CN112164063A - Data processing method and device - Google Patents

Data processing method and device Download PDF

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Publication number
CN112164063A
CN112164063A CN202011190058.9A CN202011190058A CN112164063A CN 112164063 A CN112164063 A CN 112164063A CN 202011190058 A CN202011190058 A CN 202011190058A CN 112164063 A CN112164063 A CN 112164063A
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data
point cloud
acquisition
determining
cloud data
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赵明
王兆圣
刘余钱
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Shanghai Sensetime Lingang Intelligent Technology Co Ltd
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Shanghai Sensetime Lingang Intelligent Technology Co Ltd
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Priority to CN202011190058.9A priority Critical patent/CN112164063A/en
Publication of CN112164063A publication Critical patent/CN112164063A/en
Priority to JP2022514853A priority patent/JP2023503767A/en
Priority to KR1020227007210A priority patent/KR20220058901A/en
Priority to PCT/CN2021/102869 priority patent/WO2022088723A1/en
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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
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
    • G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • 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
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
    • 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
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10028—Range image; Depth image; 3D point clouds
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30248—Vehicle exterior or interior
    • G06T2207/30252—Vehicle exterior; Vicinity of vehicle

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Remote Sensing (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Electromagnetism (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Quality & Reliability (AREA)
  • Traffic Control Systems (AREA)
  • Radar Systems Or Details Thereof (AREA)
  • Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)

Abstract

The present disclosure provides a data processing method and apparatus, including: acquiring positioning data acquired by a positioning device and point cloud data acquired by a radar device; wherein the positioning device and the radar device are deployed on the same vehicle; determining, based on the positioning data and the point cloud data, position data of the radar device at a time when the positioning device acquired the positioning data; determining ground parameter information characterizing the ground based on the point cloud data, and determining initial pose data of the radar device based on the position data; adjusting extrinsic parameter data representing a relative positional relationship between the positioning device and the radar device based on the ground parameter information and the initial pose data.

Description

Data processing method and device
Technical Field
The present disclosure relates to the field of information processing technologies, and in particular, to a data processing method and apparatus.
Background
Along with the development of information technology, unmanned technology is gradually widely applied. At present, in the unmanned technology, radar point cloud data is mainly acquired through a laser radar, then position information of the laser radar is determined based on a Positioning device (such as a Global Positioning System (GPS), a combined inertial navigation System, and the like), then information fusion is performed on the point cloud data and the position information, and a position relationship between the unmanned device and an obstacle is determined, so as to achieve obstacle avoidance.
In the related art, when external parameters are determined, the positions of the positioning device and the laser radar need to be calibrated by a series of devices, but the positions of the positioning device and the laser radar may change when the unmanned device is used, so that the external parameters need to be updated at preset intervals. In each updating process, the positions of the positioning device and the laser radar are recalibrated manually by means of measuring equipment, and then the external reference data is determined again, so that the method is low in efficiency.
Disclosure of Invention
The embodiment of the disclosure at least provides a data processing method and device.
In a first aspect, an embodiment of the present disclosure provides a data processing method, including:
acquiring positioning data acquired by a positioning device and point cloud data acquired by a radar device; wherein the positioning device and the radar device are deployed on the same vehicle;
determining, based on the positioning data and the point cloud data, position data of the radar device at a time when the positioning device acquired the positioning data;
determining ground parameter information characterizing the ground based on the point cloud data, and determining initial pose data of the radar device based on the position data;
adjusting extrinsic parameter data representing a relative positional relationship between the positioning device and the radar device based on the ground parameter information and the initial pose data.
According to the embodiment, the ground parameter information representing the ground and the initial pose data of the radar device can be determined according to the positioning data collected by the positioning device and the radar data collected by the radar device, and then the reference data can be adjusted based on the ground parameter information and the initial pose data of the radar device.
In a possible implementation manner, the point cloud data comprises coordinate information of a plurality of radar scanning points;
determining ground parameter information characterizing the ground based on the point cloud data, including:
performing plane fitting based on the point cloud data to obtain fitting plane information;
determining the distance between each radar scanning point and the plane indicated by the fitting plane information based on the coordinate information of the radar scanning points in the point cloud data;
and screening point cloud data based on the determined distance between each radar scanning point and the plane indicated by the fitting plane information, returning to the step of carrying out plane fitting based on the point cloud data based on the screened point cloud data until a preset iteration condition is reached, and determining the ground parameter information based on the finally screened point cloud data.
In the above embodiment, the point cloud data is screened for multiple times in an iterative fitting manner, and then the ground parameter information is determined based on the point cloud data after final screening, so that the precision of the ground parameter information is improved.
In one possible embodiment, before performing the plane fitting based on the point cloud data, at least one of the following data screening processes is performed:
performing down-sampling processing on the point cloud data to enable the distribution density of radar scanning points in the point cloud data to meet a preset condition;
and determining a target coordinate range according to the preset installation height of the radar device, and screening out radar scanning points of which the corresponding coordinate information is located in the target coordinate range.
Before carrying out plane fitting based on point cloud data, carrying out data screening on the point cloud data, on one hand, the calculation precision of ground parameter information can be improved, on the other hand, the calculation amount in the plane fitting process can be reduced, and the calculation efficiency is improved.
In one possible embodiment, the position data height data;
the method further comprises the following steps:
and dividing the point cloud data into a plurality of acquisition intervals based on the height data in the position data and the corresponding relation between the position data and the point cloud data.
In one possible embodiment, the dividing the point cloud data into a plurality of acquisition intervals based on the height data in the position data and the corresponding relationship between the position data and the point cloud data includes:
filtering the height data, and determining at least one extreme point in the height data reserved after filtering;
and dividing the point cloud data into a plurality of acquisition intervals by taking the acquisition time points corresponding to the extreme points as dividing points of the acquisition intervals.
