CN116299542A - Method and device for constructing high-precision map, storage medium and equipment - Google Patents
Method and device for constructing high-precision map, storage medium and equipment Download PDFInfo
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Abstract
The application discloses a method, a device, a storage medium and equipment for constructing a high-precision map, and belongs to the technical field of image processing. The method comprises the following steps: acquiring a point cloud set of laser radar data of a current frame and a previous frame, wherein the point cloud set comprises ground points, corner points and face points; acquiring a point cloud distance model, and calculating the distance between the same type of point clouds in the current frame and the previous frame according to the point cloud distance model; creating an error function according to the distance, and calculating the pose of the current frame relative to the previous frame according to the error function; and creating a high-precision map according to the pose. The method and the device can not only increase the diversity of the features, but also increase the constraint of the z axis through independent processing of the ground points, relieve the problem of too fast z axis divergence, and can also decouple the estimation of the pose with 6 degrees of freedom, realize the matching success rate and precision under larger accumulated error, and shorten the calculation time.
Description
Technical Field
The present invention relates to the field of image processing technologies, and in particular, to a method and apparatus for constructing a high-precision map, a storage medium, and a device.
Background
In an automatic driving scheme based on a high-precision map, the high-precision map provides accurate priori environmental information for positioning, sensing and PnC (Planning and Control ), has the advantages of high precision, multiple elements, high freshness and the like, is an important premise for safety decision and judgment of vehicles, and is an important guarantee for unmanned vehicles to safely and efficiently complete various tasks, so that the construction of the high-precision map is an important basis and an important link of the automatic driving scheme.
There are two general methods for constructing a high-precision map: a high-precision map is created through a traditional mapping method, specifically, the pose of a vehicle is collected through high-precision inertial navigation equipment, and vehicle-mounted laser radar data is projected to generate the high-precision map. The other is to create a high-precision map by SLAM (Simultaneous Localization and Mapping ) technology, specifically, estimate the pose corresponding to each frame of laser radar data of the vehicle by adopting a registration method, eliminate errors by loop detection and global optimization, and finally generate the high-precision map.
Although the traditional mapping method can rapidly measure the pose of the vehicle, the method is easy to be interfered by the environment, and particularly under the condition of building and thick tree, the error of the pose is often too large to meet the requirement of a high-precision map. In the case that the pose cannot be acquired through high-precision inertial navigation equipment, the pose can be accurately estimated by adopting the SLAM technology. The SLAM technology comprises a front-end odometer. The front-end odometer can adopt methods such as ICP (Iterative Closest Point, iteration closest point), GICP (Generalized Iterative Closest Point, generalized iteration closest point), NDT (Normal Distribution Transform, normal distribution transformation) and the like, but the calculated amount is relatively large, and the calculation efficiency is greatly reduced along with the increase of the range of the map; thus, the feature-based method is gradually becoming the mainstream method of front-end odometer, such as LOAM (Lidar Odometry and Mapping, laser odometer and drawing), but only corner points and face points in the environment are extracted as features to estimate the pose, the constraint in the z-axis is relatively small, and the accumulated error is larger as the moving distance is farther, resulting in rapid divergence in the z-axis.
Disclosure of Invention
The application provides a method, a device, a storage medium and equipment for constructing a high-precision map, which are used for solving the problem that the z-axis diverges too fast in the high-precision map construction process. The technical scheme is as follows:
in one aspect, a method for constructing a high-precision map is provided, the method comprising:
acquiring a point cloud set of laser radar data of a current frame and a previous frame, wherein the point cloud set comprises ground points, corner points and face points;
acquiring a point cloud distance model, and calculating the distance between the same type of point clouds in the current frame and the previous frame according to the point cloud distance model;
creating an error function according to the distance, and calculating the pose of the current frame relative to the previous frame according to the error function;
and creating a high-precision map according to the pose.
In one possible implementation manner, the obtaining a point cloud distance model, calculating a distance between the current frame and the similar point clouds in the previous frame according to the point cloud distance model includes:
and when the point cloud distance model is a ground point distance model, calculating the plane distance between each ground point in the point cloud set of the current frame and the ground in the previous frame, wherein the ground is formed by the ground points in the point cloud set of the previous frame.
