CN114754782A - Map construction method and device, electronic equipment and computer readable storage medium - Google Patents
Map construction method and device, electronic equipment and computer readable storage medium Download PDFInfo
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- 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
- G01C21/1652—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 with ranging devices, e.g. LIDAR or RADAR
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- G—PHYSICS
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- 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
- G01C21/183—Compensation of inertial measurements, e.g. for temperature effects
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- 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
- G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
- G01C21/3804—Creation or updating of map data
- G01C21/3833—Creation or updating of map data characterised by the source of data
- G01C21/3841—Data obtained from two or more sources, e.g. probe vehicles
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- 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
- G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
- G01C21/3804—Creation or updating of map data
- G01C21/3859—Differential updating map data
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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
- G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
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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
- G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
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Abstract
The application discloses a map construction method, a map construction device, electronic equipment and a computer readable storage medium, wherein the method comprises the following steps: acquiring point cloud data acquired by a laser radar and corresponding fusion positioning data, wherein the fusion positioning data comprises a global absolute position corresponding to the point cloud data; compensating the point cloud data by using the fusion positioning data to obtain compensated point cloud data; determining the pose of the compensated point cloud data, and optimizing the pose of the compensated point cloud data by using the global absolute position corresponding to the point cloud data to obtain the pose of the optimized point cloud data; and splicing the compensated point cloud data according to the pose of the optimized point cloud data to obtain a global point cloud map. According to the method, the point cloud data are compensated based on the multi-sensor data fusion result, and the global absolute position observed quantity is fused by using the pose optimization technology, so that a high-precision global point cloud positioning layer is established, and a powerful support is provided for an automatic driving positioning system.
Description
Technical Field
The present application relates to the field of automatic driving technologies, and in particular, to a map construction method and apparatus, an electronic device, and a computer-readable storage medium.
Background
The current automatic driving positioning System generally uses data collected by a plurality of sensors to perform information fusion, such as an IMU (Inertial Measurement Unit), a GNSS (Global Navigation Satellite System), a wheel speed odometer, a laser radar, and the like, so as to realize high-precision positioning of a vehicle.
Taking the GNSS as an example, the GNSS can provide global absolute position observations under normal conditions, but in a place where the GNSS fails, due to lack of the absolute position observations, the positioning result may drift, which may have a great influence on the planning control, safety, and the like of the autonomous vehicle. In contrast, one solution is to match a point cloud positioning layer, which is prepared in advance and stores absolute position information such as latitude and longitude or UTM (Universal Transverse register Grid System) coordinates, with point cloud data scanned by a laser radar in real time, so as to realize positioning and further solve the problem of absolute positioning information loss caused by GNSS failure.
However, since the range and the area of the autonomous vehicle are generally large, it is important to create a point cloud map that can cover a large scene and has high accuracy.
Disclosure of Invention
The embodiment of the application provides a map construction method and device, electronic equipment and a computer readable storage medium, so that a high-precision point cloud map covering a large scene is provided, and the positioning precision and the positioning stability of an automatic driving vehicle are further improved.
The embodiment of the application adopts the following technical scheme:
in a first aspect, an embodiment of the present application provides a map construction method, where the method includes:
acquiring point cloud data acquired by a laser radar and corresponding fusion positioning data, wherein the fusion positioning data comprises a global absolute position corresponding to the point cloud data;
compensating the point cloud data by using the fusion positioning data to obtain compensated point cloud data;
determining the pose of the compensated point cloud data, and optimizing the pose of the compensated point cloud data by using the global absolute position corresponding to the point cloud data to obtain the pose of the optimized point cloud data;
and splicing the compensated point cloud data according to the pose of the optimized point cloud data to obtain a global point cloud map.
Optionally, the fused positioning data includes an original angle of the IMU inertial measurement unit and a fused speed, and the using the fused positioning data to compensate the point cloud data includes:
performing angle compensation on the point cloud data by using an original angle of the IMU inertial measurement unit;
and determining a fused position according to the fused speed, and performing position compensation on the point cloud data by using the fused position.
Optionally, the determining the pose of the compensated point cloud data comprises:
performing down-sampling on the compensated point cloud data to obtain down-sampled point cloud data;
if the point cloud data after the downsampling is first frame point cloud data, acquiring a global pose corresponding to the first frame point cloud data, and taking the global pose as the pose of the point cloud data after the downsampling;
and if the point cloud data after the down-sampling is not the first frame of point cloud data, determining the pose of the point cloud data after the down-sampling through a local point cloud map.