In one possible embodiment, the determining ground parameter information characterizing the ground based on the point cloud data and determining initial pose data of the radar apparatus based on the position data includes:
for each acquisition interval, determining ground parameter information corresponding to the acquisition interval based on point cloud data in the acquisition interval, and determining initial pose data of a radar device corresponding to the acquisition interval based on position data of the radar device when the point cloud data in the acquisition interval is acquired;
the adjusting, based on the ground parameter information and the initial pose data, extrinsic parameter data that characterizes a relative positional relationship between the positioning device and the radar device includes:
and adjusting external parameter data representing the relative position relation between the positioning device and the radar device based on the ground parameter information and the initial pose data corresponding to each acquisition interval.
In the above embodiment, consideration on ground height information is added, the point cloud data is divided into a plurality of acquisition sections, ground parameter information corresponding to different acquisition sections is determined, and the adjusted external parameter is more accurate when the external parameter data is adjusted based on the ground parameter information corresponding to different acquisition sections and the initial pose data in the acquisition section.
In a possible embodiment, the adjusting, based on the ground parameter information and the initial pose data corresponding to each acquisition interval, extrinsic parameter data that characterizes a relative position relationship between the positioning device and the radar device includes:
determining optimized pose data corresponding to each acquisition interval based on the ground parameter information corresponding to each acquisition interval and the initial pose data;
and adjusting the external parameter data based on the initial pose data corresponding to each acquisition interval and the optimized pose data.
In a possible implementation manner, determining the optimized pose data corresponding to each acquisition interval based on the ground parameter information and the initial pose data corresponding to each acquisition interval includes:
and for any acquisition interval, determining pose data enabling the value of the objective function to be minimum based on the ground parameter information and the initial pose data corresponding to the acquisition interval, and taking the pose data enabling the value of the objective function to be minimum as the optimized pose data corresponding to the acquisition interval, wherein the objective function is the sum of the absolute value of the difference between the ground parameter information before and after pose data optimization and the absolute value of the difference between the pose data before and after the pose data optimization.
In a possible implementation manner, the adjusting the extrinsic parameter data based on the initial pose data and the optimized pose data corresponding to each acquisition interval includes:
determining a first average pose variation amount in each acquisition interval based on the initial pose data corresponding to each acquisition interval and the optimized pose data;
determining second average pose variation quantities corresponding to the plurality of acquisition intervals based on the first average pose variation quantity in each acquisition interval;
and adjusting the extrinsic parameter data based on the second average pose variation.
Due to the fact that the ground parameter information in different acquisition intervals is different, the optimized pose data corresponding to the initial pose data determined according to the ground parameter information in the different acquisition intervals are more accurate, and therefore when the first average pose variation and the second average pose variation are calculated and the external parameter data are adjusted based on the second average pose variation, the adjusted external parameter data are more accurate.
In a possible implementation, the adjusting the extrinsic parameter based on the second average pose change amount includes:
and determining the product of the second average pose variation and the external reference data before adjustment as the adjusted external reference data.
In a possible embodiment, the positioning data includes a plurality of first acquisition time points and position data acquired at each first acquisition time point, and the point cloud data includes a plurality of second acquisition time points and point cloud data acquired at each second acquisition time point;
the determining, based on the positioning data and the point cloud data, position data of the radar device when the positioning device collects the positioning data includes:
for each second acquisition time point, respectively determining two first acquisition time points which are separated from the second acquisition time point by the shortest time, wherein the two first acquisition time points comprise a first acquisition time point before the second acquisition time point and a first acquisition time point after the second acquisition time point;
and determining the position data of the radar device when the point cloud data is acquired at the second acquisition time point based on the position data respectively corresponding to the two first acquisition time points.
Through the embodiment, the position data of the radar device when the point cloud data is acquired at each second acquisition time point can be determined, and the interference caused by the difference of the data acquisition frequencies of the radar device and the positioning device is avoided.
In a second aspect, an embodiment of the present disclosure further provides a data processing apparatus, including:
the acquisition module is used for acquiring positioning data acquired by the positioning device and point cloud data acquired by the radar device; wherein the positioning device and the radar device are deployed on the same vehicle;
a first determining module, configured to determine, based on the positioning data and the point cloud data, position data of the radar apparatus when the positioning apparatus acquires the positioning data;
a second determination module, configured to determine ground parameter information characterizing the ground based on the point cloud data, and determine initial pose data of the radar apparatus based on the position data;
and the adjusting module is used for adjusting external parameter data which is used for representing the relative position relation between the positioning device and the radar device based on the ground parameter information and the initial pose data.
In a third aspect, an embodiment of the present disclosure further provides a computer device, including: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating via the bus when the computer device is running, the machine-readable instructions when executed by the processor performing the steps of the first aspect described above, or any possible implementation of the first aspect.
In a fourth aspect, this disclosed embodiment also provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to perform the steps in the first aspect or any one of the possible implementation manners of the first aspect.
For the description of the effects of the data processing apparatus, the computer device, and the computer-readable storage medium, reference is made to the description of the data processing method, which is not repeated herein.
In order to make the aforementioned objects, features and advantages of the present disclosure more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required for use in the embodiments will be briefly described below, and the drawings herein incorporated in and forming a part of the specification illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the technical solutions of the present disclosure. It is appreciated that the following drawings depict only certain embodiments of the disclosure and are therefore not to be considered limiting of its scope, for those skilled in the art will be able to derive additional related drawings therefrom without the benefit of the inventive faculty.
Fig. 1 shows a schematic flow chart of a data processing method provided by an embodiment of the present disclosure;
FIG. 2 is a schematic diagram illustrating a comparison of a first acquisition time point and a second acquisition time point provided by an embodiment of the present disclosure;
fig. 3 shows another schematic comparison diagram of a first acquisition time point and a second acquisition time point provided by the embodiment of the disclosure;
FIG. 4 is a schematic flow chart illustrating a method for determining ground parameter information characterizing the ground based on point cloud data according to an embodiment of the present disclosure;
fig. 5 is a schematic diagram illustrating a method for determining extreme points of height data according to an embodiment of the present disclosure;
FIG. 6 is a flow chart illustrating a method for adjusting extrinsic parameter data according to an embodiment of the present disclosure;
FIG. 7 is a block diagram of a data processing apparatus according to an embodiment of the present disclosure;
fig. 8 shows a schematic structural diagram of a computer device provided by an embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, not all of the embodiments. The components of the embodiments of the present disclosure, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present disclosure, presented in the figures, is not intended to limit the scope of the claimed disclosure, but is merely representative of selected embodiments of the disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the disclosure without making creative efforts, shall fall within the protection scope of the disclosure.
