CN112414415A - High-precision point cloud map construction method - Google Patents
High-precision point cloud map construction method Download PDFInfo
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- CN112414415A CN112414415A CN202011003627.4A CN202011003627A CN112414415A CN 112414415 A CN112414415 A CN 112414415A CN 202011003627 A CN202011003627 A CN 202011003627A CN 112414415 A CN112414415 A CN 112414415A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
- G01C21/30—Map- or contour-matching
- G01C21/32—Structuring or formatting of map data
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3407—Route searching; Route guidance specially adapted for specific applications
- G01C21/3415—Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents
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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
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
- G01S19/47—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
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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
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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Abstract
The embodiment of the invention relates to a high-precision point cloud map construction method, which comprises the following steps: receiving first sensor data sent by a terminal; judging whether the first sensor data accords with a data qualification judgment rule or not; when the first sensor data accords with the data qualification judgment rule, obtaining second sensor data according to the sensor data; constructing a point cloud map according to the second sensor data; the point cloud map comprises first positioning data; and when the point cloud map meets the precision condition, the distortion condition, the smoothness condition, the continuity condition and the integrity condition, determining that the point cloud map passes the verification. The high-precision point cloud map construction method provided by the embodiment of the invention improves the quality of the point cloud map and can simultaneously execute the construction of a plurality of point cloud map tasks.
Description
Technical Field
The invention relates to the technical field of automatic driving, in particular to a high-precision point cloud map construction method.
Background
Currently, the point cloud map construction methods applied in the automatic driving industry are roughly divided into two types, namely a point cloud map construction method based on offline data and a point cloud map construction method based on online data.
According to the point cloud map construction method based on the offline data, if the point cloud map construction is completed offline based on the vehicle end, the requirements on the performance of a vehicle-mounted processor are high, a large map is difficult to process, the map quality and errors cannot be guaranteed, and the point cloud map construction cannot be completed simultaneously; if the point cloud map construction is completed on the basis of a local desktop in an off-line mode, complex processes such as data downloading, software starting, achievement data uploading and the like are involved, manual intervention is needed more, the automation degree is low, the efficiency is reduced, the point cloud maps of a plurality of scenes cannot be constructed simultaneously, and waste of manual resources and local computing resources is caused seriously.
According to the point cloud map construction method based on the online data, because point cloud map construction tasks are all completed in the vehicle-mounted processor, the requirement on the performance of the vehicle-mounted processor is high, a large map is difficult to process, the map quality cannot be guaranteed, point cloud map verification is lost, data accumulation in the later period is not friendly, and a plurality of point cloud map construction tasks cannot be simultaneously realized due to vehicle and scene limitations.
Disclosure of Invention
The invention aims to provide a high-precision point cloud map construction method aiming at the defects in the prior art. The quality of the point cloud map is improved by verifying the constructed point cloud map.
In order to achieve the above object, in a first aspect, the present invention provides a high-precision point cloud map construction method, including:
receiving first sensor data sent by a terminal; the first sensor data comprises point cloud data, inertial navigation data, wheel speed meter data, GPS positioning data and image data;
after receiving the first sensor data, generating a map task construction request message for constructing a map;
acquiring the current memory occupation amount and the current map task construction number according to the map task construction request message;
when the current memory occupation amount is smaller than a preset memory occupation amount and the current map task construction number is smaller than a preset map task construction threshold value, judging whether the first sensor data accords with a data qualification judgment rule;
when the first sensor data accord with a data qualification judgment rule, second sensor data are obtained according to the point cloud data, the inertial navigation data, the wheel speed meter data, the GPS positioning data and the image data;
constructing a point cloud map according to the second sensor data; the point cloud map comprises a plurality of points, each of the points having first positioning data;
verifying the point cloud map, comparing the first positioning data with second positioning data acquired by a real-time dynamic carrier phase differential technology (RTK) when a flag bit of the RTK represents that a signal is normal, and determining that the point cloud map meets a precision condition when a difference value between the first positioning data and the second positioning data is not greater than a preset threshold value;
generating a local path with a preset length according to the inertial navigation data and the wheel speed meter data, comparing the local path with a local path in a point cloud map with the same preset length, and determining that the point cloud map meets a distortion condition when the difference value of the curvature of each point of the local path and the local path in the point cloud map with the same preset length is not greater than a preset difference value threshold;
judging whether a connecting line between a plurality of first positioning data in the point cloud map has jump or not, and determining that the point cloud map meets a smoothness condition when the jump does not exist;
judging whether the distance between any two adjacent points corresponding to the first positioning data among the first positioning data in the point cloud map is not greater than a preset distance threshold, and when the distance is not greater than the preset distance threshold, determining that the point cloud map meets a continuity condition;
judging whether the point is complete or not, and determining that the point cloud map meets the complete condition when the point is complete;
and when the point cloud map meets the precision condition, the distortion condition, the smoothness condition, the continuity condition and the integrity condition, determining that the point cloud map passes the verification.
