CN116953658A - Data processing method, device and storage medium - Google Patents
Data processing method, device and storage medium Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
- G01S7/4802—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
The application discloses a data processing method, a device and a storage medium, wherein the method comprises the following steps: collecting a frame of laser point cloud data by using a laser radar, and correcting the laser point cloud data to obtain corrected laser point cloud data; performing straight line detection on laser point clouds included in the corrected laser point cloud data to obtain straight lines corresponding to each laser point cloud included in the corrected laser point cloud data; setting a corresponding weight according to a distance from a corresponding straight line for each laser point cloud included in the corrected laser point cloud data; and determining positioning information of the laser radar and actual laser point cloud data based on the corrected laser point cloud data and the weight of each laser point cloud included in the corrected laser point cloud data by using a point-to-line nearest iterative algorithm. By the technical scheme, the accuracy of data processing is improved.
Description
Technical Field
The present application relates to the field of robotics, and in particular, to a data processing method, apparatus, and storage medium.
Background
With the rapid development of the robot industry in recent years, the requirements of various industries on mobile robots are becoming wider and wider. Therefore, mobile robots need to be deployed into a variety of industrial applications, while mobile robots that can accommodate a variety of changing environments, are low cost and that can function properly therein would be favored. The two-dimensional laser radar is used as environment sensing equipment with low cost and is widely applied to the mobile robot.
However, as the frequency of the two-dimensional laser radar for collecting the point cloud data is lower, the mobile robot has shaking conditions in the moving process, or as the current scene has long distance or small volume objects, the collected point cloud data contains points with offset errors, the accuracy is lower, and the point cloud data with lower accuracy can influence the drawing precision and the positioning accuracy of the mobile robot.
Disclosure of Invention
In order to solve the above technical problems, embodiments of the present invention are expected to provide a data processing method, apparatus, and storage medium, which improve accuracy of data processing.
The technical scheme of the invention is realized as follows:
the invention provides a data processing method, which comprises the following steps:
collecting a frame of laser point cloud data by using a laser radar, and correcting the laser point cloud data to obtain corrected laser point cloud data;
performing straight line detection on laser point clouds included in the corrected laser point cloud data to obtain straight lines corresponding to each laser point cloud included in the corrected laser point cloud data;
setting a corresponding weight according to the distance from a corresponding straight line for each laser point cloud included in the corrected laser point cloud data;
And determining positioning information of the laser radar and actual laser point cloud data based on the corrected laser point cloud data and the weight of each laser point cloud included in the corrected laser point cloud data by using a point-to-line nearest iterative algorithm.
In the above method, the correcting the laser point cloud data to obtain corrected laser point cloud data includes:
acquiring pose change information of the laser radar in the process of acquiring the laser point cloud data by the laser radar;
and correcting the laser point cloud data based on the pose change information to obtain corrected laser point cloud data.
In the above method, in the process of acquiring the laser point cloud data by the lidar, pose change information of the lidar includes:
acquiring inertial measurement unit data and/or laser radar odometer data;
and determining the pose change information according to the inertial measurement unit data and/or the laser radar odometer data by using a nonlinear Kalman filtering method.
In the above method, the pose change information includes: in the process of acquiring the laser point cloud data by the laser radar, acquiring initial pose information when a first laser point cloud is acquired and final pose information when a last laser point cloud is acquired, correcting the laser point cloud data based on the pose change information to obtain corrected laser point cloud data, wherein the method comprises the following steps:
Dividing the acquisition time length of the laser radar for acquiring the laser point cloud data into a plurality of sub-time lengths;
for each duration in the plurality of sub-durations, estimating corresponding pose information by adopting a linear interpolation mode according to the initial pose information and the final pose information;
acquiring laser point clouds acquired in each time length in the plurality of sub-time lengths from the laser point cloud data to obtain a plurality of groups of laser point clouds corresponding to the plurality of sub-time lengths one by one;
determining pose information corresponding to the same sub-time length as pose information corresponding to each laser point cloud in the group aiming at each laser point cloud in the plurality of groups of laser point clouds;
and aiming at each laser point cloud included in the laser point cloud data, taking the coordinate system of the first laser point cloud as a reference coordinate system, and determining the corresponding laser point cloud data under the reference coordinate system by utilizing the corresponding pose information to obtain the corrected laser point cloud data.
In the above method, the detecting the straight line of the laser point cloud included in the corrected laser point cloud data to obtain a straight line corresponding to each laser point cloud included in the corrected laser point cloud data includes:
Selecting a first laser point cloud from laser point clouds included in the corrected laser point cloud data, and determining the first laser point cloud as a 1 st laser point cloud;
sequentially selecting laser point clouds from the corrected laser point cloud data according to a preset direction by taking the 1 st laser point cloud as a reference until the kth laser point cloud is selected, wherein a straight line residual error determined based on the 1 st laser point cloud to the kth laser point cloud is larger than a preset residual error threshold; wherein k is a natural number greater than 1;
determining a straight line determined based on the 1 st to the k-1 st laser point clouds as a straight line corresponding to each of the 1 st to the k-1 st laser point clouds;
and continuously detecting the laser point clouds included in the corrected laser point cloud data according to the preset direction by taking the kth laser point cloud as a reference until a straight line corresponding to each laser point cloud included in the corrected laser point cloud data is obtained.
In the above method, the selecting the laser point cloud sequentially from the corrected laser point cloud data according to the preset direction with the 1 st laser point cloud as a reference until the kth laser point cloud is selected, and determining that the linear residual error based on the 1 st laser point cloud to the kth laser point cloud is greater than a preset residual error threshold value includes:
Sequentially selecting laser point clouds from the corrected laser point cloud data according to the preset direction by taking the 1 st laser point cloud as a reference until the nth laser point cloud is selected, wherein the linear parameter determined based on the 1 st laser point cloud to the n-1 st laser point cloud and the linear parameter determined based on the 1 st laser point cloud to the nth laser point cloud are larger than a preset parameter threshold; wherein n is a natural number greater than 1 and less than or equal to k;
removing the nth laser point cloud from the corrected laser point cloud data, and re-determining the next selected laser point cloud as the nth laser point cloud;
and sequentially selecting laser point clouds from the corrected laser point cloud data according to the preset direction until the kth laser point cloud is selected, wherein the linear residual error determined based on the 1 st laser point cloud to the kth laser point cloud is larger than the preset residual error threshold value.