Generally, before an autonomous vehicle leaves a factory, positions of a positioning device and a radar device on the autonomous vehicle are calibrated by a series of devices so as to determine an external parameter data, however, since the relative position between the positioning device and the radar device changes in the use process of the autonomous vehicle, the external parameter data needs to be updated periodically.
In the related art, if the external parameter data is updated, the external parameter data is determined again by adopting a method for determining the external parameter data when the automatic driving vehicle leaves the factory, so that the steps are complicated, and the efficiency is low.
In another method, the external parameter data can be estimated through a hand-eye calibration algorithm, specifically, the automatic driving vehicle can be controlled to run for a section of track, positioning data collected by a positioning device and point cloud data collected by a radar device on the automatic driving vehicle in the running process are recorded, then pose tracks of the positioning device and the radar device are respectively calculated, and then the external parameter is solved based on the hand-eye calibration algorithm.
However, in this method, the measurement accuracy for the positioning device and the radar device is high, and only the variation parameters in three degrees of freedom can be provided, but since the external reference data are parameters in six degrees of freedom, the external reference error determined by this method is large.
Based on the above, the present disclosure provides a data processing method, which can determine ground parameter information representing the ground and initial pose data of a radar device according to positioning data acquired by a positioning device and radar data acquired by the radar device, and then adjust the external reference data based on the ground parameter information and the initial pose data of the radar device, because the ground parameter information and the initial pose data of the radar device can be acquired or calculated by data acquisition, positions of the positioning device and the radar device do not need to be calibrated by manpower with the help of other devices in the adjustment process of the external reference data, so that the adjustment efficiency of the external reference data is improved, and the labor cost is saved; in addition, in the scheme, the external reference data is adjusted based on the pose variation, and the pose data is data in six degrees of freedom, so that when the external reference data is adjusted, the six amounts of the external reference data can be adjusted, and the adjustment precision of the external reference data is improved.
The above-mentioned drawbacks are the results of the inventor after practical and careful study, and therefore, the discovery process of the above-mentioned problems and the solutions proposed by the present disclosure to the above-mentioned problems should be the contribution of the inventor in the process of the present disclosure.
The technical solutions in the present disclosure will be described clearly and completely with reference to the accompanying drawings in the present disclosure, and it is to be understood that the described embodiments are only a part of the embodiments of the present disclosure, and not all of the embodiments. The components of the present disclosure, as generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present disclosure, presented in the figures, is not intended to limit the scope of the claimed disclosure, but is merely representative of selected embodiments of the disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the disclosure without making creative efforts, shall fall within the protection scope of the disclosure.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
To facilitate understanding of the present embodiment, first, a data processing method disclosed in the embodiments of the present disclosure is described in detail, where an execution subject of the data processing method provided in the embodiments of the present disclosure is generally a computer device with certain computing capability, and the computer device includes, for example: a terminal device, which may be a User Equipment (UE), a mobile device, a User terminal, a cellular phone, a cordless phone, a Personal Digital Assistant (PDA), or the like, or a server or other processing device. In some possible implementations, the data processing method may be processed by a processor mounted on the autopilot device.
The data processing method provided by the embodiment of the present disclosure is described below by taking an execution subject as an example of a processor installed on an automatic driving device, where it should be noted that the positioning device and the radar device are respectively connected to the processor in a manner including, but not limited to, a wired connection and a wireless connection, where the wireless connection may be, for example, a bluetooth connection, a wireless lan connection, and the like.
Referring to fig. 1, a schematic flow chart of a data processing method provided in the embodiment of the present disclosure includes the following steps:
step 101, acquiring positioning data acquired by a positioning device and point cloud data acquired by a radar device; wherein the positioning device and the radar device are deployed on the same vehicle.
And 102, determining position data of the radar device when the positioning device collects the positioning data based on the positioning data and the point cloud data.
And 103, determining ground parameter information representing the ground based on the point cloud data, and determining initial pose data of the radar device based on the position data.
And 104, adjusting external parameter data representing the relative position relation between the positioning device and the radar device based on the ground parameter information and the initial pose data.
According to the implementation method, the ground parameter information representing the ground and the initial pose data of the radar device can be determined according to the positioning data acquired by the positioning device and the radar data acquired by the radar device, and then the reference data can be adjusted based on the ground parameter information and the initial pose data of the radar device.
The following is a detailed description of steps 101 to 104.
For step 101:
the positioning device and the radar device can be installed on the same vehicle, and the installation positions of the positioning device and the radar device are different. The Positioning device may be, for example, a Global Positioning System (GPS), or a combined inertial navigation System in which a GPS and an inertial sensor are combined.
With respect to step 102,
In practical application, the data acquisition frequencies of the radar device and the positioning device are different in the same time period. When the positioning device collects the position data, the positioning device can collect the position data according to the collection frequency set by the GPS satellite within the data collection time; when the radar device collects point cloud data, the radar device can emit radio beams according to a set frequency, and the point cloud data is collected according to the set frequency. In practical application, the acquisition frequency set by the GPS satellite is higher than the set frequency corresponding to the radar device.
The positioning data acquired by the positioning device comprises a plurality of first acquisition time points and position data acquired at each first acquisition time point, wherein the position data acquired at each first acquisition time point comprises longitude and latitude data and altitude data, and the altitude data is the altitude relative to the sea level; the point cloud data comprises a plurality of second acquisition time points and point cloud data acquired at each second acquisition time point, wherein the point cloud data acquired at each second acquisition time point comprises coordinate information of a plurality of radar scanning points.
It should be noted that the positioning data output by the positioning device is the positioning data adjusted based on the external parameter data representing the relative position relationship between the positioning device and the radar device, that is, the positioning data output by the positioning device is the positioning data of the radar device.
Because the frequency that radar apparatus gathered radar data is different with the frequency that positioner gathered the location data, consequently need be based on location data and radar data, confirm the position data of radar apparatus when positioner gathers the location data, confirm the position data that radar apparatus corresponds when every second collection time point gathers point cloud data promptly.