Optionally, the determining, through the local point cloud map, the pose of the downsampled point cloud data includes:
Acquiring the local point cloud map;
carrying out down-sampling on the local point cloud map to obtain a down-sampled local point cloud map;
and matching the point cloud data subjected to the down-sampling with the local point cloud map subjected to the down-sampling by utilizing a preset matching algorithm to obtain the pose of the point cloud data subjected to the down-sampling.
Optionally, after down-sampling the compensated point cloud data to obtain down-sampled point cloud data, the method further includes:
if the point cloud data after the down-sampling is the first frame of point cloud data, directly taking the point cloud data after the down-sampling as key frame point cloud data to be stored in an external memory and the local point cloud map;
and if the point cloud data after the down-sampling is not the first frame of point cloud data, determining whether the point cloud data after the down-sampling is key frame point cloud data or not according to the distance between the point cloud data after the down-sampling and the corresponding previous frame of point cloud data.
Optionally, the determining whether the down-sampled point cloud data is the key frame point cloud data according to the distance between the down-sampled point cloud data and the corresponding previous frame point cloud data includes:
Acquiring the pose of the point cloud data after the down sampling and the corresponding pose of the point cloud data of the previous frame;
determining the distance between the down-sampled point cloud data and the previous frame of point cloud data according to the pose of the down-sampled point cloud data and the pose of the previous frame of point cloud data;
if the distance between the point cloud data after the down-sampling and the previous frame of point cloud data reaches a preset distance threshold, determining the point cloud data after the down-sampling as key frame point cloud data, and storing the key frame point cloud data in the external memory and the local point cloud map;
otherwise, determining the point cloud data after the down-sampling as non-key frame point cloud data, and abandoning the point cloud data after the down-sampling.
Optionally, the compensated point cloud data is key frame point cloud data, and the splicing of the compensated point cloud data according to the pose of the optimized point cloud data includes:
loading the keyframe point cloud data from external memory;
down-sampling the key frame point cloud data to obtain down-sampled key frame point cloud data;
and splicing the key frame point cloud data after the down sampling based on the pose of the optimized point cloud data corresponding to the key frame point cloud data to obtain the global point cloud map.
In a second aspect, an embodiment of the present application further provides a map building apparatus, where the apparatus includes:
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring point cloud data acquired by a laser radar and corresponding fusion positioning data, and the fusion positioning data comprises a global absolute position corresponding to the point cloud data;
the compensation unit is used for compensating the point cloud data by using the fusion positioning data to obtain compensated point cloud data;
the optimization unit is used for determining the pose of the compensated point cloud data and optimizing the pose of the compensated point cloud data by using the global absolute position corresponding to the point cloud data to obtain the pose of the optimized point cloud data;
and the splicing unit is used for splicing the compensated point cloud data according to the pose of the optimized point cloud data to obtain a global point cloud map.
In a third aspect, an embodiment of the present application further provides an electronic device, including:
a processor; and
a memory arranged to store computer executable instructions that, when executed, cause the processor to perform any of the methods described above.
In a fourth aspect, embodiments of the present application further provide a computer-readable storage medium storing one or more programs that, when executed by an electronic device including a plurality of application programs, cause the electronic device to perform any of the methods described above.
The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects: according to the map construction method, point cloud data acquired by a laser radar and corresponding fusion positioning data are acquired, and the fusion positioning data comprise global absolute positions corresponding to the point cloud data; then, compensating the point cloud data by using the fusion positioning data to obtain compensated point cloud data; then, the pose of the compensated point cloud data is determined, and the pose of the compensated point cloud data is optimized by using the global absolute position corresponding to the point cloud data to obtain the pose of the optimized point cloud data; and finally, splicing the compensated point cloud data according to the pose of the optimized point cloud data to obtain a global point cloud map. The map construction method is based on the multi-sensor data fusion result to compensate point cloud data, and the pose optimization technology is used to fuse the global absolute position observed quantity, so that a high-precision global point cloud positioning layer is established, and powerful support is provided for an automatic driving positioning system.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic flow chart diagram of a map construction method in an embodiment of the present application;
FIG. 2 is a schematic diagram of an overall architecture of an automatic driving positioning system according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a process for constructing a map in the embodiment of the present application;
FIG. 4 is a schematic structural diagram of a map building apparatus according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
An embodiment of the present application provides a map building method, and as shown in fig. 1, provides a flowchart illustration of a map building method in an embodiment of the present application, where the method at least includes the following steps S110 to S140:
Step S110, point cloud data collected by a laser radar and corresponding fusion positioning data are obtained, wherein the fusion positioning data comprise global absolute positions corresponding to the point cloud data.