In the above method, the performing straight line detection on the laser point cloud included in the corrected laser point cloud data with the kth laser point cloud as a reference according to the preset direction includes:
determining a straight line determined based on the mth laser point cloud to the last laser point cloud as a straight line corresponding to each of the mth laser point cloud to the last laser point cloud when the straight line residual determined based on the mth laser point cloud to the last laser point cloud is smaller than or equal to the preset residual threshold and the straight line residual determined based on the mth laser point cloud to the last laser point cloud is larger than the preset residual threshold; where m is a natural number greater than k.
In the above method, the performing straight line detection on the laser point cloud included in the corrected laser point cloud data with the kth laser point cloud as a reference according to the preset direction includes:
determining a straight line determined based on the 1 st to the k th laser point clouds and the m to the last laser point clouds as a straight line corresponding to each of the 1 st to the k th laser point clouds and the m to the last laser point clouds when a straight line residual determined based on the selected m to the last laser point clouds is less than or equal to the preset residual threshold and a straight line residual determined based on the 1 st to the k to the last laser point clouds is less than or equal to the preset residual threshold; where m is a natural number greater than k.
In the above method, the performing straight line detection on the laser point cloud included in the corrected laser point cloud data with the kth laser point cloud as a reference according to the preset direction includes:
Determining a straight line determined based on the mth laser point cloud to the last laser point cloud as a straight line corresponding to each of the mth laser point cloud to the last laser point cloud when the straight line residual determined based on the mth laser point cloud to the last laser point cloud selected is smaller than or equal to the preset residual threshold and the straight line residual determined based on the mth laser point cloud to the kth laser point cloud and the mth laser point cloud is larger than the preset residual threshold; where m is a natural number greater than k.
The present invention provides a data processing apparatus comprising:
the acquisition module is used for acquiring a frame of laser point cloud data by using the laser radar and correcting the laser point cloud data to obtain corrected laser point cloud data;
the detection module is used for carrying out straight line detection on the laser point clouds included in the corrected laser point cloud data to obtain straight lines corresponding to each laser point cloud included in the corrected laser point cloud data;
the setting module is used for setting corresponding weights according to the distance between each laser point cloud included in the corrected laser point cloud data and the corresponding straight line;
The determining module is used for determining positioning information of the laser radar and actual laser point cloud data based on the corrected laser point cloud data and weight of each laser point cloud included in the corrected laser point cloud data by using a point-to-line nearest iterative algorithm.
The present invention provides a data processing apparatus comprising: a processor, a memory, and a communication bus;
the communication bus is used for realizing communication connection between the processor and the memory;
the processor is configured to execute the computer program stored in the memory, so as to implement the data processing method.
The present invention provides a computer readable storage medium storing one or more computer programs executable by one or more processors to implement the above described data processing method.
The invention provides a data processing method, a device and a storage medium, wherein the method comprises the following steps: collecting a frame of laser point cloud data by using a laser radar, and correcting the laser point cloud data to obtain corrected laser point cloud data; performing straight line detection on laser point clouds included in the corrected laser point cloud data to obtain straight lines corresponding to each laser point cloud included in the corrected laser point cloud data; setting a corresponding weight according to a distance from a corresponding straight line for each laser point cloud included in the corrected laser point cloud data; and determining positioning information of the laser radar and actual laser point cloud data based on the corrected laser point cloud data and the weight of each laser point cloud included in the corrected laser point cloud data by using a point-to-line nearest iterative algorithm. According to the technical scheme, before the positioning and scene construction process is carried out on the laser point cloud data collected based on the laser radar, not only is the motion distortion of the laser point cloud data corrected, but also point cloud data with larger offset error of the laser point cloud data are removed in a linear detection mode; in addition, corresponding weights are set for each laser point cloud, so that the accuracy of positioning information and actual laser point cloud data determined based on the laser point cloud data with higher accuracy is higher, and the accuracy of data processing is improved.
Drawings
FIG. 1 is a schematic flow chart of a data processing method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of an exemplary laser point cloud correction according to an embodiment of the present invention;
fig. 3 is a flow chart of an exemplary straight line detection method according to an embodiment of the present invention;
FIG. 4 is a schematic flow chart of an exemplary laser point cloud data processing according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a data processing apparatus according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a data processing apparatus according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the specific embodiments described herein are merely illustrative of the application and not limiting of the application. It should be noted that, for convenience of description, only a portion related to the related application is shown in the drawings.
The invention provides a data processing method, which is realized by a data processing device, and fig. 1 is a flow diagram of the data processing method provided by the embodiment of the invention. As shown in fig. 1, the method mainly comprises the following steps:
S101, acquiring a frame of laser point cloud data by using a laser radar, and correcting the laser point cloud data to obtain corrected laser point cloud data.
In the embodiment of the invention, the data processing device collects a frame of laser point cloud data by using the laser radar, and corrects the laser point cloud data to obtain corrected laser point cloud data.
It should be noted that, in the embodiment of the present invention, the data processing device may be mounted with a laser radar, so that the data processing device may collect a frame of laser point cloud data by using the laser radar. The laser point cloud data of one frame are laser point clouds acquired by 360 degrees of rotation of the laser radar, and the laser point cloud data comprise two-dimensional coordinates of all acquired laser point clouds.
