation coefficient is calculated in the third step, the category of the obstacle can be directly extracted from the millimeter wave obstacle information, the point cloud data is calculated through pattern recognition or a deep learning algorithm, the category of the obstacle corresponding to the point cloud data is recognized, and if the categories of the two obstacles are different, the correlation coefficient is 0; if the two obstacles have the same category, calculating a weighted average value of a distance correlation coefficient, a 2D overlapping correlation coefficient, a 3D overlapping correlation coefficient and a shape correlation coefficient between the two obstacles as a correlation coefficient between the two obstacles; the threshold is greater than 0.
An unmanned mine car anti-interference system, comprising:
a point cloud data processing module: detecting obstacles in the running process of the unmanned mine car through a laser radar, generating point cloud data, and processing the point cloud data to generate laser radar obstacle information;
the millimeter wave processing module: detecting obstacles in the running process of the unmanned mine car through millimeter waves and generating millimeter wave obstacle information;
an interference removal module: calculating correlation coefficients of the laser radar obstacle information and the millimeter wave obstacle information, and keeping the laser radar obstacle information corresponding to the correlation coefficients larger than or equal to a threshold value as obstacle information of a real obstacle; and removing the laser radar obstacle information corresponding to the correlation coefficient smaller than the threshold value as the information generated by dust or raindrops.
A computer device comprising a memory and a processor, the memory having stored therein a computer program which, when executed by the processor, performs the steps of the tamper-resistant method.
Compared with the prior art, the invention has the beneficial technical effects that:
the laser radar has high detection precision on the characteristics of the barrier, but is easily influenced by dense dust and large raindrops; the millimeter wave radar has good penetrability on heavy raindrops and heavy dust, but the detection precision is lower than that of a laser radar, the detection characteristics of the two radars are fused, the correlation of detection results of the two radars is calculated by the anti-interference method, whether obstacles in the detection results are real obstacles is judged, and the influence of the heavy dust and the heavy raindrops on the detection results is reduced.
Drawings
Fig. 1 is a schematic flow chart of an anti-interference method according to the present invention.
Detailed Description
A preferred embodiment of the present invention will be described in detail below with reference to the accompanying drawings.
The unmanned mine car detects the obstacles through various sensors including a laser radar so as to avoid the obstacles in time in the unmanned process.
The working environment of a mining area is severe, and the laser radar can generate a large amount of false detections in a mining area scene, wherein the false detections are mainly caused by dense dust and heavy raindrops; dense dust can form the dust point cloud on laser radar, and big raindrop can form the rainwater point cloud on laser radar.
Dense dust refers to dust with a dust concentration of more than 10 mg/cubic meter; the heavy raindrops refer to raindrops corresponding to raindrops with rainfall greater than 15 mm in 12 hours or rainfall greater than 25 mm in 24 hours, and include raindrops corresponding to heavy rain, heavy rainstorm and extra-heavy rainstorm.
In the prior art, the laser radar mainly removes dust point cloud and rainwater point cloud by the following two methods: the dust point cloud and the rainwater point cloud are filtered by a 1550nm laser, or removed by a multi-echo technique.
When the dust point cloud and the rainwater point cloud are filtered by the 1550nm laser, only a part of small-concentration dust and small raindrops, such as raindrops of medium rain and small rain, can be filtered due to the short wavelength, but the point cloud formed by large raindrops and dense dust cannot be removed.
When the dust point cloud and the rainwater point cloud are removed through the multi-echo technology, because dense dust and large raindrops can shield obstacles behind the laser, multi-echo cannot be formed, and only the last echo in the multi-echo can be selected to filter out a part of point cloud formed by small particle dust and small raindrops.
As shown in FIG. 1, the invention provides an anti-interference method for an unmanned mine car, which is used for eliminating interference of dense dust or heavy raindrops in a mining area to a laser radar on the unmanned mine car, and comprises the following steps:
s1: the method comprises the steps of detecting obstacles in the running process of the unmanned mine car through the laser radar, generating point cloud data, and processing the point cloud data to generate laser radar obstacle information.