When the map is constructed, the point cloud data acquired by the Lidar in real time needs to be acquired first, and the fusion positioning data corresponding to the frame of point cloud data needs to be acquired, wherein the fusion positioning data can be output by combined navigation, for example, the combined navigation composed of an IMU and a GNSS can be adopted, and the observed quantity of the global absolute position of the GNSS can be specifically output.
Because the data acquisition frequency of laser radar and other sensors is different, and is also different with the frequency of combination navigation output fusion positioning data, lead to there being the problem of time synchronization, consequently here can carry out the alignment of time stamp with the integration positioning data of combination navigation output with the point cloud data of laser radar collection earlier, for example can adopt the interpolation method to realize to can obtain the integration positioning data of the corresponding moment of point cloud data of laser radar collection.
And S120, compensating the point cloud data by using the fusion positioning data to obtain compensated point cloud data.
Because the fusion positioning result output after the data fusion of the multiple sensors is more accurate and more reliable compared with the data collected by a single sensor, the fusion positioning data obtained by the steps can be utilized to carry out motion compensation on the point cloud data collected by the laser radar, so that the point cloud data after compensation is obtained.
Step S130, determining the pose of the compensated point cloud data, and optimizing the pose of the compensated point cloud data by using the global absolute position corresponding to the point cloud data to obtain the pose of the optimized point cloud data.
Because the compensated point cloud data only contains coordinate information of a series of points, in order to construct a point cloud map, the pose of the compensated point cloud data needs to be further determined, the determined pose can be regarded as an initial pose, and in order to ensure the accuracy of the point cloud map, a certain map optimization algorithm such as a GN (Gauss Newton) algorithm or an LM (Levenberg-Marquardt Algo) algorithm can be used to optimize the pose of the compensated point cloud data by combining with the global absolute position corresponding to the point cloud data, so that the optimized global pose is obtained.
And step S140, splicing the compensated point cloud data according to the pose of the optimized point cloud data to obtain a global point cloud map.
The compensated point cloud data is located under a laser radar coordinate system, and the pose of the optimized point cloud data can be regarded as a relative transformation relation from the laser radar coordinate system to a global coordinate system such as a UTM coordinate system, so that the point cloud data compensated by multiple frames can be subjected to point cloud splicing based on the pose of each frame of optimized point cloud data, a point cloud map with the same global situation is obtained, and data support is provided for real-time positioning of an automatic driving vehicle.
The map construction method is based on the multi-sensor data fusion result to compensate point cloud data, and the pose optimization technology is used to fuse the global absolute position observed quantity, so that a high-precision global point cloud positioning layer is established, and powerful support is provided for an automatic driving positioning system.
In an embodiment of the present application, the fused positioning data includes an original angle of an IMU inertial measurement unit and a fused speed, and the compensating the point cloud data by using the fused positioning data includes: performing angle compensation on the point cloud data by using an original angle of the IMU inertial measurement unit; and determining a fused position according to the fused speed, and performing position compensation on the point cloud data by using the fused position.
As shown in fig. 2, a schematic diagram of an overall architecture of an automatic driving location system in the embodiment of the present application is provided. The automatic driving positioning system comprises a plurality of sensors such as a laser radar Lidar, an IMU and a GNSS, wherein the combined navigation based on the IMU and the GNSS can output fusion positioning data through Kalman filtering (Kalman), the fusion positioning data can also comprise original angle information output by the IMU besides the global absolute Position provided by the GNSS, and the original angle information output by the IMU can be used for carrying out angle compensation on point cloud data of the laser radar Lidar.
In addition, the fusion positioning data can also comprise the fused speed velocity, and the time interval between two adjacent frames can be determined, so that the fused position can be calculated according to the fused speed information and the time interval, and then the position compensation can be carried out on the point cloud data of the Lidar by utilizing the fused position.
Based on the method, the compensated point cloud data is used for SLAM (synchronous positioning And Mapping) Mapping of a Multi-Sensor (Multi-Sensor), so that a final point cloud positioning map layer (Localization map) is obtained.