It should be noted that, in the embodiment of the present invention, since the data processing device may move during the process of collecting one frame of laser point cloud data by using the laser radar, the laser point cloud data collected by the laser radar installed in the data processing device may be distorted, that is, the two-dimensional coordinates of each laser point cloud included in the collected laser point cloud data are not in a coordinate system along with the movement of the laser radar, and then the laser point cloud data needs to be corrected, so as to obtain corrected laser point cloud data.
Specifically, in an embodiment of the present invention, a data processing apparatus corrects laser point cloud data to obtain corrected laser point cloud data, including: acquiring pose change information of the laser radar in the process of acquiring laser point cloud data by the laser radar; and correcting the laser point cloud data based on the pose change information to obtain corrected laser point cloud data.
In the embodiment of the invention, the data processing device can acquire pose change information of the laser radar in the process of acquiring the laser point cloud data by the laser radar in the process of correcting the laser point cloud data, and further corrects the laser point cloud data based on the pose change information to obtain corrected laser point cloud data.
Specifically, in the embodiment of the present invention, in the process of acquiring laser point cloud data by a laser radar, a data processing device acquires pose change information of the laser radar, including: acquiring inertial measurement unit data and/or laser radar odometer data; and determining pose change information according to the inertial measurement unit data and/or the laser radar odometer data by using a nonlinear Kalman filtering method.
It should be noted that, in an embodiment of the present invention, the data processing device may acquire the inertial measurement unit data and/or the lidar odometer data, and the data processing device may be provided with the inertial measurement unit and/or a sensor for measuring the lidar odometer data, so as to acquire the inertial measurement unit data and/or the lidar odometer data. The inertial measurement unit data comprise three-axis attitude angles or angular rates of the laser radar and acceleration; the lidar odometry data includes the speed of travel and angular velocity of the lidar.
It should be noted that, in the embodiment of the present invention, after the data processing device acquires the inertial measurement unit data and/or the lidar odometer data, the data processing device may determine pose change information of the lidar according to the inertial measurement unit data and/or the lidar odometer data by using a nonlinear kalman filtering method.
Specifically, in the embodiment of the present invention, the pose change information includes: in the process of collecting laser point cloud data by a laser radar, initial pose information when a first laser point cloud is collected and final pose information when a last laser point cloud is collected, a data processing device corrects the laser point cloud data based on pose change information to obtain corrected laser point cloud data, and the method comprises the following steps: dividing the acquisition time length of laser radar for acquiring laser point cloud data into a plurality of sub-time lengths; for each duration in the plurality of sub-durations, estimating corresponding pose information by adopting a linear interpolation mode according to the initial pose information and the final pose information; acquiring laser point clouds acquired in each time length in a plurality of sub-time lengths from the laser point cloud data to obtain a plurality of groups of laser point clouds corresponding to the plurality of sub-time lengths one by one; determining pose information corresponding to the same sub-time length as pose information corresponding to each laser point cloud in a group aiming at each group of laser point clouds in a plurality of groups of laser point clouds; and for each laser point cloud included in the laser point cloud data, determining the corresponding laser point cloud data under the reference coordinate system by taking the coordinate system of the first laser point cloud as the reference coordinate system and utilizing the corresponding pose information to obtain the corrected laser point cloud data.
It should be noted that in the embodiment of the present invention, the pose change information may be initial pose information when the laser radar collects laser point cloud data and final pose information when the laser radar collects the last laser point cloud, and then after the data processing device knows the collection time length when the laser radar collects one frame of laser point cloud data, the collection time length is divided into a plurality of sub-time lengths, and further, pose information of each sub-time length is determined according to the initial pose information and the final pose information by using a linear interpolation mode, and the pose information is determined as pose information of the laser point cloud collected in the same sub-time length; and finally, aiming at each laser point cloud included in the laser point cloud data, taking the coordinate system of the first laser point cloud as a reference coordinate system, and determining the corresponding laser point cloud data under the reference coordinate system by utilizing the corresponding pose information to obtain the corrected laser point cloud data. That is, the laser point cloud is mapped into the reference coordinate system where the first laser point cloud is located based on pose information corresponding to each laser point cloud data, so as to obtain corrected laser point cloud data.
It should be noted that, in the embodiment of the present invention, the pose change information may also be a plurality of pose information obtained by the data processing device during the process of collecting laser point cloud data, for example, 10-20ms is required for collecting one frame of laser point cloud data, and the inertial measurement unit data and/or the laser radar odometer data may be obtained once every 5ms, so that during the process of collecting the laser point cloud, the inertial measurement unit data of a plurality of time points may be obtained, and/or the laser radar odometer data, and at this time, the data processing device may determine pose information of the laser radar of a plurality of time points according to the inertial measurement unit data of a plurality of time points, and/or the laser radar odometer data, and further determine pose information of each sub-duration according to the pose information of the laser radar of a plurality of time points by using a linear interpolation method.
Fig. 2 is a schematic flow chart of an exemplary laser point cloud correction according to an embodiment of the present invention. As shown in fig. 2, the exemplary flow of implementing laser point cloud correction by the data processing apparatus is: s201, acquiring inertial measurement unit data and/or laser radar odometer data, and determining pose change information according to the inertial measurement unit data and/or the laser radar odometer data by using a nonlinear Kalman filtering method; s202, dividing the acquisition time length of laser radar acquisition laser point cloud data into a plurality of sub-time lengths, and estimating corresponding pose information according to pose change information for each time length in the plurality of sub-time lengths; s203, acquiring laser point clouds acquired in each time length in a plurality of sub-time lengths from the laser point cloud data, obtaining a plurality of groups of laser point clouds corresponding to the plurality of sub-time lengths one by one, and determining pose information corresponding to the same sub-time length as pose information corresponding to each laser point cloud in the group aiming at each group of laser point clouds in the plurality of groups of laser point clouds; s204, for each laser point cloud included in the laser point cloud data, the coordinate system where the first laser point cloud is located is taken as a reference coordinate system, and corresponding pose information is utilized to determine corresponding laser point cloud data under the reference coordinate system, so that corrected laser point cloud data are obtained.