Preferably, the first sensor data sent by the receiving terminal specifically includes:
receiving multiframe original sensor data sent by a terminal; each frame of the raw sensor data comprises a frame header;
judging whether the transmission of the multi-frame original sensor data is finished or not according to the frame header;
and when the transmission is finished, splicing the multi-frame original sensor data according to the frame header to obtain first sensor data.
Preferably, the receiving the first sensor data sent by the terminal previously includes:
the method comprises the following steps that a sensor receives a map acquisition instruction sent by a terminal, and data are acquired according to a preset route and a preset mode according to the map acquisition instruction;
and when the data acquisition is finished, the terminal indicates that the data acquisition is finished by exporting the instruction.
Preferably, the judging whether the first sensor data meets the data qualification judgment rule specifically includes:
judging whether the format of the first sensor data is a preset format or not;
and when the format of the first sensor data does not conform to the preset format, generating a first prompt message for indicating to reacquire the first sensor data, and sending the first prompt message to a terminal.
Preferably, the judging whether the first sensor data meets the data qualification judgment rule specifically includes:
judging whether the data quality of the first sensor meets the requirement or not;
and when the quality of the first sensor data does not meet the requirement, generating a second prompt message for indicating to reacquire the original sensor data, and sending the second prompt message to the terminal.
Preferably, the point cloud data is a first data set corresponding to a first sampling frequency, each data in the first data set has a first time stamp, and when the first time stamp is inconsistent with the first sampling frequency, it is determined that the quality of the point cloud data does not meet the requirement; and/or the presence of a gas in the gas,
the inertial navigation data is a second data set corresponding to a second sampling frequency, each data in the second data set has a second timestamp, and when the second timestamp is inconsistent with the second sampling frequency, the inertial navigation data quality is determined to be not in accordance with the requirement; and/or the presence of a gas in the gas,
the wheel speed meter data is a third data set corresponding to a third sampling frequency, each data in the third data set is provided with a third time stamp, and when the third time stamp is inconsistent with the third sampling frequency, the quality of the wheel speed meter data is determined to be not qualified; and/or the presence of a gas in the gas,
the GPS positioning data is a fourth data set corresponding to a fourth sampling frequency, each data in the fourth data set has a fourth time stamp, and when the fourth time stamp is inconsistent with the fourth sampling frequency, the quality of the GPS positioning data is determined to be not in accordance with the requirement; and/or the presence of a gas in the gas,
the image data is a fifth data set corresponding to a fifth sampling frequency, each data in the fifth data set has a fifth time stamp, and when the fifth time stamp is inconsistent with the fifth sampling frequency, the image data quality is determined to be not satisfactory.
Preferably, second sensor data is obtained according to the point cloud data, the inertial navigation data, the wheel speed meter data, the GPS positioning data and the image data;
the inertial navigation data comprise acceleration information, angular velocity information and attitude angle information of the vehicle;
the wheel speed count data comprises angular velocity information, linear velocity information and vehicle yaw rate information of the left wheel and the right wheel of the vehicle;
and fusing the point cloud data, the acceleration information, the angular speed information and the attitude angle information of the vehicle, the angular speed information, the linear speed information and the vehicle yaw rate information of the left wheel and the right wheel of the vehicle, the GPS positioning data and the first image data to obtain second sensor data.