In one embodiment of the application, the determining the pose of the compensated point cloud data comprises: performing down-sampling on the compensated point cloud data to obtain down-sampled point cloud data; if the point cloud data after the down-sampling is first frame point cloud data, acquiring a global pose corresponding to the first frame point cloud data, and taking the global pose as the pose of the point cloud data after the down-sampling; and if the point cloud data after the down-sampling is not the first frame of point cloud data, determining the pose of the point cloud data after the down-sampling through a local point cloud map.
The Voxel filtering uses a voxelization grid method to perform downsampling on the point cloud data, so that the number of points can be reduced, the point cloud data can be reduced, the shape characteristics of the point cloud can be kept, and the method is very practical in improving the algorithm speed of registration, curved surface reconstruction, shape identification and the like. According to the embodiment of the application, when the pose of the compensated point cloud data is determined, the compensated point cloud data can be subjected to Voxel filtering, namely down-sampling processing, so that the storage space of the point cloud data is saved, and the point cloud matching efficiency is improved.
The point cloud data acquired by the laser radar is real-time, so that after the point cloud data after the down-sampling is obtained, whether the point cloud data after the down-sampling is the first frame of point cloud data currently acquired by the laser radar can be further judged, the first condition is that the point cloud data after the down-sampling is the first frame of point cloud data, and because no historical point cloud data can be used for reference, the global pose corresponding to the first frame of point cloud data can be directly acquired at this time and is used as the pose of the point cloud data after the down-sampling. The second case is that the point cloud data after down-sampling is not the first frame point cloud data, which indicates that historical point cloud data exists at the moment, so that the pose of the point cloud data after down-sampling can be determined through a local point cloud map formed by a certain number of frames of historical point cloud data.
In an embodiment of the application, the determining, through the local point cloud map, the pose of the downsampled point cloud data includes: acquiring the local point cloud map; carrying out down-sampling on the local point cloud map to obtain a down-sampled local point cloud map; and matching the point cloud data subjected to the down-sampling with the local point cloud map subjected to the down-sampling by utilizing a preset matching algorithm to obtain the pose of the point cloud data subjected to the down-sampling.
For the second case, when determining the pose of the point cloud data after downsampling, a local point cloud map may be obtained first, where the local point cloud map may be regarded as being formed by a certain number of frames of point cloud data, for example, a local point cloud map may be set to store 20 frames of point cloud data at most, and when 21 st frame of point cloud data enters, the earliest point cloud data stored in the local point cloud map is deleted, that is, it is ensured that the latest point cloud data is always stored in the local point cloud map.
In order to further improve the efficiency of point cloud matching, the obtained local point cloud map can be subjected to Voxel downsampling to obtain a downsampled local point cloud map, and then the downsampled point cloud data and the downsampled local point cloud map are matched by using a preset point cloud matching algorithm to form a laser odometer so as to obtain the pose of the downsampled point cloud data.
The preset matching algorithm may be implemented by, for example, an NDT (Normal Distribution Transform) algorithm, an LOAM (laser odometer And Mapping) algorithm, or an ICP (Iterative Closest Point) matching algorithm, And specifically, which matching algorithm is adopted may be flexibly selected by a person skilled in the art according to actual requirements, And is not specifically limited herein.
According to the method and the device, the strategy of matching the point cloud data after down-sampling with the corresponding local point cloud map is adopted, and compared with the mode of matching the previous frame of point cloud data corresponding to the point cloud data after down-sampling, the precision of point cloud matching can be improved, so that a more accurate and smooth point cloud map can be established.
In an embodiment of the application, after down-sampling the compensated point cloud data to obtain down-sampled point cloud data, the method further includes: if the point cloud data after the down-sampling is the first frame of point cloud data, directly taking the point cloud data after the down-sampling as key frame point cloud data to be stored in an external memory and the local point cloud map; and if the down-sampled point cloud data is not the first frame point cloud data, determining whether the down-sampled point cloud data is the key frame point cloud data or not according to the distance between the down-sampled point cloud data and the corresponding previous frame point cloud data.
Because the data acquisition frequency of the laser radar is generally 0.1 s/frame, a large amount of point cloud data can be accumulated in a large-scale map construction scene, and therefore in order to reduce the memory and calculation power required by point cloud map manufacturing, the embodiment of the application can utilize the key frame point cloud data to construct the point cloud map, and the key frame point cloud data can be regarded as being selected from a large amount of continuous point cloud data according to a certain strategy, namely, original multi-frame point cloud data is deleted to a certain extent, so that the subsequent processing efficiency is improved.