S102, performing straight line detection on the laser point clouds included in the corrected laser point cloud data to obtain straight lines corresponding to the laser point clouds included in the corrected laser point cloud data.
In the embodiment of the invention, the data processing device detects the straight line of the laser point cloud included in the corrected laser point cloud data to obtain the straight line corresponding to each laser point cloud included in the corrected laser point cloud data.
It should be noted that, in the embodiment of the present invention, in the process that the data processing device collects one frame of laser point cloud data by using the laser radar, due to the limitation of the machine of the data processing device, jitter may exist, or the accuracy of data reflected by an object with a long distance or a small volume is low, so that many external points may exist in the obtained laser point cloud data, and therefore, the data processing device needs to further perform linear detection on the laser point cloud included in the corrected laser point cloud data to reject the external points existing in the laser point cloud data.
Specifically, in an embodiment of the present invention, a data processing apparatus performs straight line detection on laser point clouds included in corrected laser point cloud data to obtain straight lines corresponding to each laser point cloud included in the corrected laser point cloud data, including: selecting a first laser point cloud from laser point clouds included in the corrected laser point cloud data, and determining the first laser point cloud as a 1 st laser point cloud; sequentially selecting laser point clouds from the corrected laser point cloud data according to a preset direction by taking the 1 st laser point cloud as a reference until the kth laser point cloud is selected, wherein a straight line residual error determined based on the 1 st laser point cloud to the kth laser point cloud is larger than a preset residual error threshold; wherein k is a natural number greater than 1; determining a straight line determined based on the 1 st to the (k-1) th laser point clouds as a straight line corresponding to each of the 1 st to the (k-1) th laser point clouds; and continuously detecting the straight line of the laser point clouds included in the corrected laser point cloud data according to the preset direction by taking the kth laser point cloud as a reference until the straight line corresponding to each laser point cloud included in the corrected laser point cloud data is obtained.
It should be noted that, in the embodiment of the present invention, the data processing device may randomly select a first laser point cloud from the corrected laser point cloud data, determine the selected laser point cloud as the 1 st laser point cloud, and sequentially select the laser point clouds from the corrected laser point cloud data according to a preset direction with the 1 st laser point cloud as a reference until the k-th laser point cloud is selected, where the straight line residual determined based on the 1 st laser point cloud to the k-th laser point cloud is greater than a preset residual threshold. The preset direction may be a counterclockwise direction or a clockwise direction, and the specific preset direction may be set according to the actual situation and the application requirement, which is not limited by the present invention.
It should be noted that, in the embodiment of the present invention, when each laser point cloud is selected by the data processing apparatus, a straight line formed by the laser point cloud and all laser point clouds from the laser point cloud to the reference laser point cloud is calculated by using a least square method, and an exemplary straight line is shown in formula (1):
y＝a i x+b i (1)
wherein a is i ，b i For straight line parameters, the method can use least square method, see formula(2)：
Wherein, (x) k ,y k ) Is the coordinates of the reference laser point cloud, (x q ,y q ) For the coordinates of the currently selected laser point cloud, i E [ k, q]。
Further, the data processing device may determine a linear residual error based on the 1 st laser point cloud to the kth laser point cloud, and an exemplary residual error calculation formula is shown in formula (3):
wherein e i Is a straight line residual.
It should be noted that, in the embodiment of the present invention, if the straight line residual determined by the data processing apparatus based on the 1 st laser point cloud to the kth laser point cloud is greater than the preset residual threshold; wherein k is a natural number greater than 1; then, it is stated that the linear residual error between the kth laser point cloud and the previous k-1 laser point clouds is larger, that is, the kth laser point cloud and the previous k-1 laser point clouds are not considered to be in the same line.
Fig. 3 is a flow chart of an exemplary straight line detection method according to an embodiment of the present invention. As shown in fig. 3, the data processing apparatus may buffer the traversed laser point cloud into a point cloud cluster, and an exemplary method for implementing the line detection may be: randomly selecting a laser point cloud from the corrected laser point cloud data, adding the selected laser point cloud into a point cloud cluster, traversing the laser point cloud included in the corrected laser point cloud data according to the anticlockwise direction by taking the laser point cloud as a reference, adding the traversed laser point cloud into the point cloud cluster, calculating a linear equation of the current point cloud cluster and a linear residual error, and continuing traversing the laser point cloud included in the corrected laser point cloud data if the calculated linear residual error is smaller than or equal to a preset residual error threshold; and if the calculated linear residual is larger than a preset residual threshold, re-adding the newly added laser point cloud into a new point cloud cluster.
Specifically, in an embodiment of the present invention, the data processing apparatus sequentially selects laser point clouds from the corrected laser point cloud data according to a preset direction with the 1 st laser point cloud as a reference until the kth laser point cloud is selected, and determines that a straight line residual error based on the 1 st laser point cloud to the kth laser point cloud is greater than a preset residual error threshold value, including: sequentially selecting laser point clouds from the corrected laser point cloud data according to a preset direction by taking the 1 st laser point cloud as a reference until the nth laser point cloud is selected, wherein the straight line parameters determined based on the 1 st laser point cloud to the (n-1) th laser point cloud and the straight line parameters determined based on the 1 st laser point cloud to the (n) th laser point cloud are larger than a preset parameter threshold; wherein n is a natural number greater than 1 and less than or equal to k; removing the nth laser point cloud from the corrected laser point cloud data, and re-determining the next selected laser point cloud as the nth laser point cloud; and sequentially selecting laser point clouds from the corrected laser point cloud data according to a preset direction until the kth laser point cloud is selected, wherein the straight line residual error determined based on the 1 st laser point cloud to the kth laser point cloud is larger than a preset residual error threshold value.