Specifically, in the first step, when point cloud data are processed to generate laser radar obstacle information, the point cloud data are preprocessed to generate preprocessed point cloud data; and after the ground features in the preprocessed point cloud data are removed, extracting obstacle information of the preprocessed point cloud data to generate laser radar obstacle information.
Specifically, when the ground features in the pre-processed point cloud data are removed, the ground point cloud in the pre-processed point cloud data is removed by adopting an elevation difference filtering method, an elevation straight-through filtering method or a morphological filtering method.
Specifically, when the point cloud data is preprocessed to generate preprocessed point cloud data, the preprocessed point cloud data is generated after time synchronization, space synchronization, noise filtering and point cloud sampling are sequentially performed on the point cloud data.
The time synchronization is to match the point cloud data of a plurality of laser radars in time and find the point cloud data which are closest to different laser radars in time; the spatial synchronization is to rotate and translate point cloud data of a plurality of laser radars to a vehicle body coordinate system to achieve pixel level fusion of different laser radar point clouds; the noise filtering is to filter some isolated point noise in the point cloud data by using a filtering method; the point cloud sampling is to remove redundant data in the point cloud data under the condition of meeting the precision, so that the calculation amount of a subsequent algorithm is reduced.
Specifically, the lidar obstacle information and the millimeter wave obstacle information both include an obstacle position and obstacle features, the obstacle position refers to an obstacle center point position, and the obstacle features include a length, a width, a height, a yaw angle, and a category of the obstacle.
The obstacle information can support the unmanned mine car to accurately and quickly bypass the obstacle.
Specifically, when obstacle information is carried out on the preprocessed point cloud data, point cloud segmentation is carried out on the preprocessed point cloud data by using a point cloud segmentation algorithm to generate segmented point cloud data, and laser radar obstacle information is extracted from the segmented point cloud data.
Specifically, the point cloud segmentation algorithm comprises an Euclidean clustering algorithm, a conditional Euclidean clustering algorithm, a region growing method and a deep learning algorithm.
S2: and detecting obstacles in the running process of the unmanned mine car through millimeter waves and generating millimeter wave obstacle information.
Millimeter waves are transmitted through the millimeter wave radar, misdetection of the millimeter waves can be filtered from parameters such as reflection areas and existence probabilities, and millimeter wave obstacle information is generated.
S3: calculating correlation coefficients of the laser radar obstacle information and the millimeter wave obstacle information, and keeping the laser radar obstacle information corresponding to the correlation coefficients larger than or equal to a threshold value as obstacle information of a real obstacle; and removing the laser radar obstacle information corresponding to the correlation coefficient smaller than the threshold value as the information generated by dust or raindrops.
Judging whether the obstacles detected by the two types of sensors are the same object or not according to the correlation coefficient of the millimeter wave obstacle information and the laser radar obstacle information, wherein the larger the correlation coefficient is, the more likely the two obstacles are the same object, and when the correlation coefficient is larger than a certain threshold value, the two obstacles are regarded as the same object; if an object can be detected by the millimeter wave radar and also can be detected by the laser radar, the object is not a large raindrop or dense dust but a real obstacle, and then the interference elimination work of the large raindrop or dense dust is completed.
Because the precision of the millimeter wave radar for detecting the characteristics of the obstacles is lower than that of the laser radar, a more accurate detection result of the laser radar is selected as the obstacle information of the real obstacle.