Specifically, for each second acquisition time point, whether a first acquisition time point corresponding to the second acquisition time point exists or not may be detected, if the second acquisition time point has a corresponding first acquisition time point, the position data of the first acquisition time point corresponding to the second acquisition time point is determined as the position data of the radar device when the point cloud data of the second acquisition time point is acquired, and if the second acquisition time point does not have a corresponding first acquisition time point, the position data of the radar device when the point cloud data is acquired at the second acquisition time point is calculated based on a time interval between the first acquisition time point and the second acquisition time point.
In a possible embodiment, when calculating the position data of the radar device at the time of acquiring the point cloud data at the second acquisition time point, for each second acquisition time point, two first acquisition time points which are separated from the second acquisition time point by the shortest time may be determined, respectively, and the two first acquisition time points include one first acquisition time point before the second acquisition time point and one first acquisition time point after the second acquisition time point, and then the position data of the radar device at the time of acquiring the point cloud data at the second acquisition time point is determined based on the position data corresponding to the two first acquisition time points, respectively.
Specifically, when the position data of the radar device at the second acquisition time point is determined based on the data positions corresponding to the two first acquisition time points, if the first acquisition time points and the second acquisition time points are uniformly distributed, the mean value of the position data of the two first acquisition time points with the shortest interval time with the second time point may be determined as the position data of the radar device at the second acquisition time point when the point cloud data is acquired.
Illustratively, as shown in FIG. 2, point a above the horizontal line in FIG. 21、b1、c1、d1、e1、f1、g1All represent first acquisition time points, each first acquisition time point has corresponding position data, a is transversely below2、b2、c2、d2、e2All represent second acquisition time points, each second acquisition time point having corresponding point cloud data for the first acquisition time point a1、d1、g1All have corresponding second acquisition time points, and the specific corresponding relation is a1→a2、d1→c2、g1→e2Then a will be1The corresponding position data is determined as the radar device is in a2Position data of point cloud data is collected, d1Corresponding toPosition data determined as radar means at c2Position data of point cloud data is collected, g1The corresponding position data is determined as the radar means is in e2Collecting position data of the point cloud data; and for a second acquisition time point without a corresponding first acquisition time point, e.g. b2Then select and b2Closest first acquisition time point b1And c1Then b is1And c1The mean value of the corresponding position data is determined as the radar device in b2Position data when point cloud data is collected, and so on, calculating the radar device at d2And collecting position data when the point cloud data is collected.
When calculating the mean value of the position data of the two first acquisition time points, each item of data included in the position data may be averaged, for example, the position data includes longitude, latitude, and altitude, and then the mean value of the longitude, the mean value of the latitude, and the mean value of the altitude may be calculated respectively, and then the mean value of the longitude, the mean value of the latitude, and the mean value of the altitude are taken as the position data after averaging.
When determining the position data of the radar device at the time of acquiring the point cloud data at the second acquisition time point based on the data positions corresponding to the two first acquisition time points, if the first acquisition time points and the second acquisition time points are not uniformly distributed, the position data of the radar device at the time of acquiring the point cloud data at the second acquisition time point without the corresponding first acquisition time may be determined based on the distance between the two first time points and the second acquisition time point.
For example, as shown in fig. 3, a point a and a point b are first acquisition time points, and a point c is a second acquisition time point, when determining the position data of the radar device when acquiring the point cloud data at the point c, the weight corresponding to the position data of the point a and the weight corresponding to the position data of the point b may be respectively determined according to the distance between the point a and the point c and the distance between the point b and the point c, and then the position data of the point a and the position data of the point b are weighted and summed according to the respective weights, so as to obtain the position data of the radar device when acquiring the point cloud data at the point c.
Through the embodiment, the position data of the radar device when the point cloud data is acquired at each second acquisition time point can be determined, and the interference caused by the difference of the data acquisition frequencies of the radar device and the positioning device is avoided.
For step 103:
the ground parameter information characterizing the ground includes a normal vector and an intercept. Specifically, when determining the ground parameter information characterizing the ground based on the point cloud data, the method shown in fig. 4 may be referred to, which includes the following steps:
step 401, performing plane fitting based on the point cloud data to obtain fitting plane information.
When performing plane fitting based on point cloud data, the plane fitting may be performed based on a Random sample consensus (RANSAC). Specifically, point cloud data of a preset proportion can be randomly selected from the point cloud data, and plane fitting is performed based on the randomly selected point cloud data.
Step 402, determining the distance between each radar scanning point and the plane indicated by the fitting plane information based on the coordinate information of the radar scanning points in the point cloud data.
And 403, screening point cloud data based on the determined distance between each radar scanning point and the plane indicated by the fitting plane information.
When the ground parameter information is determined, the ground needs to be fitted according to the point cloud data, so that radar scanning points which are not on the ground are filtered when the point cloud data is screened. Specifically, when the point cloud data is screened based on the determined distance between each radar scanning point and the plane indicated by the fitting plane information, the radar scanning points whose distance between the radar scanning points and the plane indicated by the fitting information exceeds the preset distance range can be filtered.
And step 404, judging whether a preset iteration condition is met.
If the preset iteration condition is satisfied, executing step 405;
and if the preset iteration condition is not met, returning to execute the step 401 based on the screened point cloud data.
The preset iteration condition may be that a preset number of iterations is reached, or when the point cloud data is screened in step 403, distances between all radar scanning points and a plane indicated by the fitting plane information are within a preset distance range.
And 405, determining ground parameter information based on the finally screened point cloud data.
In the above manner, the point cloud data is screened for multiple times in an iterative fitting manner, and then the ground parameter information is determined based on the point cloud data after final screening, so that the precision of the ground parameter information is improved.
When determining the ground parameter information based on the finally screened point cloud data, plane fitting may be performed again based on the finally screened point cloud data, and then the ground parameter information of the fitted plane is determined, wherein the ground parameter information includes a normal vector and an intercept.