It should be noted that, in the embodiment of the present invention, when each laser point cloud is selected, the data processing apparatus calculates a line formed by the laser point cloud and all laser point clouds from the laser point cloud to its reference laser point cloud by using a least square method, if a line parameter determined based on the 1 st to n-1 st laser point clouds and a line parameter determined based on the 1 st to n-th laser point clouds are greater than a preset parameter threshold, it is indicated that a larger deviation occurs in the newly added n-th laser point cloud, that is, an external point, at this time, the data processing apparatus needs to remove the n-th laser point cloud, determine the next selected laser point cloud as the n-th laser point cloud, and then sequentially select the laser point clouds from the corrected laser point cloud data according to a preset direction until the k-th laser point cloud is selected, and a line residual error determined based on the 1 st to k-th laser point clouds is greater than a preset residual error threshold. In this way, the data processing apparatus removes all the outliers in the process of detecting the laser point cloud included in the corrected laser point cloud data, and can improve the accuracy of data processing.
In the embodiment of the present invention, when the straight line residuals determined based on the 1 st to the kth laser point clouds are greater than the preset residual threshold, the data processing apparatus determines the straight line determined based on the 1 st to the kth-1 st laser point clouds as the straight line corresponding to each of the 1 st to the kth-1 st laser point clouds, and then, based on the kth laser point cloud, continues to perform straight line detection on the laser point clouds included in the corrected laser point cloud data according to the preset direction until the straight line corresponding to each of the laser point clouds included in the corrected laser point cloud data is obtained.
Specifically, in an embodiment of the present invention, the data processing apparatus continuously performs straight line detection on a laser point cloud included in corrected laser point cloud data according to a preset direction with a kth laser point cloud as a reference, including: determining a straight line determined based on the m-th to last laser point clouds as a straight line corresponding to each of the m-th to last laser point clouds when the straight line residual determined based on the m-th to last laser point clouds is smaller than or equal to a preset residual threshold, and the straight line residual determined based on the m-th to last laser point clouds is larger than the preset residual threshold; where m is a natural number greater than k.
It should be noted that, in the embodiment of the present invention, if the linear residual error determined by the data processing apparatus based on the selected mth laser point cloud to the last laser point cloud is smaller than or equal to the preset residual error threshold, and the linear residual error determined based on the mth laser point cloud to the last laser point cloud is greater than the preset residual error threshold, it is indicated that the mth laser point cloud to the last laser point cloud just forms a straight line, at this time, the straight line determined based on the mth laser point cloud to the last laser point cloud may be directly determined as the straight line corresponding to each laser point cloud in the mth laser point cloud to the last laser point cloud, so as to obtain the straight line corresponding to each laser point cloud included in the corrected laser point cloud data.
Specifically, in an embodiment of the present invention, the data processing apparatus continuously performs straight line detection on a laser point cloud included in corrected laser point cloud data according to a preset direction with a kth laser point cloud as a reference, including: determining a straight line determined based on the 1 st to the k th laser point clouds and the m to the last laser point clouds as a straight line corresponding to each of the 1 st to the k th laser point clouds and the m to the last laser point clouds when the straight line residual determined based on the selected m to the last laser point clouds is less than or equal to a preset residual threshold and the straight line residual determined based on the 1 st to the k to the last laser point clouds is less than or equal to a preset residual threshold; where m is a natural number greater than k.
It should be noted that, in the embodiment of the present invention, if the straight line residual error determined by the data processing apparatus based on the selected mth to last laser point clouds is less than or equal to the preset residual error threshold, and the straight line residual error determined based on the 1 st to k th laser point clouds and the mth to last laser point clouds is less than or equal to the preset residual error threshold, it is indicated that the straight lines determined by the 1 st to k th laser point clouds and the mth to last laser point clouds may be combined, and at this time, the data processing apparatus may determine the straight line determined based on the 1 st to k th laser point clouds and the mth to last laser point clouds as the straight line corresponding to each of the 1 st to k th laser point clouds and the mth to last laser point clouds.
Specifically, in an embodiment of the present invention, the data processing apparatus continuously performs straight line detection on a laser point cloud included in corrected laser point cloud data according to a preset direction with a kth laser point cloud as a reference, including: determining a straight line determined based on the m-th to last laser point clouds as a straight line corresponding to each of the m-th to last laser point clouds when the straight line residual determined based on the selected m-th to last laser point clouds is smaller than or equal to a preset residual threshold, and the straight line residual determined based on the 1-th to k-th laser point clouds and the m-th to last laser point clouds is larger than the preset residual threshold; where m is a natural number greater than k.
It should be noted that, in the embodiment of the present invention, if the linear residual determined by the data processing apparatus based on the selected mth to last laser point clouds is less than or equal to the preset residual threshold, and the linear residual determined based on the 1 st to kth laser point clouds and the mth to last laser point clouds is greater than the preset residual threshold, it is indicated that the lines determined by the 1 st to kth laser point clouds and the mth to last laser point clouds may not be merged, and at this time, the data processing apparatus directly determines the line determined based on the mth to last laser point clouds as the line corresponding to each of the mth to last laser point clouds.
S103, setting corresponding weights according to the distances between the laser point clouds included in the corrected laser point cloud data and the corresponding straight lines.
In an embodiment of the present invention, the data processing apparatus sets, for each laser point cloud included in the corrected laser point cloud data, a corresponding weight according to a distance from a corresponding straight line.
It should be noted that, in the embodiment of the present invention, when obtaining each laser point cloud included in the corrected laser point cloud data, the data processing apparatus sets a corresponding weight according to the distance between the corrected laser point cloud data and the corresponding straight line, that is, weights the offset of each laser point cloud, so that the importance level of positioning each laser point cloud data can be better represented, and the accuracy of data processing is improved
S104, determining positioning information of the laser radar and actual laser point cloud data based on the corrected laser point cloud data and weight of each laser point cloud included in the corrected laser point cloud data by using a point-to-line nearest iterative algorithm.