Based on this, the key frame point cloud data needs to be determined in the embodiment of the application, specifically, whether the point cloud data after down-sampling is the first frame point cloud data currently acquired by the laser radar can be determined firstly, if the point cloud data is the first frame point cloud data, the point cloud data after down-sampling can be directly stored as the key frame point cloud data, and if the point cloud data is not the first frame point cloud data, whether the point cloud data can be used as the key frame point cloud data or not is determined by determining the distance between the point cloud data and the previous frame point cloud data and storing the point cloud data.
The storage comprises two dimensions, one dimension is stored in the local point cloud map, and the dynamic update is equivalent to the dynamic update of the local point cloud map, so that the key frame point cloud data with a certain number of latest frames are always stored in the local point cloud map, and a basis is provided for point cloud matching. The other dimension is stored in an external storage, and each frame of key frame point cloud data is stored in the external storage, so that the memory occupation can be greatly reduced, the program operation efficiency can be improved, and the condition of point cloud map construction in a large scene is met.
In an embodiment of the application, the determining whether the down-sampled point cloud data is the key frame point cloud data according to the distance between the down-sampled point cloud data and the corresponding previous frame point cloud data includes: acquiring the pose of the point cloud data after the down sampling and the corresponding pose of the point cloud data of the previous frame; determining the distance between the down-sampled point cloud data and the previous frame of point cloud data according to the pose of the down-sampled point cloud data and the pose of the previous frame of point cloud data; if the distance between the down-sampled point cloud data and the previous frame of point cloud data reaches a preset distance threshold, determining that the down-sampled point cloud data is key frame point cloud data and storing the key frame point cloud data into the external memory and the local point cloud map; otherwise, determining the point cloud data after the down-sampling as non-key frame point cloud data, and abandoning the point cloud data after the down-sampling.
If the down-sampled point cloud data is not the first frame of point cloud data, it is required to determine whether the down-sampled point cloud data is the key frame point cloud data or not by the distance between the down-sampled point cloud data and the corresponding previous frame of point cloud data. Specifically, the pose of the down-sampled point cloud data and the pose of the corresponding previous frame of point cloud data may be obtained first, and the pose of the down-sampled point cloud data may be obtained by matching the down-sampled point cloud data with the down-sampled local point cloud map by using a preset matching algorithm in the foregoing embodiment.
Because the pose contains the position information, the distance between the position of the point cloud data after the down-sampling and the position of the point cloud data of the previous frame corresponding to the position of the point cloud data of the previous frame can be calculated, then the distance is compared with a preset distance threshold, if the distance reaches the preset distance threshold, the point cloud data after the down-sampling can be used as key frame point cloud data to be stored, otherwise, the frame point cloud data is directly abandoned.
The preset distance threshold can be flexibly set according to actual requirements, for example, the preset distance threshold can be set to be 2m, if the distance between the point cloud data after down-sampling and the point cloud data of the previous frame is less than 2m, the point cloud data is not used as key frame point cloud data, and otherwise, the point cloud data is used as key frame point cloud data to be stored.
In an embodiment of the application, the compensated point cloud data is key frame point cloud data, and the splicing the compensated point cloud data according to the pose of the optimized point cloud data includes: loading the key frame point cloud data from an external memory; down-sampling the key frame point cloud data to obtain down-sampled key frame point cloud data; and splicing the key frame point cloud data after the down sampling based on the position and the posture of the optimized point cloud data corresponding to the key frame point cloud data to obtain the global point cloud map.
As described above, the point cloud data used for constructing the point cloud map in the embodiment of the present application are all key frame point cloud data, and the key frame point cloud data are all stored in the external memory, so that when the map is constructed, a plurality of frames of key frame point cloud data stored in the external memory can be loaded first, then, Voxel down-sampling is performed on the key frame point cloud data, and finally, point cloud splicing is performed on the down-sampled key frame point cloud data by using the pose of the optimized point cloud data corresponding to the key frame point cloud data, so as to obtain a global point cloud positioning layer, that is, a global point cloud map, which can be used for real-time positioning of an automatic driving vehicle.
According to the method and the device, the downsampling processing is carried out again in the point cloud data splicing stage, the internal memory occupied by each frame of point cloud data after downsampling is further reduced, the point cloud data splicing speed is further improved, and the requirement for building a point cloud map in a large scene is met.