In a second aspect, the invention provides an apparatus comprising a memory for storing a program and a processor for performing the method of any of the first aspects.
In a third aspect, the invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the method of any of the first aspects.
According to the high-precision point cloud map construction method provided by the embodiment of the invention, the cloud server automatically responds and triggers the point cloud map construction tasks, so that the construction of a plurality of point cloud map tasks can be realized simultaneously, and the difficulty that a large map is difficult to process is overcome; when the first sensor data meet the data qualification judgment rule, the point cloud map is constructed, and the integrity, precision, distortion, smoothness and continuity of the point cloud map are verified, so that the error in constructing the point cloud map is reduced, the requirement of the point cloud map on constructing high precision is met, and the quality of the point cloud map is improved.
Drawings
Fig. 1 is a schematic flow chart of a high-precision point cloud map construction method according to an embodiment of the present invention.
Detailed Description
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
The high-precision point cloud map construction method provided by the embodiment of the invention is applied to a cloud server and is used for constructing the high-precision point cloud map.
It should be noted that the sensor data required for constructing the high-precision point cloud map is acquired by an intelligent vehicle, wherein the intelligent vehicle may be understood as an unmanned autonomous vehicle. The intelligent vehicle is equipped with various sensors, the source of the point cloud map data is based on the sensors, and the various sensors include but are not limited to a camera, a laser radar, a Global Positioning System (GPS), an Inertial Measurement Unit (IMU) and a wheel speed meter. The number of the cameras and the laser radar can be multiple, and the cameras and the laser radar are respectively arranged on the periphery of the intelligent vehicle; the GPS is arranged on the top of the vehicle; the inertia measurement unit can be arranged at a hidden position such as a chassis of a vehicle; the wheel speed meters can be two and are respectively arranged on the left wheel and the right wheel of the vehicle.
Fig. 1 is a schematic flow chart of a high-precision point cloud map construction method according to an embodiment of the present invention. As shown in fig. 1, a high-precision point cloud map construction method provided by an embodiment of the present invention includes the following steps:
step 101, receiving first sensor data sent by a terminal; the first sensor data comprises point cloud data, inertial navigation data, wheel speed meter data, GPS positioning data and image data;
specifically, the point cloud data is data obtained through a laser radar, the data of the laser radar may be one or multiple, and when the number of the laser radars is multiple, the point cloud data represents a set of point cloud data obtained by the laser radars.
The inertial navigation data refers to current acceleration information, angular velocity information and attitude angle information of the vehicle, which are acquired by an inertial measurement unit IMU.
The wheel speed meter data is angular velocity information, linear velocity information, and vehicle yaw rate information of the left and right wheels of the vehicle, which are acquired by the wheel speed meter.
The GPS positioning data is position information of the vehicle acquired by GPS. Because of the error in GPS positioning, the position information of the vehicle is not the current accurate position information of the vehicle. In a preferred embodiment, to reduce the error between the position information of the vehicle and the current position of the vehicle, the GPS is preferably a differential GPS, and the smart vehicle acquires the position information of the vehicle detected by the differential GPS.
The image data is environmental data around the vehicle acquired by the cameras, and when the number of the cameras is plural, the image data represents a set of the environmental data acquired by the plural cameras.
Before step 101, the intelligent vehicle receives driving information sent by a cloud server or a terminal, the driving information includes destination and time information, and the intelligent vehicle automatically drives to a corresponding block according to the destination information and the time information. After a sensor arranged on the intelligent vehicle receives a map acquisition instruction sent by a terminal, acquiring data according to a preset route and a preset mode according to the map acquisition instruction; and when the data acquisition of the sensor is completed, the terminal indicates that the data acquisition is completed through the export instruction. The map collecting instruction can be input by a user through a terminal or sent by a server to the terminal.