In an embodiment of the invention, the data processing device determines the positioning information of the laser radar and the actual laser point cloud data based on the corrected laser point cloud data and the weight of each laser point cloud included in the corrected laser point cloud data by using a point-to-line nearest iterative algorithm.
It should be noted that, in the embodiment of the present invention, the data processing device may input the corrected laser point cloud data and the weight of each laser point cloud included in the corrected laser point cloud data into a point-to-line nearest iterative algorithm (PL-ICP), so as to obtain the positioning information of the laser radar and the actual laser point cloud data. The point-to-line nearest iterative algorithm can further adjust the corrected laser point cloud data to enable the corrected laser point cloud data to be more in line with the current actual scene, and actual laser point cloud data is obtained.
Fig. 4 is a schematic flow chart of an exemplary laser point cloud data processing according to an embodiment of the present invention. As shown in fig. 4, the data processing device firstly acquires laser point cloud data, inertial measurement unit data and laser radar odometer data, then corrects the laser point cloud data, namely, distortion elimination, based on the inertial measurement unit data and the laser radar odometer data to obtain corrected laser point cloud data, further carries out straight line detection on laser point clouds included in the corrected laser point cloud data, namely, removes external points with larger offset in the corrected laser point cloud data, and sets corresponding weights for each laser point cloud in the laser point cloud data from which the external points are removed; and finally, positioning and adjusting the actual laser point cloud data conforming to the actual scene based on the laser point cloud data with the outer points removed and the weight of each laser point cloud.
The invention provides a data processing method, which comprises the following steps: collecting a frame of laser point cloud data by using a laser radar, and correcting the laser point cloud data to obtain corrected laser point cloud data; performing straight line detection on laser point clouds included in the corrected laser point cloud data to obtain straight lines corresponding to each laser point cloud included in the corrected laser point cloud data; setting a corresponding weight according to a distance from a corresponding straight line for each laser point cloud included in the corrected laser point cloud data; and determining positioning information of the laser radar and actual laser point cloud data based on the corrected laser point cloud data and the weight of each laser point cloud included in the corrected laser point cloud data by using a point-to-line nearest iterative algorithm. According to the data processing method provided by the invention, before the positioning and scene construction process is carried out on the laser point cloud data acquired based on the laser radar, not only is the motion distortion of the laser point cloud data corrected, but also the point cloud data with larger offset error of the laser point cloud data is removed in a linear detection mode; in addition, corresponding weights are set for each laser point cloud, so that the accuracy of positioning information and actual laser point cloud data determined based on the laser point cloud data with higher accuracy is higher, and the accuracy of data processing is improved.
The present invention provides a data processing device, and fig. 5 is a schematic structural diagram of a data processing device according to an embodiment of the present invention. As shown in fig. 5, includes:
the acquisition module 501 is configured to acquire a frame of laser point cloud data by using a laser radar, and correct the laser point cloud data to obtain corrected laser point cloud data;
the detection module 502 is configured to perform line detection on the laser point clouds included in the corrected laser point cloud data, so as to obtain a line corresponding to each laser point cloud included in the corrected laser point cloud data;
a setting module 503, configured to set, for each laser point cloud included in the corrected laser point cloud data, a corresponding weight according to a distance from a corresponding straight line;
a determining module 504, configured to determine positioning information of the laser radar and actual laser point cloud data based on the corrected laser point cloud data and a weight of each laser point cloud included in the corrected laser point cloud data by using a point-to-line nearest iterative algorithm.
In an embodiment of the present invention, the obtaining module 501 is further configured to obtain pose change information of the laser radar in a process of collecting the laser point cloud data by the laser radar; and correcting the laser point cloud data based on the pose change information to obtain corrected laser point cloud data.
In an embodiment of the present invention, the obtaining module 501 is further configured to obtain inertial measurement unit data and/or lidar odometer data; and determining the pose change information according to the inertial measurement unit data and/or the laser radar odometer data by using a nonlinear Kalman filtering method.
In an embodiment of the present invention, the pose change information includes: in the process of acquiring the laser point cloud data by the laser radar, acquiring initial pose information when a first laser point cloud is acquired and final pose information when a last laser point cloud is acquired, the acquisition module 501 is further configured to divide acquisition time length of the laser radar for acquiring the laser point cloud data into a plurality of sub-time lengths; for each duration in the plurality of sub-durations, estimating corresponding pose information by adopting a linear interpolation mode according to the initial pose information and the final pose information; acquiring laser point clouds acquired in each time length in the plurality of sub-time lengths from the laser point cloud data to obtain a plurality of groups of laser point clouds corresponding to the plurality of sub-time lengths one by one; determining pose information corresponding to the same sub-time length as pose information corresponding to each laser point cloud in the group aiming at each laser point cloud in the plurality of groups of laser point clouds; and aiming at each laser point cloud included in the laser point cloud data, taking the coordinate system of the first laser point cloud as a reference coordinate system, and determining the corresponding laser point cloud data under the reference coordinate system by utilizing the corresponding pose information to obtain the corrected laser point cloud data.
In an embodiment of the present invention, the detection module 502 is further configured to select a first laser point cloud from the laser point clouds included in the corrected laser point cloud data, and determine the first laser point cloud as the 1 st laser point cloud; sequentially selecting laser point clouds from the corrected laser point cloud data according to a preset direction by taking the 1 st laser point cloud as a reference until the kth laser point cloud is selected, wherein a straight line residual error determined based on the 1 st laser point cloud to the kth laser point cloud is larger than a preset residual error threshold; wherein k is a natural number greater than 1; determining a straight line determined based on the 1 st to the k-1 st laser point clouds as a straight line corresponding to each of the 1 st to the k-1 st laser point clouds; and continuously detecting the laser point clouds included in the corrected laser point cloud data according to the preset direction by taking the kth laser point cloud as a reference until a straight line corresponding to each laser point cloud included in the corrected laser point cloud data is obtained.