In one embodiment of the present application, the loading the keyframe point cloud data from external memory comprises: acquiring a map storage request; and loading the key frame point cloud data from the external memory according to the map storage request.
When the key frame point cloud data are loaded from the external storage, the strategy of executing the map storage command externally can be adopted, namely, the logic of map storage can be carried out only after the map storage command is executed externally, so that when a program runs, the map storage does not need to be carried out in real time, the running efficiency of the map building program is ensured, and the memory occupation is reduced.
In order to facilitate understanding of the embodiments of the present application, as shown in fig. 3, a schematic diagram of a map construction process in the embodiments of the present application is provided. Firstly, point cloud data acquired by a laser radar Lidar, corresponding IMU original angle data are acquired to perform angle compensation on the point cloud data, and position compensation is performed on the point cloud data based on the fused speed, so that compensated point cloud data are obtained.
And then, performing Voxel downsampling processing on the compensated point cloud data to obtain downsampled point cloud data. And further judging whether the point cloud data after the down-sampling is the current first frame point cloud data. If yes, initializing the down-sampled point cloud data by using the corresponding global position of the GNSS, and simultaneously marking the down-sampled point cloud data as key frame point cloud data to be stored in an external memory and a local point cloud map. If not, further acquiring a local point cloud map, performing Voxel down-sampling processing on the local point cloud map to obtain a down-sampled local point cloud map, performing point cloud matching on the down-sampled point cloud data and the down-sampled local point cloud map, and further obtaining the pose of the down-sampled point cloud data.
And then, according to the pose of the point cloud data after the down sampling, determining whether the distance between the point cloud data and the previous frame of point cloud data is larger than a preset distance threshold value, such as 2m, if so, marking the point cloud data after the down sampling as key frame point cloud data, storing the key frame point cloud data into an external memory and a local point cloud map, and if not, directly abandoning the key frame point cloud data.
And then, performing global optimization on the poses of all the key frame point cloud data by using a preset map optimization algorithm to obtain the poses of the optimized point cloud data.
And finally, loading all key frame point cloud data from an external memory based on a map storage command, performing Voxel down-sampling processing on all key frame point cloud data again, and performing point cloud splicing on all down-sampled key frame point cloud data by using the pose of the optimized point cloud data to obtain a global point cloud map.
According to the map construction method, memory optimization and computational power optimization are carried out on the construction process of the point cloud map, the high-precision global point cloud map under a large scene can be established at one time, a foundation is provided for matching and positioning of an automatic driving positioning system under the large scene by using a laser radar, and the precision and the stability of automatic driving positioning are guaranteed.
An embodiment of the present application further provides a map building apparatus 400, as shown in fig. 4, which provides a schematic structural diagram of the map building apparatus in the embodiment of the present application, where the apparatus 400 includes: an obtaining unit 410, a compensation unit 420, an optimization unit 430, and a splicing unit 440, wherein:
the acquiring unit 410 is configured to acquire point cloud data acquired by a laser radar and corresponding fusion positioning data, where the fusion positioning data includes a global absolute position corresponding to the point cloud data;
a compensation unit 420, configured to compensate the point cloud data by using the fusion positioning data to obtain compensated point cloud data;
the optimizing unit 430 is configured to determine a pose of the compensated point cloud data, and optimize the pose of the compensated point cloud data by using the global absolute position corresponding to the point cloud data to obtain an optimized pose of the point cloud data;
and the splicing unit 440 is configured to splice the compensated point cloud data according to the pose of the optimized point cloud data, so as to obtain a global point cloud map.
In an embodiment of the present application, the fused positioning data includes an original angle and a fused speed of the IMU inertial measurement unit, and the compensation unit 420 is specifically configured to: carrying out angle compensation on the point cloud data by using the original angle of the IMU inertial measurement unit; and determining a fused position according to the fused speed, and performing position compensation on the point cloud data by using the fused position.
In an embodiment of the present application, the optimizing unit 430 is specifically configured to: performing down-sampling on the compensated point cloud data to obtain down-sampled point cloud data; if the point cloud data after the downsampling is first frame point cloud data, acquiring a global pose corresponding to the first frame point cloud data, and taking the global pose as the pose of the point cloud data after the downsampling; and if the point cloud data after the down-sampling is not the first frame of point cloud data, determining the pose of the point cloud data after the down-sampling through a local point cloud map.