Wherein, the first sensor data that cloud server receiving terminal sent specifically includes:
receiving multiframe original sensor data sent by a terminal; each frame of original sensor data comprises a frame header;
judging whether the transmission of the multi-frame original sensor data is finished or not according to the frame header;
and when the transmission is finished, splicing the multi-frame original sensor data according to the frame header to obtain first sensor data.
Because the types and formats of input sensor data are different due to different sensor models, which is not favorable for the universality of the algorithm, the raw sensor data needs to be preprocessed, and the sensor data is converted into a specific data type and format, so that the consistency of the data at the input end of the algorithm is realized. Therefore, the first sensor data is uploaded to the cloud server after the raw sensor data is sorted and refined.
specifically, the cloud server can detect whether the first sensor data are uploaded in real time, and meanwhile, whether the uploading of the first sensor data is finished is monitored. After the first sensor data is uploaded, a map building command is triggered, the cloud server automatically distributes a task name with a unique identifier for a task corresponding to the map building command, different map task building request messages are automatically generated according to different task names, and therefore a map is built, and therefore the cloud server can automatically respond and trigger a point cloud map building task.
104, judging whether the data of the first sensor accords with a data qualification judgment rule or not when the current memory occupation amount is less than the preset memory occupation amount and the current map task construction number is less than a preset map task construction threshold value;
specifically, the cloud server sets a plurality of configuration parameters, including memory occupancy and task number.
The preset memory occupation amount can be understood as the memory occupation amount allowed by the server. In one specific example, the preset memory footprint may be set to 80%. The preset map task construction threshold may be understood as the number of tasks that can be simultaneously run on the cloud server, in another specific example, the preset map task construction threshold is N, where 0< N <6, and N is a positive integer.
The cloud server can judge whether the point cloud map construction task needs to wait for other tasks to finish or not by judging the memory occupation amount and the number of the simultaneously running tasks, and if so, the point cloud map construction task is executed until other tasks are finished. In a specific example, when the memory occupancy is 60%, and the number of tasks simultaneously running on the cloud server is 4, the cloud server can execute the point cloud map construction task without waiting. Therefore, the cloud point map is constructed through the cloud server, the defect of high requirement on an on-board processor when the map is constructed through the on-board processor in the existing online map construction technology can be avoided, the construction of a plurality of cloud point map tasks can be realized simultaneously, and the difficulty that a large map is difficult to process is overcome.
105, when the first sensor data accord with the data qualification judgment rule, obtaining second sensor data according to the point cloud data, the inertial navigation data, the wheel speed meter data, the GPS positioning data and the image data;
specifically, the data qualification judgment rule specifically includes whether the format of the first sensor data is a preset format and whether the quality of the first sensor data meets the requirement.
When the format of the first sensor data does not conform to the preset format, the cloud server generates a first prompt message and sends the first prompt message to the terminal so as to instruct the terminal to acquire the first sensor data again, namely, the original sensor data is rearranged according to the preset format and uploaded to the cloud server.
Furthermore, each sensor can acquire data according to a preset sampling frequency, so that whether the quality of the first sensor data meets the requirement or not is judged by checking whether the data measured by a certain sensor is missing in the first sensor data.
The point cloud data is a first data set corresponding to a first sampling frequency, each data in the first data set is provided with a first time stamp, and when the first time stamp is inconsistent with the first sampling frequency, the quality of the point cloud data is determined to be not in accordance with the requirement; and/or the inertial navigation data is a second data set corresponding to a second sampling frequency, each data in the second data set has a second timestamp, and when the second timestamp is inconsistent with the second sampling frequency, the inertial navigation data quality is determined to be not in accordance with the requirement; and/or the wheel speed meter data is a third data set corresponding to a third sampling frequency, each data in the third data set has a third time stamp, and when the third time stamp is inconsistent with the third sampling frequency, the quality of the wheel speed count data is determined to be not qualified; and/or the GPS positioning data is a fourth data set corresponding to a fourth sampling frequency, each data in the fourth data set has a fourth time stamp, and when the fourth time stamp is inconsistent with the fourth sampling frequency, the quality of the GPS positioning data is determined to be not in accordance with the requirement; and/or the image data is a fifth data set corresponding to a fifth sampling frequency, each data in the fifth data set has a fifth time stamp, and when the fifth time stamp is inconsistent with the fifth sampling frequency, the image data quality is determined to be not qualified.