Specifically, when the correlation coefficient is calculated in the third step, the category of the obstacle can be directly extracted from the millimeter wave obstacle information, the point cloud data is calculated through pattern recognition or a deep learning algorithm, the category of the obstacle corresponding to the point cloud data is recognized, and if the categories of the two obstacles are different, the correlation coefficient is 0; if the two obstacles have the same category, calculating a weighted average value of a distance correlation coefficient, a 2D overlapping correlation coefficient, a 3D overlapping correlation coefficient and a shape correlation coefficient between the two obstacles as a correlation coefficient between the two obstacles; the threshold is greater than 0; the deep learning algorithm comprises a PointNet + + algorithm and a Complex-YOLO algorithm.
And calculating the Euclidean distance between the obstacle measured by the laser radar and the center point of the obstacle measured by the millimeter wave radar, and taking the reciprocal of the Euclidean distance as a distance correlation coefficient between the two obstacles.
And calculating the intersection ratio of the obstacle measured by the laser radar and the obstacle measured by the millimeter wave radar on the 2D rectangular frame on the aerial view as a 2D overlapping correlation coefficient.
And calculating the intersection ratio of the obstacle measured by the laser radar and the 3D rectangular frame of the obstacle measured by the millimeter wave radar as a 3D overlapping correlation coefficient.
And calculating the shape error of the obstacle measured by the laser radar and the obstacle measured by the millimeter wave radar, namely the sum of the absolute values of the difference between the length, the width and the height of the two obstacles, and taking the reciprocal of the sum of the absolute values as a shape correlation coefficient.
And when the weighted average value of the correlation coefficients is calculated, setting the weight according to experience or historical data.
The physical quantity required for calculating the correlation coefficient can be obtained from the laser radar obstacle information and the millimeter wave obstacle information.
If the laser radar and the millimeter wave radar detect a plurality of obstacles simultaneously, the obstacles need to be subjected to cross comparison for a plurality of times; for example, m obstacles are detected by a laser radar, n obstacles are detected by a millimeter wave radar, and m × n comparison detections are needed for the obstacles.
Selecting m1 obstacles from m obstacles, selecting n1 obstacles from n obstacles, judging whether the categories of the two obstacles are the same, and if the categories are not the same, the correlation coefficient is 0; if the categories are the same, calculating a correlation coefficient; selecting n2 obstacles from the n obstacles, and further calculating the correlation coefficient of the m1 obstacles and the n2 obstacles; sequentially calculating correlation coefficients of the m1 obstacle and the n obstacles, selecting the maximum value of the correlation coefficients, and if the maximum value is greater than a threshold value, indicating that an object which is the same as the m1 exists in the n obstacles, indicating that the object is not a large raindrop or dense dust but a real obstacle; otherwise, the object is dense dust or heavy raindrops; and analogizing in turn to judge other m-1 obstacles.
An unmanned mine car anti-interference system, comprising:
a point cloud data processing module: detecting obstacles in the running process of the unmanned mine car through a laser radar, generating point cloud data, and processing the point cloud data to generate laser radar obstacle information;
the millimeter wave processing module: detecting obstacles in the running process of the unmanned mine car through millimeter waves and generating millimeter wave obstacle information;
an interference removal module: calculating correlation coefficients of the laser radar obstacle information and the millimeter wave obstacle information, and keeping the laser radar obstacle information corresponding to the correlation coefficients larger than or equal to a threshold value as obstacle information of a real obstacle; and removing the laser radar obstacle information corresponding to the correlation coefficient smaller than the threshold value as the information generated by dust or raindrops.
A computer device comprising a memory and a processor, the memory having stored therein a computer program which, when executed by the processor, performs the steps of the tamper-resistant method.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein, and any reference signs in the claims are not intended to be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (10)
1. An unmanned mine car anti-interference method is used for eliminating interference of dense dust or heavy raindrops in a mine area to a laser radar on the unmanned mine car and identifying obstacle information of a real obstacle, and comprises the following steps:
the method comprises the following steps: detecting obstacles in the running process of the unmanned mine car through a laser radar, generating point cloud data, and processing the point cloud data to generate laser radar obstacle information;
step two: detecting obstacles in the running process of the unmanned mine car through millimeter waves and generating millimeter wave obstacle information;
step three: calculating correlation coefficients of the laser radar obstacle information and the millimeter wave obstacle information, and keeping the laser radar obstacle information corresponding to the correlation coefficients larger than or equal to a threshold value as obstacle information of a real obstacle; and removing the laser radar obstacle information corresponding to the correlation coefficient smaller than the threshold value as the information generated by dust or raindrops.