In an embodiment of the present application, the optimizing unit 430 is specifically configured to: acquiring the local point cloud map; carrying out down-sampling on the local point cloud map to obtain a down-sampled local point cloud map; and matching the point cloud data subjected to the down-sampling with the local point cloud map subjected to the down-sampling by utilizing a preset matching algorithm to obtain the pose of the point cloud data subjected to the down-sampling.
In one embodiment of the present application, the apparatus further comprises: the storage unit is used for directly storing the down-sampled point cloud data serving as key frame point cloud data into an external memory and the local point cloud map if the down-sampled point cloud data is the first frame point cloud data; and the determining unit is used for determining whether the down-sampled point cloud data is key frame point cloud data or not according to the distance between the down-sampled point cloud data and the corresponding previous frame point cloud data if the down-sampled point cloud data is not the first frame point cloud data.
In an embodiment of the application, the determining unit is specifically configured to: acquiring the pose of the point cloud data after the down sampling and the corresponding pose of the point cloud data of the previous frame; determining the distance between the down-sampled point cloud data and the previous frame of point cloud data according to the pose of the down-sampled point cloud data and the pose of the previous frame of point cloud data; if the distance between the down-sampled point cloud data and the previous frame of point cloud data reaches a preset distance threshold, determining that the down-sampled point cloud data is key frame point cloud data and storing the key frame point cloud data into the external memory and the local point cloud map; otherwise, determining the point cloud data after the down-sampling as non-key frame point cloud data, and abandoning the point cloud data after the down-sampling.
In an embodiment of the application, the compensated point cloud data is key frame point cloud data, and the stitching unit 440 is specifically configured to: loading the key frame point cloud data from an external memory; down-sampling the key frame point cloud data to obtain down-sampled key frame point cloud data; and splicing the key frame point cloud data after the down sampling based on the position and the posture of the optimized point cloud data corresponding to the key frame point cloud data to obtain the global point cloud map.
It can be understood that the map building apparatus can implement the steps of the map building method provided in the foregoing embodiment, and the related explanations about the map building method are all applicable to the map building apparatus, and are not described herein again.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application. Referring to fig. 5, at a hardware level, the electronic device includes a processor, and optionally further includes an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, the network interface, and the memory may be connected to each other by an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 5, but this does not indicate only one bus or one type of bus.
And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.
The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to form the mapping device on a logic level. The processor is used for executing the program stored in the memory and is specifically used for executing the following operations:
acquiring point cloud data acquired by a laser radar and corresponding fusion positioning data, wherein the fusion positioning data comprises a global absolute position corresponding to the point cloud data;
compensating the point cloud data by using the fusion positioning data to obtain compensated point cloud data;
determining the pose of the compensated point cloud data, and optimizing the pose of the compensated point cloud data by using the global absolute position corresponding to the point cloud data to obtain the pose of the optimized point cloud data;
and splicing the compensated point cloud data according to the pose of the optimized point cloud data to obtain a global point cloud map.
The method performed by the map building apparatus disclosed in the embodiment of fig. 1 of the present application may be applied to or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or by instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, etc. as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and combines hardware thereof to complete the steps of the method.
The electronic device may further execute the method executed by the map building apparatus in fig. 1, and implement the function of the map building apparatus in the embodiment shown in fig. 1, which is not described herein again in this embodiment of the present application.
An embodiment of the present application further provides a computer-readable storage medium storing one or more programs, where the one or more programs include instructions, which, when executed by an electronic device including multiple application programs, enable the electronic device to perform the method performed by the map building apparatus in the embodiment shown in fig. 1, and are specifically configured to perform:
acquiring point cloud data acquired by a laser radar and corresponding fusion positioning data, wherein the fusion positioning data comprises a global absolute position corresponding to the point cloud data;
compensating the point cloud data by using the fusion positioning data to obtain compensated point cloud data;
determining the pose of the compensated point cloud data, and optimizing the pose of the compensated point cloud data by using the global absolute position corresponding to the point cloud data to obtain the pose of the optimized point cloud data;
and splicing the compensated point cloud data according to the pose of the optimized point cloud data to obtain a global point cloud map.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both permanent and non-permanent, removable and non-removable media, may implement the information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art to which the present application pertains. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A map construction method, wherein the method comprises:
acquiring point cloud data acquired by a laser radar and corresponding fusion positioning data, wherein the fusion positioning data comprises a global absolute position corresponding to the point cloud data;
compensating the point cloud data by using the fusion positioning data to obtain compensated point cloud data;
Determining the pose of the compensated point cloud data, and optimizing the pose of the compensated point cloud data by using the global absolute position corresponding to the point cloud data to obtain the pose of the optimized point cloud data;
and splicing the compensated point cloud data according to the pose of the optimized point cloud data to obtain a global point cloud map.