And when the data quality of the first sensor does not meet the requirement, generating a second prompt message by the cloud server, and sending the second prompt message to the terminal to instruct the terminal to acquire the original sensor data again.
Therefore, only when the format of the first sensor data meets the preset format and the quality of the first sensor data meets the requirement, the cloud server executes the point cloud map construction task. The data qualification judgment rule is set, so that the quality of sensor data for constructing the point cloud map is greatly improved, the data error for constructing the point cloud map is reduced, and the quality of the constructed point cloud map is improved.
106, constructing a point cloud map according to the second sensor data; the point cloud map comprises a plurality of points, and each point has first positioning data;
specifically, the point cloud data, the acceleration information, the angular velocity information, the attitude angle information, the angular velocity information, the linear velocity information, the vehicle yaw rate information, the GPS positioning data, and the first image data of the vehicle are fused to obtain second sensor data, that is, the second sensor data is the data obtained by fusing the first sensor data. The first positioning data is positioning pose information of each point in the point cloud map calculated by a preset algorithm, wherein the positioning pose information can comprise positioning information and a posture angle.
specifically, the cloud server performs quality verification on the successfully constructed point cloud map. In order to reduce the error of map data and improve the quality of map data, a constructed point cloud map must meet certain precision conditions. Therefore, in the method, in addition to the two differential GPS measurements, Real-time kinematic (RTK) carrier-phase differential technology is combined to obtain second positioning data, when a flag bit of the RTK shows that a signal is normal, the first positioning data and the second positioning data are compared, whether a difference value between the first positioning data and the second positioning data meets a preset threshold value or not is judged, if the difference value is not greater than the preset threshold value, the accuracy of the point cloud map can be determined to meet an accuracy condition, and meanwhile, centimeter-level positioning results are given, so that the requirement for building the high-accuracy point cloud map is met.
specifically, according to the timestamp, the inertial navigation data and the wheel speed meter data are aligned in time, and a certain preset length is selected to generate a local path. In a specific example, the preset length may be 10 m. If two aligned local paths (curves) are too different, the two curves are different, and if the two curves are in the same coordinate system, one of the curves is distorted.
specifically, smoothness means that a connecting line of points corresponding to a plurality of first positioning data in a constructed point cloud map should be a smooth curve, if a certain point jumps, a jagged connecting line appears, and therefore whether the point cloud map meets the smoothness condition can be judged by judging whether the connecting line among the plurality of first positioning data jumps or not.
specifically, the distance between any two adjacent points corresponding to the first positioning data among the plurality of first positioning data in the point cloud map has a preset distance threshold, so that the distance between the adjacent points in the point cloud map is compared with the preset distance threshold, and when the distance is not greater than the preset distance threshold, the continuity of the points is considered to be good, and the point cloud map meets the requirement of the continuity. In a specific example, the preset distance threshold is 1m, and if the distance between two points is 2m, it can be determined that the continuity of the point cloud map does not meet the condition.
specifically, the integrity condition refers to determining whether points in the point cloud map are intact, where the points include, but are not limited to, functional points, traffic light identification data, and boundary information. The functional points refer to preset points on the point cloud map. For example, in a point cloud map on the user terminal, the selected point. The traffic light identification data refers to the position of a traffic light, such as the coordinate of the traffic light on a point cloud map, which may be the position of the central point of the light panel frame.
In one example, when the boundary information is judged to be complete, the number of track points on the boundary can be calculated according to the frequency of the sensor and the acquisition time of the sensor, and whether the boundary track data in the generated point cloud map is consistent with the calculated number of the track points on the boundary or not is checked. And when the boundary track data in the generated point cloud map is consistent with the calculated number of track points on the boundary, determining that the boundary information meets the complete condition. And when the functional points, the traffic light identification data and the boundary information all meet the complete conditions, determining that the point cloud map meets the complete conditions.