In an embodiment of the present invention, the detection module 502 is further configured to sequentially select laser point clouds from the corrected laser point cloud data according to the preset direction with the 1 st laser point cloud as a reference until the nth laser point cloud is selected, where a straight line parameter determined based on the 1 st laser point cloud to the n-1 st laser point cloud and a straight line parameter determined based on the 1 st laser point cloud to the nth laser point cloud are greater than a preset parameter threshold; wherein n is a natural number greater than 1 and less than or equal to k; removing the nth laser point cloud from the corrected laser point cloud data, and re-determining the next selected laser point cloud as the nth laser point cloud; and sequentially selecting laser point clouds from the corrected laser point cloud data according to the preset direction until the kth laser point cloud is selected, wherein the linear residual error determined based on the 1 st laser point cloud to the kth laser point cloud is larger than the preset residual error threshold value.
In an embodiment of the present invention, the detection module 502 is further configured to determine, as a straight line corresponding to each of the mth to last laser point clouds, a straight line determined based on the mth to last laser point clouds when a straight line residual determined based on the mth to last laser point clouds is less than or equal to the preset residual threshold, and a straight line residual determined based on the 1 st to last laser point clouds is greater than the preset residual threshold; where m is a natural number greater than k.
In an embodiment of the present invention, the detection module 502 is further configured to determine, as a straight line corresponding to each of the 1 st to the k th laser point clouds and the m to the last laser point clouds, a straight line determined based on the 1 st to the k th laser point clouds and the m to the last laser point clouds when a straight line residual determined based on the selected m to the last laser point clouds is less than or equal to the preset residual threshold; where m is a natural number greater than k.
In an embodiment of the present invention, the detection module 502 is further configured to determine, as a straight line corresponding to each of the mth to last laser point clouds, a straight line determined based on the mth to last laser point clouds when a straight line residual determined based on the selected mth to last laser point clouds is less than or equal to the preset residual threshold, and a straight line residual determined based on the 1 st to the kth laser point clouds, and the mth to last laser point clouds is greater than the preset residual threshold; where m is a natural number greater than k.
The invention provides a data processing device, and fig. 6 is a schematic structural diagram of a data processing device according to an embodiment of the invention. As shown in fig. 6, the data processing apparatus includes: a processor 601, a memory 602, and a communication bus 603;
the communication bus 603 is configured to implement a communication connection between the processor 601 and the memory 602;
the processor 601 is configured to execute a computer program stored in the memory 602 to implement the data processing method described above.
The invention provides a data processing device, which collects a frame of laser point cloud data by using a laser radar, corrects the laser point cloud data and obtains corrected laser point cloud data; performing straight line detection on laser point clouds included in the corrected laser point cloud data to obtain straight lines corresponding to each laser point cloud included in the corrected laser point cloud data; setting a corresponding weight according to a distance from a corresponding straight line for each laser point cloud included in the corrected laser point cloud data; and determining positioning information of the laser radar and actual laser point cloud data based on the corrected laser point cloud data and the weight of each laser point cloud included in the corrected laser point cloud data by using a point-to-line nearest iterative algorithm. According to the data processing device, before the positioning and scene construction process is carried out on the laser point cloud data collected based on the laser radar, not only is the motion distortion of the laser point cloud data corrected, but also point cloud data with larger offset error of the laser point cloud data are removed in a linear detection mode; in addition, corresponding weights are set for each laser point cloud, so that the accuracy of positioning information and actual laser point cloud data determined based on the laser point cloud data with higher accuracy is higher, and the accuracy of data processing is improved.
The present invention provides a computer readable storage medium storing one or more computer programs executable by one or more processors to implement the above described data processing method. The computer readable storage medium may be a volatile Memory (RAM), such as a Random-Access Memory (RAM); or a nonvolatile Memory (non-volatile Memory), such as a Read-Only Memory (ROM), a flash Memory (flash Memory), a Hard Disk (HDD) or a Solid State Drive (SSD); but may be a respective device, such as a mobile phone, a computer, a tablet device, a personal digital assistant, etc., comprising one or any combination of the above memories.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. 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.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the scope of the present invention should be included in the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (12)
1. A method of data processing, the method comprising:
collecting a frame of laser point cloud data by using a laser radar, and correcting the laser point cloud data to obtain corrected laser point cloud data;
performing straight line detection on laser point clouds included in the corrected laser point cloud data to obtain straight lines corresponding to each laser point cloud included in the corrected laser point cloud data;
Setting a corresponding weight according to the distance from a corresponding straight line for each laser point cloud included in the corrected laser point cloud data;
and determining positioning information of the laser radar and actual laser point cloud data based on the corrected laser point cloud data and the weight of each laser point cloud included in the corrected laser point cloud data by using a point-to-line nearest iterative algorithm.
2. The method of claim 1, wherein the modifying the laser point cloud data to obtain modified laser point cloud data comprises:
acquiring pose change information of the laser radar in the process of acquiring the laser point cloud data by the laser radar;
and correcting the laser point cloud data based on the pose change information to obtain corrected laser point cloud data.
3. The method according to claim 2, wherein the step of acquiring pose change information of the laser radar during the step of acquiring the laser point cloud data by the laser radar includes:
acquiring inertial measurement unit data and/or laser radar odometer data;
and determining the pose change information according to the inertial measurement unit data and/or the laser radar odometer data by using a nonlinear Kalman filtering method.