2. The method of claim 1, wherein the fused positioning data comprises an original angle and a fused velocity of an Inertial Measurement Unit (IMU), and the compensating the point cloud data with the fused positioning data comprises:
performing angle compensation on the point cloud data by using an original angle of the IMU inertial measurement unit;
and determining a fused position according to the fused speed, and performing position compensation on the point cloud data by using the fused position.
3. The method of claim 1, wherein the determining the pose of the compensated point cloud data comprises:
performing down-sampling on the compensated point cloud data to obtain down-sampled point cloud data;
if the point cloud data after the down-sampling is first frame point cloud data, acquiring a global pose corresponding to the first frame point cloud data, and taking the global pose as the pose of the point cloud data after the down-sampling;
And if the point cloud data after the down-sampling is not the first frame of point cloud data, determining the pose of the point cloud data after the down-sampling through a local point cloud map.
4. The method of claim 3, wherein the determining the pose of the downsampled point cloud data from the local point cloud map comprises:
acquiring the local point cloud map;
carrying out down-sampling on the local point cloud map to obtain a down-sampled local point cloud map;
and matching the point cloud data subjected to the down-sampling with the local point cloud map subjected to the down-sampling by utilizing a preset matching algorithm to obtain the pose of the point cloud data subjected to the down-sampling.
5. The method of claim 3, wherein after down-sampling the compensated point cloud data to obtain down-sampled point cloud data, the method further comprises:
if the point cloud data after the down-sampling is the first frame of point cloud data, directly taking the point cloud data after the down-sampling as key frame point cloud data to be stored in an external memory and the local point cloud map;
and if the down-sampled point cloud data is not the first frame point cloud data, determining whether the down-sampled point cloud data is the key frame point cloud data or not according to the distance between the down-sampled point cloud data and the corresponding previous frame point cloud data.
6. The method of claim 5, wherein the determining whether the downsampled point cloud data is key frame point cloud data by a distance between the downsampled point cloud data and a corresponding previous frame of point cloud data comprises:
acquiring the pose of the point cloud data after the down sampling and the corresponding pose of the point cloud data of the previous frame;
determining the distance between the down-sampled point cloud data and the previous frame of point cloud data according to the pose of the down-sampled point cloud data and the pose of the previous frame of point cloud data;
if the distance between the down-sampled point cloud data and the previous frame of point cloud data reaches a preset distance threshold, determining that the down-sampled point cloud data is key frame point cloud data and storing the key frame point cloud data into the external memory and the local point cloud map;
otherwise, determining the point cloud data after the down-sampling as non-key frame point cloud data, and abandoning the point cloud data after the down-sampling.
7. The method of claim 1, wherein the compensated point cloud data is key frame point cloud data, and wherein stitching the compensated point cloud data according to the pose of the optimized point cloud data comprises:
Loading the key frame point cloud data from an external memory;
down-sampling the key frame point cloud data to obtain down-sampled key frame point cloud data;
and splicing the key frame point cloud data after the down sampling based on the position and the posture of the optimized point cloud data corresponding to the key frame point cloud data to obtain the global point cloud map.
8. A map building apparatus, wherein the apparatus comprises:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring point cloud data acquired by a laser radar and corresponding fusion positioning data, and the fusion positioning data comprises a global absolute position corresponding to the point cloud data;
the compensation unit is used for compensating the point cloud data by using the fusion positioning data to obtain compensated point cloud data;
the optimization unit is used for determining the pose of the compensated point cloud data and optimizing the pose of the compensated point cloud data by using the global absolute position corresponding to the point cloud data to obtain the pose of the optimized point cloud data;
and the splicing unit is used for splicing the compensated point cloud data according to the position and the posture of the optimized point cloud data to obtain a global point cloud map.
9. An electronic device, comprising:
a processor; and
a memory arranged to store computer executable instructions which, when executed, cause the processor to perform the method of any of claims 1 to 7.
10. A computer readable storage medium storing one or more programs which, when executed by an electronic device comprising a plurality of application programs, cause the electronic device to perform the method of any of claims 1-7.
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