And 112, when the point cloud map meets the precision condition, the distortion condition, the smoothness condition, the continuity condition and the integrity condition, determining that the point cloud map passes the verification.
In a preferred embodiment, when the point cloud map verification fails, for example, the point cloud map does not meet the precision condition, the distortion condition, the smoothness condition and the continuity condition, the cloud server automatically modifies specified parameters required for point cloud map construction, such as positioning deviation, positioning state and the like, and performs point cloud map construction again with the new parameters, and then performs point cloud map quality verification again.
And if the point cloud map does not meet the complete condition, the cloud server gives a specific data missing item and prompts the terminal to collect and upload the original data again. Then, step 101-.
Through verification of the point cloud map, errors in the constructed point cloud map are reduced, and therefore the quality of the point cloud map is improved.
Further, the present application may further include: and when the point cloud map passes the verification, storing the sensor data corresponding to the point cloud map so as to subsequently splice the stored sensor data corresponding to the plurality of areas to obtain the point cloud map with a larger block.
According to the high-precision point cloud map construction method provided by the embodiment of the invention, the cloud server automatically responds and triggers the point cloud map construction, so that the construction of a plurality of point cloud map tasks can be realized at the same time, and the difficulty that a large map is difficult to process is overcome; when the first sensor data meet the data qualification judgment rule, the point cloud map is constructed, and the integrity, precision, distortion, smoothness and continuity of the point cloud map are verified, so that the error of the constructed point cloud map is reduced, the requirement of the point cloud map on high precision construction is met, and the quality of the point cloud map is improved.
The second embodiment of the present invention provides an apparatus, which includes a memory and a processor, where the memory is used for storing a program, and the processor is used for executing the method provided by the first embodiment of the present invention.
A third embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the method provided in the first embodiment of the present invention is implemented.
Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, a software module executed by a processor, or a combination of the two. The software module may be disposed in a random access memory (RA high-precision point cloud map construction method), a memory, a read-only memory (RO high-precision point cloud map construction method), an electrically programmable RO high-precision point cloud map construction method, an electrically erasable programmable RO high-precision point cloud map construction method, a register, a hard disk, a removable disk, a CD-RO high-precision point cloud map construction method, a power system control method, or any other form of storage medium known in the art.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (9)
1. A high-precision point cloud map construction method is characterized by comprising the following steps:
receiving first sensor data sent by a terminal; the first sensor data comprises point cloud data, inertial navigation data, wheel speed meter data, GPS positioning data and image data;
after receiving the first sensor data, generating a map task construction request message for constructing a map;
acquiring the current memory occupation amount and the current map task construction number according to the map task construction request message;
when the current memory occupation amount is smaller than a preset memory occupation amount and the current map task construction number is smaller than a preset map task construction threshold value, judging whether the first sensor data accords with a data qualification judgment rule;
when the first sensor data accord with a data qualification judgment rule, second sensor data are obtained according to the point cloud data, the inertial navigation data, the wheel speed meter data, the GPS positioning data and the image data;
constructing a point cloud map according to the second sensor data; the point cloud map comprises a plurality of points, each of the points having first positioning data;
verifying the point cloud map, comparing the first positioning data with second positioning data acquired by a real-time dynamic carrier phase differential technology (RTK) when a flag bit of the RTK represents that a signal is normal, and determining that the point cloud map meets a precision condition when a difference value between the first positioning data and the second positioning data is not greater than a preset threshold value;
generating a local path with a preset length according to the inertial navigation data and the wheel speed meter data, comparing the local path with a local path in a point cloud map with the same preset length, and determining that the point cloud map meets a distortion condition when the difference value of the curvature of each point of the local path and the local path in the point cloud map with the same preset length is not greater than a preset difference value threshold;
judging whether a connecting line between a plurality of first positioning data in the point cloud map has jump or not, and determining that the point cloud map meets a smoothness condition when the jump does not exist;
judging whether the distance between any two adjacent points corresponding to the first positioning data among the first positioning data in the point cloud map is not greater than a preset distance threshold, and when the distance is not greater than the preset distance threshold, determining that the point cloud map meets a continuity condition;
judging whether the point is complete or not, and determining that the point cloud map meets the complete condition when the point is complete;
and when the point cloud map meets the precision condition, the distortion condition, the smoothness condition, the continuity condition and the integrity condition, determining that the point cloud map passes the verification.