4. The method of claim 2, wherein the pose change information comprises: in the process of acquiring the laser point cloud data by the laser radar, acquiring initial pose information when a first laser point cloud is acquired and final pose information when a last laser point cloud is acquired, correcting the laser point cloud data based on the pose change information to obtain corrected laser point cloud data, wherein the method comprises the following steps:
dividing the acquisition time length of the laser radar for acquiring the laser point cloud data into a plurality of sub-time lengths;
for each duration in the plurality of sub-durations, estimating corresponding pose information by adopting a linear interpolation mode according to the initial pose information and the final pose information;
acquiring laser point clouds acquired in each time length in the plurality of sub-time lengths from the laser point cloud data to obtain a plurality of groups of laser point clouds corresponding to the plurality of sub-time lengths one by one;
determining pose information corresponding to the same sub-time length as pose information corresponding to each laser point cloud in the group aiming at each laser point cloud in the plurality of groups of laser point clouds;
and aiming at each laser point cloud included in the laser point cloud data, taking the coordinate system of the first laser point cloud as a reference coordinate system, and determining the corresponding laser point cloud data under the reference coordinate system by utilizing the corresponding pose information to obtain the corrected laser point cloud data.
5. The method according to claim 1, wherein the performing straight line detection on the laser point clouds included in the corrected laser point cloud data to obtain straight lines corresponding to each laser point cloud included in the corrected laser point cloud data includes:
selecting a first laser point cloud from laser point clouds included in the corrected laser point cloud data, and determining the first laser point cloud as a 1 st laser point cloud;
sequentially selecting laser point clouds from the corrected laser point cloud data according to a preset direction by taking the 1 st laser point cloud as a reference until the kth laser point cloud is selected, wherein a straight line residual error determined based on the 1 st laser point cloud to the kth laser point cloud is larger than a preset residual error threshold; wherein k is a natural number greater than 1;
determining a straight line determined based on the 1 st to the k-1 st laser point clouds as a straight line corresponding to each of the 1 st to the k-1 st laser point clouds;
and continuously detecting the laser point clouds included in the corrected laser point cloud data according to the preset direction by taking the kth laser point cloud as a reference until a straight line corresponding to each laser point cloud included in the corrected laser point cloud data is obtained.
6. The method according to claim 5, wherein the sequentially selecting laser point clouds from the corrected laser point cloud data in a preset direction based on the 1 st laser point cloud until the kth laser point cloud is selected, and the straight line residual determined based on the 1 st laser point cloud to the kth laser point cloud is greater than a preset residual threshold value, comprises:
sequentially selecting laser point clouds from the corrected laser point cloud data according to the preset direction by taking the 1 st laser point cloud as a reference until the nth laser point cloud is selected, wherein the linear parameter determined based on the 1 st laser point cloud to the n-1 st laser point cloud and the linear parameter determined based on the 1 st laser point cloud to the nth laser point cloud are larger than a preset parameter threshold; wherein n is a natural number greater than 1 and less than or equal to k;
removing the nth laser point cloud from the corrected laser point cloud data, and re-determining the next selected laser point cloud as the nth laser point cloud;
and sequentially selecting laser point clouds from the corrected laser point cloud data according to the preset direction until the kth laser point cloud is selected, wherein the linear residual error determined based on the 1 st laser point cloud to the kth laser point cloud is larger than the preset residual error threshold value.
7. The method of claim 5, wherein the continuing to perform straight line detection on the laser point cloud included in the corrected laser point cloud data according to the preset direction based on the kth laser point cloud includes:
determining a straight line determined based on the mth laser point cloud to the last laser point cloud as a straight line corresponding to each of the mth laser point cloud to the last laser point cloud when the straight line residual determined based on the mth laser point cloud to the last laser point cloud is smaller than or equal to the preset residual threshold and the straight line residual determined based on the mth laser point cloud to the last laser point cloud is larger than the preset residual threshold; where m is a natural number greater than k.
8. The method of claim 5, wherein the continuing to perform straight line detection on the laser point cloud included in the corrected laser point cloud data according to the preset direction based on the kth laser point cloud includes:
determining a straight line determined based on the 1 st to the k th laser point clouds and the m to the last laser point clouds as a straight line corresponding to each of the 1 st to the k th laser point clouds and the m to the last laser point clouds when a straight line residual determined based on the selected m to the last laser point clouds is less than or equal to the preset residual threshold and a straight line residual determined based on the 1 st to the k to the last laser point clouds is less than or equal to the preset residual threshold; where m is a natural number greater than k.
9. The method of claim 5, wherein the continuing to perform straight line detection on the laser point cloud included in the corrected laser point cloud data according to the preset direction based on the kth laser point cloud includes:
determining a straight line determined based on the mth laser point cloud to the last laser point cloud as a straight line corresponding to each of the mth laser point cloud to the last laser point cloud when the straight line residual determined based on the mth laser point cloud to the last laser point cloud selected is smaller than or equal to the preset residual threshold and the straight line residual determined based on the mth laser point cloud to the kth laser point cloud and the mth laser point cloud is larger than the preset residual threshold; where m is a natural number greater than k.
10. A data processing apparatus, comprising:
the acquisition module is used for acquiring a frame of laser point cloud data by using the laser radar and correcting the laser point cloud data to obtain corrected laser point cloud data;
the detection module is used for carrying out straight line detection on the laser point clouds included in the corrected laser point cloud data to obtain straight lines corresponding to each laser point cloud included in the corrected laser point cloud data;
The setting module is used for setting corresponding weights according to the distance between each laser point cloud included in the corrected laser point cloud data and the corresponding straight line;
the determining module is used for determining positioning information of the laser radar and actual laser point cloud data based on the corrected laser point cloud data and weight of each laser point cloud included in the corrected laser point cloud data by using a point-to-line nearest iterative algorithm.
11. A data processing apparatus, comprising: a processor, a memory, and a communication bus;
the communication bus is used for realizing communication connection between the processor and the memory;
the processor being adapted to execute a computer program stored in the memory for implementing the data processing method of any of claims 1-9.
12. A computer readable storage medium storing one or more computer programs executable by one or more processors to implement the data processing method of any of claims 1-9.
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