2. The unmanned mining vehicle anti-jamming method according to claim 1, characterized in that: firstly, preprocessing point cloud data to generate preprocessed point cloud data when the point cloud data are processed to generate laser radar obstacle information; and after the ground features in the preprocessed point cloud data are removed, extracting obstacle information of the preprocessed point cloud data to generate laser radar obstacle information.
3. The unmanned mining vehicle anti-jamming method according to claim 2, characterized in that: and when the ground features in the preprocessed point cloud data are cut off, removing the ground point cloud in the preprocessed point cloud data by adopting an elevation difference filtering method, an elevation straight-through filtering method or a morphological filtering method.
4. The unmanned mining vehicle anti-jamming method according to claim 2, characterized in that: when the point cloud data is preprocessed to generate preprocessed point cloud data, the preprocessed point cloud data is generated after time synchronization, space synchronization, noise filtering and point cloud sampling are sequentially performed on the point cloud data.
5. The unmanned mining vehicle anti-jamming method according to claim 2, characterized in that: the laser radar obstacle information and the millimeter wave obstacle information both comprise obstacle positions and obstacle features, the obstacle positions refer to the positions of center points of the obstacles, and the obstacle features comprise the lengths, widths, heights, yaw angles and categories of the obstacles.
6. The unmanned mining vehicle anti-jamming method according to claim 2, characterized in that: when obstacle information extraction is carried out on the preprocessed point cloud data, point cloud segmentation is carried out on the preprocessed point cloud data by using a point cloud segmentation algorithm to generate segmented point cloud data, and laser radar obstacle information is extracted from the segmented point cloud data.
7. The unmanned mining vehicle anti-jamming method according to claim 6, characterized in that: the point cloud segmentation algorithm comprises an Euclidean clustering algorithm, a conditional Euclidean clustering algorithm, a region growing method and a deep learning algorithm.
8. The unmanned mining vehicle anti-jamming method according to claim 1, characterized in that: when the correlation coefficient is calculated in the third step, the category of the obstacle can be directly extracted from the millimeter wave obstacle information, the point cloud data is calculated through pattern recognition or a deep learning algorithm, the category of the obstacle corresponding to the point cloud data is recognized, and if the categories of the two obstacles are different, the correlation coefficient is 0; if the two obstacles have the same category, calculating a weighted average value of a distance correlation coefficient, a 2D overlapping correlation coefficient, a 3D overlapping correlation coefficient and a shape correlation coefficient between the two obstacles as a correlation coefficient between the two obstacles; the threshold is greater than 0.
9. An unmanned mine car anti-interference system, its characterized in that includes:
a point cloud data processing module: detecting obstacles in the running process of the unmanned mine car through a laser radar, generating point cloud data, and processing the point cloud data to generate laser radar obstacle information;
the millimeter wave processing module: detecting obstacles in the running process of the unmanned mine car through millimeter waves and generating millimeter wave obstacle information;
an interference removal module: calculating correlation coefficients of the laser radar obstacle information and the millimeter wave obstacle information, and keeping the laser radar obstacle information corresponding to the correlation coefficients larger than or equal to a threshold value as obstacle information of a real obstacle; and removing the laser radar obstacle information corresponding to the correlation coefficient smaller than the threshold value as the information generated by dust or raindrops.
10. A computer arrangement comprising a memory and a processor, the memory having stored thereon a computer program which, when executed by the processor, causes the processor to carry out the steps of the tamper resistant method according to any one of claims 1-8.
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