2. The method for constructing a high-precision point cloud map according to claim 1, wherein the first sensor data sent by the receiving terminal specifically comprises:
receiving multiframe original sensor data sent by a terminal; each frame of the raw sensor data comprises a frame header;
judging whether the transmission of the multi-frame original sensor data is finished or not according to the frame header;
and when the transmission is finished, splicing the multi-frame original sensor data according to the frame header to obtain first sensor data.
3. The method for constructing the point cloud map with high precision according to claim 1, wherein the receiving terminal sends the first sensor data which comprises:
the method comprises the following steps that a sensor receives a map acquisition instruction sent by a terminal, and data are acquired according to a preset route and a preset mode according to the map acquisition instruction;
and when the data acquisition is finished, the terminal indicates that the data acquisition is finished by exporting the instruction.
4. The method for constructing a high-precision point cloud map according to claim 1, wherein the step of judging whether the first sensor data meets a data qualification judgment rule specifically comprises the steps of:
judging whether the format of the first sensor data is a preset format or not;
and when the format of the first sensor data does not conform to the preset format, generating a first prompt message for indicating to reacquire the first sensor data, and sending the first prompt message to a terminal.
5. The method for constructing a high-precision point cloud map according to claim 1, wherein the step of judging whether the first sensor data meets a data qualification judgment rule specifically comprises the steps of:
judging whether the data quality of the first sensor meets the requirement or not;
and when the quality of the first sensor data does not meet the requirement, generating a second prompt message for indicating to reacquire the original sensor data, and sending the second prompt message to the terminal.
6. The method of claim 1, wherein the point cloud data is a first data set corresponding to a first sampling frequency, each data in the first data set having a first time stamp, and wherein the point cloud data quality is determined to be unsatisfactory when the first time stamp is inconsistent with the first sampling frequency; and/or the presence of a gas in the gas,
the inertial navigation data is a second data set corresponding to a second sampling frequency, each data in the second data set has a second timestamp, and when the second timestamp is inconsistent with the second sampling frequency, the inertial navigation data quality is determined to be not in accordance with the requirement; and/or the presence of a gas in the gas,
the wheel speed meter data is a third data set corresponding to a third sampling frequency, each data in the third data set is provided with a third time stamp, and when the third time stamp is inconsistent with the third sampling frequency, the quality of the wheel speed meter data is determined to be not qualified; and/or the presence of a gas in the gas,
the GPS positioning data is a fourth data set corresponding to a fourth sampling frequency, each data in the fourth data set has a fourth time stamp, and when the fourth time stamp is inconsistent with the fourth sampling frequency, the quality of the GPS positioning data is determined to be not in accordance with the requirement; and/or the presence of a gas in the gas,
the image data is a fifth data set corresponding to a fifth sampling frequency, each data in the fifth data set has a fifth time stamp, and when the fifth time stamp is inconsistent with the fifth sampling frequency, the image data quality is determined to be not satisfactory.
7. The method for constructing a high-precision point cloud map according to claim 1, wherein second sensor data is obtained according to the point cloud data, inertial navigation data, wheel speed meter data, GPS positioning data and image data;
the inertial navigation data comprise acceleration information, angular velocity information and attitude angle information of the vehicle;
the wheel speed count data comprises angular velocity information, linear velocity information and vehicle yaw rate information of the left wheel and the right wheel of the vehicle;
and fusing the point cloud data, the acceleration information, the angular speed information and the attitude angle information of the vehicle, the angular speed information, the linear speed information and the vehicle yaw rate information of the left wheel and the right wheel of the vehicle, the GPS positioning data and the first image data to obtain second sensor data.
8. An apparatus, comprising a memory for storing a program and a processor for performing the method of any one of claims 1-7.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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