In one possible embodiment, in order to improve the efficiency of data screening, before performing plane fitting based on the point cloud data, at least one of the following data screening processes may be further performed:
1. performing down-sampling processing on the point cloud data to enable the distribution density of radar scanning points in the point cloud data to meet preset conditions;
the density of the point cloud data influences the efficiency of plane fitting, so that the point cloud data can be subjected to down-sampling processing, the efficiency of point cloud data screening is improved, and the determination efficiency of ground parameter information is improved. Illustratively, the point cloud data may be down-sampled by a voxel filtering method.
2. And determining a target coordinate range according to the preset installation height of the radar device, and screening out radar scanning points of which the corresponding coordinate information is located in the target coordinate range.
The preset installation height of the radar device is the height of the radar device from the ground, when a world coordinate system is established, the position point where the radar device is located can be used as the starting point of the Z axis, then the target coordinate range is determined according to the preset installation height of the radar device, the target coordinate range can be used for representing the height range of the radar scanning point on the ground on the Z axis, the radar scanning point corresponding to the point cloud data is selected and located in the target coordinate range, the point cloud data can be roughly screened, the efficiency of point cloud data screening can be improved, and the determination efficiency of ground parameter information is improved.
The pose data of the radar apparatus includes at least one of a pitch angle, a yaw angle, and a roll angle in addition to the coordinates of the radar apparatus in the established world coordinate system. When the initial pose data of the radar device is determined based on the position data of the radar device when the point cloud data is acquired at each second acquisition time point, the position data comprises longitude and latitude and height, so that the position of the radar device in the three-dimensional space is determined, and the pose data of the radar device in the world coordinate system is also determined after the world coordinate system is established for the three-dimensional space.
In the data acquisition process, the automatic driving device may not drive on the same ground plane, and therefore, the ground parameter information may not be the same between different ground planes, and therefore, in another embodiment of the present application, the point cloud data may be further divided into a plurality of acquisition intervals according to the height data in the position data and the corresponding relationship between the position data and the point cloud data, each acquisition interval includes a plurality of second acquisition time points, and it may be considered that the vehicle drives on the same ground plane in each acquisition interval.
In a possible implementation manner, when the point cloud data is divided into a plurality of collection intervals according to the height data in the position data, the height data may be filtered based on the height value of the height data at each second collection time point, then at least one extreme point in the height data that is retained after the filtering is determined, and then the collection time point corresponding to each extreme point is used as a dividing point of the collection interval, so that the point cloud data is divided into the plurality of collection intervals.
Considering that the height data collected by the automatic driving device during driving may be affected by ground obstacles, such as jolts caused by the obstacles, the height data collected by the automatic driving device may be filtered. Specifically, the height values with the height value fluctuation frequency higher than the set frequency or the fluctuation amplitude smaller than the set amplitude can be filtered out.
For example, as shown in fig. 5, in the coordinate diagram in fig. 5, the abscissa is the second acquisition time point, the ordinate is the height data in the position data corresponding to each second acquisition time point, the points in the line graph are extreme points, the second acquisition time point between every two extreme points is an acquisition interval, and each acquisition interval includes a plurality of second acquisition time points, and similarly includes a multi-frame point cloud image.
After the point cloud data is divided into a plurality of acquisition intervals, for each acquisition interval, the ground parameter information corresponding to the acquisition interval can be determined based on the point cloud data in the acquisition interval, and the initial pose data of the radar device corresponding to the acquisition interval is determined based on the position data of the radar device when the point cloud data in the acquisition interval is acquired.
After determining the ground parameter information of each acquisition interval and the initial pose data of the radar device corresponding to the acquisition interval, the extrinsic parameter data representing the relative position relationship between the positioning device and the radar device may be adjusted based on the ground parameter information and the initial pose data respectively corresponding to each acquisition interval.
In a possible implementation manner, when the extrinsic parameter data is adjusted based on the ground parameter information and the initial pose data respectively corresponding to each acquisition interval, the extrinsic parameter data adjusting method shown in fig. 6 may be referred to, and includes the following steps:
step 601, determining optimized pose data corresponding to each acquisition interval based on the ground parameter information corresponding to each acquisition interval and the initial pose data.
Specifically, when the optimized pose data corresponding to each acquisition interval is determined based on the ground parameter information and the initial pose data corresponding to each acquisition interval, for any acquisition interval, the optimized pose data that minimizes the value of the objective function may be determined based on the ground parameter information and the initial pose data corresponding to the acquisition interval, and the pose data that minimizes the value of the objective function may be used as the optimized pose data corresponding to the acquisition interval, where the objective function is the sum of the absolute value of the difference between the ground parameter information before and after the pose data is optimized and the absolute value of the difference between the pose data before and after the pose data is optimized.
For example, the objective function may be as follows:
Figure BDA0002752492460000171
wherein the content of the first and second substances,
Figure BDA0002752492460000172
initial pose data representing the ith frame of point cloud image,
Figure BDA0002752492460000173
representing the optimized pose data to be calculated corresponding to the initial pose data of the ith frame of point cloud image,
Figure BDA0002752492460000174
representing the ground parameter information before calculation optimization, namely the determined ground parameter information based on the point cloud data in the acquisition interval,
Figure BDA0002752492460000175
and N represents the number of point cloud images contained in the point cloud data in the acquisition interval, namely the number of second acquisition time points in the acquisition interval.
Aiming at any acquisition interval, a group of point cloud data is acquired at each second acquisition time point of the acquisition interval, the group of point cloud data can form a frame of point cloud image, in the data acquisition process, the plurality of second acquisition time points are included and correspond to a plurality of groups of point cloud data, each group of point cloud data corresponds to one frame of point cloud image, for each group of point cloud data, initial pose data corresponding to the group of point cloud data are calculated and are brought into the formula, the formula is solved through a least square method, and optimized pose data corresponding to the initial pose data of each frame of point cloud image can be obtained.
In addition, the optimized ground parameter information can be obtained by solving the equation, but the optimized ground parameter information is irrelevant to the adjustment external parameter data.
For example, if N is 3, the above formula can be expanded as:
Figure BDA0002752492460000181
by the least square method, the optimized pose data corresponding to the first frame point cloud image can be respectively calculated
Figure BDA0002752492460000182
Optimized pose data corresponding to second frame point cloud image
Figure BDA0002752492460000183
Optimized pose data corresponding to third frame point cloud image
Figure BDA0002752492460000184
And optimized ground parameter information
Figure BDA0002752492460000185
It should be noted that, in the following description,
Figure BDA0002752492460000186
and
Figure BDA0002752492460000187
can be a six-dimensional vector, and each element in the vector respectively represents the world coordinates of the radar deviceCoordinates x, y, z in the system, pitch angle, yaw angle, and roll angle,
Figure BDA0002752492460000188
and
Figure BDA0002752492460000189
the vector can be a four-dimensional vector, each element in the vector respectively represents the value and intercept of a normal vector, and the normal vector is a three-dimensional vector.
Step 602, adjusting the extrinsic parameter data based on the initial pose data corresponding to each acquisition interval and the optimized pose data.
Specifically, when the reference data is adjusted based on the initial pose data corresponding to each acquisition interval and the optimized pose data, a first average pose variation in the acquisition interval may be determined based on the initial pose data corresponding to each acquisition interval and the optimized pose data; then determining second average pose variation quantities corresponding to the multiple acquisition intervals based on the first average pose variation quantity in each acquisition interval; and adjusting the reference data based on the second average pose variation.
In a possible implementation manner, when determining the first average pose change amount of any acquisition interval, for each frame of point cloud image corresponding to point cloud data in the acquisition interval, the pose change amount between the initial pose data corresponding to the frame of point cloud image and the optimized pose data may be determined, and then the first average pose change amount is calculated based on the pose change amounts of multiple frames of point cloud images corresponding to the point cloud data.
For any frame of point cloud image, when calculating the pose variation between the initial pose data corresponding to the frame of point cloud image and the optimized pose data, the longitude and latitude and the height in the initial pose data and the variation corresponding to the longitude and latitude and the height in the optimized pose data can be respectively calculated.
In one possible implementation, when the external parameter data is adjusted based on the second average pose change amount, a product between the second average pose change amount and the external parameter data before adjustment may be determined as the adjusted external parameter data.
By the method, the ground parameter information and the initial pose data of the radar device can be determined according to the positioning data collected by the positioning device and the radar data collected by the radar device, and then the external reference data is adjusted based on the ground parameter information and the initial pose data of the radar device.
It will be understood by those skilled in the art that in the method of the present invention, the order of writing the steps does not imply a strict order of execution and any limitations on the implementation, and the specific order of execution of the steps should be determined by their function and possible inherent logic.
Based on the same inventive concept, a data processing apparatus corresponding to the data processing method is also provided in the embodiments of the present disclosure, and because the principle of the apparatus in the embodiments of the present disclosure for solving the problem is similar to the data processing method described above in the embodiments of the present disclosure, the implementation of the apparatus may refer to the implementation of the method, and repeated details are not described again.
Referring to fig. 7, there is shown a schematic architecture diagram of a data processing apparatus according to an embodiment of the present disclosure, where the apparatus includes: an obtaining module 701, a first determining module 702, a second determining module 703 and an adjusting module 704; wherein the content of the first and second substances,
an obtaining module 701, configured to obtain positioning data collected by a positioning device and point cloud data collected by a radar device; wherein the positioning device and the radar device are deployed on the same vehicle;
a first determining module 702, configured to determine, based on the positioning data and the point cloud data, position data of the radar apparatus at a time when the positioning apparatus acquires the positioning data;
a second determining module 703, configured to determine, based on the point cloud data, ground parameter information representing the ground, and determine, based on the position data, initial pose data of the radar apparatus;
an adjusting module 704, configured to adjust extrinsic parameter data representing a relative position relationship between the positioning device and the radar device based on the ground parameter information and the initial pose data.
In one possible embodiment, the point cloud data includes coordinate information of a plurality of radar scanning points;
the second determining module 703, when determining the ground parameter information characterizing the ground based on the point cloud data, is configured to:
performing plane fitting based on the point cloud data to obtain fitting plane information;
determining the distance between each radar scanning point and the plane indicated by the fitting plane information based on the coordinate information of the radar scanning points in the point cloud data;
and screening point cloud data based on the determined distance between each radar scanning point and the plane indicated by the fitting plane information, returning to the step of carrying out plane fitting based on the point cloud data based on the screened point cloud data until a preset iteration condition is reached, and determining the ground parameter information based on the finally screened point cloud data.
In a possible embodiment, the second determining module 703 is further configured to perform at least one of the following data screening processes before performing plane fitting based on the point cloud data:
performing down-sampling processing on the point cloud data to enable the distribution density of radar scanning points in the point cloud data to meet a preset condition;
and determining a target coordinate range according to the preset installation height of the radar device, and screening out radar scanning points of which the corresponding coordinate information is located in the target coordinate range.
In one possible embodiment, the position data height data;
the second determining module 703 is further configured to:
and dividing the point cloud data into a plurality of acquisition intervals based on the height data in the position data and the corresponding relation between the position data and the point cloud data.
In a possible implementation, the second determining module 703, when dividing the point cloud data into a plurality of acquisition intervals based on the height data in the position data and the corresponding relationship between the position data and the point cloud data, is configured to:
filtering the height data, and determining at least one extreme point in the height data reserved after filtering;
and dividing the point cloud data into a plurality of acquisition intervals by taking the acquisition time points corresponding to the extreme points as dividing points of the acquisition intervals.
In a possible implementation, the second determining module 703, when determining ground parameter information characterizing the ground based on the point cloud data and determining initial pose data of the radar apparatus based on the position data, is configured to:
for each acquisition interval, determining ground parameter information corresponding to the acquisition interval based on point cloud data in the acquisition interval, and determining initial pose data of a radar device corresponding to the acquisition interval based on position data of the radar device when the point cloud data in the acquisition interval is acquired;
the adjusting module 704, when adjusting extrinsic parameter data representing a relative positional relationship between the positioning device and the radar device based on the ground parameter information and the initial pose data, is configured to:
and adjusting external parameter data representing the relative position relation between the positioning device and the radar device based on the ground parameter information and the initial pose data corresponding to each acquisition interval.
In a possible implementation, the adjusting module 704, when adjusting extrinsic parameter data representing a relative position relationship between the positioning device and the radar device based on the ground parameter information and the initial pose data corresponding to each acquisition interval, is configured to:
determining optimized pose data corresponding to each acquisition interval based on the ground parameter information corresponding to each acquisition interval and the initial pose data;
and adjusting the external parameter data based on the initial pose data corresponding to each acquisition interval and the optimized pose data.
In a possible implementation, the adjusting module 704, when determining the optimized pose data corresponding to each acquisition interval based on the ground parameter information and the initial pose data corresponding to each acquisition interval, is configured to:
and for any acquisition interval, determining pose data enabling the value of the objective function to be minimum based on the ground parameter information and the initial pose data corresponding to the acquisition interval, and taking the pose data enabling the value of the objective function to be minimum as the optimized pose data corresponding to the acquisition interval, wherein the objective function is the sum of the absolute value of the difference between the ground parameter information before and after pose data optimization and the absolute value of the difference between the pose data before and after the pose data optimization.
In a possible implementation, the adjusting module 704, when adjusting the extrinsic parameter data based on the initial pose data and the optimized pose data corresponding to each acquisition interval, is configured to:
determining a first average pose variation amount in each acquisition interval based on the initial pose data corresponding to each acquisition interval and the optimized pose data;
determining second average pose variation quantities corresponding to the plurality of acquisition intervals based on the first average pose variation quantity in each acquisition interval;
and adjusting the extrinsic parameter data based on the second average pose variation.
In a possible implementation, the adjusting module 704, when adjusting the extrinsic parameter based on the second average pose change amount, is configured to:
and determining the product of the second average pose variation and the external reference data before adjustment as the adjusted external reference data.
In a possible embodiment, the positioning data comprises a plurality of first acquisition time points and position data acquired at each first acquisition time point, and the point cloud data comprises a plurality of second acquisition time points and point cloud data acquired at each second acquisition time point;
the first determining module 702, when determining the position data of the radar apparatus at the time of the positioning apparatus acquiring the positioning data based on the positioning data and the point cloud data, is configured to:
for each second acquisition time point, respectively determining two first acquisition time points which are separated from the second acquisition time point by the shortest time, wherein the two first acquisition time points comprise a first acquisition time point before the second acquisition time point and a first acquisition time point after the second acquisition time point;
and determining the position data of the radar device when the point cloud data is acquired at the second acquisition time point based on the position data respectively corresponding to the two first acquisition time points.
The description of the processing flow of each module in the device and the interaction flow between the modules may refer to the related description in the above method embodiments, and will not be described in detail here.
In some embodiments, the functions of the apparatus provided in the embodiments of the present disclosure or the included templates may be used to execute the method described in the above method embodiments, and specific implementation thereof may refer to the description of the above method embodiments, and for brevity, no further description is provided here.
Based on the same technical concept, the embodiment of the disclosure also provides computer equipment. Referring to fig. 8, a schematic structural diagram of a computer device provided in the embodiment of the present disclosure includes a processor 801, a memory 802, and a bus 803. The memory 802 is used for storing execution instructions and includes a memory 8021 and an external memory 8022; the memory 8021 is also referred to as an internal memory, and is used for temporarily storing operation data in the processor 801 and data exchanged with an external storage 8022 such as a hard disk, the processor 801 exchanges data with the external storage 8022 through the memory 8021, and when the computer apparatus 800 operates, the processor 801 communicates with the storage 802 through the bus 803, so that the processor 801 executes the following instructions:
acquiring positioning data acquired by a positioning device and point cloud data acquired by a radar device; wherein the positioning device and the radar device are deployed on the same vehicle;
determining, based on the positioning data and the point cloud data, position data of the radar device at a time when the positioning device acquired the positioning data;
determining ground parameter information characterizing the ground based on the point cloud data, and determining initial pose data of the radar device based on the position data;
adjusting extrinsic parameter data representing a relative positional relationship between the positioning device and the radar device based on the ground parameter information and the initial pose data.
The specific processing procedures executed by the processor 801 may refer to the description of the above method embodiments, and are not further described here.
The embodiments of the present disclosure also provide a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to perform the steps of the data processing method described in the above method embodiments. The storage medium may be a volatile or non-volatile computer-readable storage medium.
The computer program product of the data processing method provided by the embodiment of the present disclosure includes a computer readable storage medium storing a program code, where the program code includes instructions for executing the steps of the data processing method described in the above method embodiment. The computer program product may be embodied in hardware, software or a combination thereof. In an alternative embodiment, the computer program product is embodied in a computer storage medium, and in another alternative embodiment, the computer program product is embodied in a Software product, such as a Software Development Kit (SDK), or the like.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and the apparatus described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. In the several embodiments provided in the present disclosure, it should be understood that the disclosed system, apparatus, and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present disclosure may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present disclosure. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
Finally, it should be noted that: the above-mentioned embodiments are merely specific embodiments of the present disclosure, which are used for illustrating the technical solutions of the present disclosure and not for limiting the same, and the scope of the present disclosure is not limited thereto, and although the present disclosure is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive of the technical solutions described in the foregoing embodiments or equivalent technical features thereof within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present disclosure, and should be construed as being included therein. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.

Claims (14)

1. A data processing method, comprising:
acquiring positioning data acquired by a positioning device and point cloud data acquired by a radar device; wherein the positioning device and the radar device are deployed on the same vehicle;
determining, based on the positioning data and the point cloud data, position data of the radar device at a time when the positioning device acquired the positioning data;
determining ground parameter information characterizing the ground based on the point cloud data, and determining initial pose data of the radar device based on the position data;
adjusting extrinsic parameter data representing a relative positional relationship between the positioning device and the radar device based on the ground parameter information and the initial pose data.
2. The method of claim 1, wherein the point cloud data includes coordinate information of a plurality of radar scan points;
determining ground parameter information characterizing the ground based on the point cloud data, including:
performing plane fitting based on the point cloud data to obtain fitting plane information;
determining the distance between each radar scanning point and the plane indicated by the fitting plane information based on the coordinate information of the radar scanning points in the point cloud data;
and screening point cloud data based on the determined distance between each radar scanning point and the plane indicated by the fitting plane information, returning to the step of carrying out plane fitting based on the point cloud data based on the screened point cloud data until a preset iteration condition is reached, and determining the ground parameter information based on the finally screened point cloud data.
3. The method of claim 2, wherein prior to performing the plane fitting based on the point cloud data, performing at least one of the following data screening processes:
performing down-sampling processing on the point cloud data to enable the distribution density of radar scanning points in the point cloud data to meet a preset condition;
and determining a target coordinate range according to the preset installation height of the radar device, and screening out radar scanning points of which the corresponding coordinate information is located in the target coordinate range.
4. The method of claim 1, wherein the position data height data;
the method further comprises the following steps:
and dividing the point cloud data into a plurality of acquisition intervals based on the height data in the position data and the corresponding relation between the position data and the point cloud data.
5. The method of claim 4, wherein the partitioning the point cloud data into a plurality of acquisition intervals based on the height data in the location data and the correspondence between location data and point cloud data comprises:
filtering the height data, and determining at least one extreme point in the height data reserved after filtering;
and dividing the point cloud data into a plurality of acquisition intervals by taking the acquisition time points corresponding to the extreme points as dividing points of the acquisition intervals.
6. The method of claim 4, wherein determining ground parameter information characterizing the ground based on the point cloud data and determining initial pose data of the radar device based on the location data comprises:
for each acquisition interval, determining ground parameter information corresponding to the acquisition interval based on point cloud data in the acquisition interval, and determining initial pose data of a radar device corresponding to the acquisition interval based on position data of the radar device when the point cloud data in the acquisition interval is acquired;
the adjusting, based on the ground parameter information and the initial pose data, extrinsic parameter data that characterizes a relative positional relationship between the positioning device and the radar device includes:
and adjusting external parameter data representing the relative position relation between the positioning device and the radar device based on the ground parameter information and the initial pose data corresponding to each acquisition interval.
7. The method of claim 6, wherein the adjusting extrinsic parameter data that characterizes a relative positional relationship between the positioning device and the radar device based on the ground parameter information and the initial pose data corresponding to each acquisition interval comprises:
determining optimized pose data corresponding to each acquisition interval based on the ground parameter information corresponding to each acquisition interval and the initial pose data;
and adjusting the external parameter data based on the initial pose data corresponding to each acquisition interval and the optimized pose data.
8. The method of claim 7, wherein determining the optimized pose data for each acquisition interval based on the ground parameter information and the initial pose data for each acquisition interval comprises:
and for any acquisition interval, determining pose data enabling the value of the objective function to be minimum based on the ground parameter information and the initial pose data corresponding to the acquisition interval, and taking the pose data enabling the value of the objective function to be minimum as the optimized pose data corresponding to the acquisition interval, wherein the objective function is the sum of the absolute value of the difference between the ground parameter information before and after pose data optimization and the absolute value of the difference between the pose data before and after the pose data optimization.
9. The method of claim 7, wherein the adjusting the extrinsic parameter data based on the initial pose data and the optimized pose data for each acquisition interval comprises:
determining a first average pose variation amount in each acquisition interval based on the initial pose data corresponding to each acquisition interval and the optimized pose data;
determining second average pose variation quantities corresponding to the plurality of acquisition intervals based on the first average pose variation quantity in each acquisition interval;
and adjusting the extrinsic parameter data based on the second average pose variation.
10. The method of claim 9, wherein the adjusting the extrinsic parameter based on the second average pose change amount comprises:
and determining the product of the second average pose variation and the external reference data before adjustment as the adjusted external reference data.
11. The method according to claim 1, wherein the positioning data comprises a plurality of first acquisition time points and position data acquired at each first acquisition time point, and the point cloud data comprises a plurality of second acquisition time points and point cloud data acquired at each second acquisition time point;
the determining, based on the positioning data and the point cloud data, position data of the radar device when the positioning device collects the positioning data includes:
for each second acquisition time point, respectively determining two first acquisition time points which are separated from the second acquisition time point by the shortest time, wherein the two first acquisition time points comprise a first acquisition time point before the second acquisition time point and a first acquisition time point after the second acquisition time point;
and determining the position data of the radar device when the point cloud data is acquired at the second acquisition time point based on the position data respectively corresponding to the two first acquisition time points.
12. A data processing apparatus, comprising:
the acquisition module is used for acquiring positioning data acquired by the positioning device and point cloud data acquired by the radar device; wherein the positioning device and the radar device are deployed on the same vehicle;
a first determining module, configured to determine, based on the positioning data and the point cloud data, position data of the radar apparatus when the positioning apparatus acquires the positioning data;
a second determination module, configured to determine ground parameter information characterizing the ground based on the point cloud data, and determine initial pose data of the radar apparatus based on the position data;
and the adjusting module is used for adjusting external parameter data which is used for representing the relative position relation between the positioning device and the radar device based on the ground parameter information and the initial pose data.
13. A computer device, comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating over the bus when a computer device is running, the machine-readable instructions when executed by the processor performing the steps of the data processing method of any of claims 1 to 11.
14. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, performs the steps of the data processing method according to any one of claims 1 to 11.
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CN112835007A (en) * 2021-01-07 2021-05-25 北京百度网讯科技有限公司 Point cloud data conversion method and device, electronic equipment and storage medium
CN112835007B (en) * 2021-01-07 2023-04-18 北京百度网讯科技有限公司 Point cloud data conversion method and device, electronic equipment and storage medium
CN112946591A (en) * 2021-02-26 2021-06-11 商汤集团有限公司 External parameter calibration method and device, electronic equipment and storage medium
CN112946612A (en) * 2021-03-29 2021-06-11 上海商汤临港智能科技有限公司 External parameter calibration method and device, electronic equipment and storage medium
CN112946612B (en) * 2021-03-29 2024-05-17 上海商汤临港智能科技有限公司 External parameter calibration method and device, electronic equipment and storage medium
CN113484843A (en) * 2021-06-02 2021-10-08 福瑞泰克智能系统有限公司 Method and device for determining external parameters between laser radar and integrated navigation

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