In one possible implementation manner, the obtaining a point cloud distance model, calculating a distance between the current frame and the similar point clouds in the previous frame according to the point cloud distance model includes:
and when the point cloud distance model is an angular point distance model, calculating the angular line distance from each angular point in the point cloud set of the current frame to the angular point of the previous frame.
In one possible implementation manner, the obtaining a point cloud distance model, calculating a distance between the current frame and the similar point clouds in the previous frame according to the point cloud distance model includes:
and when the point cloud distance model is a face point distance model, calculating the plane distance between each face point in the point cloud set of the current frame and the plane in the previous frame, wherein the plane is formed by the face points in the point cloud set of the previous frame.
In one possible implementation, the method further includes:
performing loop detection on the point cloud set of each frame of laser radar data;
if a loop exists between the mth point cloud set and the nth point cloud set, matching ground points in the mth point cloud set and the nth point cloud set to obtain a conversion value of a z-axis, a roll angle and a pitch angle between mth frame and nth frame laser radar data;
taking the conversion values of the z-axis, the roll angle and the pitch angle as initial values, and calculating the conversion values of the x-axis, the y-axis and the yaw angle between the laser radar data of the mth frame and the laser radar data of the nth frame;
and calculating a final conversion value between the m-th frame and the n-th frame of laser radar data by using the conversion values of the x-axis, the y-axis, the z-axis, the yaw angle, the roll angle and the pitch angle as initial values through an ICP algorithm.
In one possible implementation manner, the calculating the conversion values of the x-axis, the y-axis and the yaw angle between the mth frame and the nth frame of laser radar data using the conversion values of the z-axis, the roll angle and the pitch angle as initial values includes:
the transformation values of the z-axis, the roll angle and the pitch angle are used as initial values, and the corner points and the face points in the mth point cloud set and the nth point cloud set are compressed into a two-dimensional map;
and matching the two-dimensional map by using a CSM scanning matching algorithm to obtain conversion values of an x axis, a y axis and a yaw angle between the laser radar data of the mth frame and the laser radar data of the nth frame.
In one possible implementation manner, the creating a high-precision map according to the pose includes:
and creating the high-precision map according to the pose and the final transformation value.
In one aspect, there is provided a construction apparatus of a high-precision map, the apparatus comprising:
the acquisition module is used for acquiring a point cloud set of the laser radar data of the current frame and the previous frame, wherein the point cloud set comprises ground points, corner points and face points;
the acquisition module is further used for acquiring a point cloud distance model, and calculating the distance between the current frame and the similar point cloud in the previous frame according to the point cloud distance model;
the calculating module is used for creating an error function according to the distance and calculating the pose of the current frame relative to the previous frame according to the error function;
and the creation module is used for creating a high-precision map according to the pose.
In one aspect, a computer readable storage medium having stored therein at least one instruction that is loaded and executed by a processor to implement a method of constructing a high-precision map as described above is provided.
In one aspect, a computer device is provided that includes a processor and a memory having at least one instruction stored therein that is loaded and executed by the processor to implement a method of constructing a high-precision map as described above.
The beneficial effects of the technical scheme that this application provided include at least:
the point cloud in the point cloud set is divided into three categories of ground points, corner points and surface points, and then a high-precision map is created, so that the diversity of features can be increased, the constraint of a z axis can be increased through independent processing of the ground points, and the problem of too fast z axis divergence is solved.
By introducing the ground points into the loop matching link, the estimation of the pose with 6 degrees of freedom can be decoupled, the matching success rate and accuracy under the condition of larger accumulated error are realized, and the calculation time is shortened.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a method flow diagram of a method for constructing a high-precision map provided by one embodiment of the present application;
FIG. 2 is a method flow diagram of a method for constructing a high-precision map provided by one embodiment of the present application;
FIG. 3 is a block diagram of a high-precision map construction apparatus according to still another embodiment of the present application;
fig. 4 is a block diagram of a construction apparatus for a high-precision map according to still another embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
Referring to fig. 1, a method flowchart of a method for constructing a high-precision map according to an embodiment of the present application is shown, where the method for constructing a high-precision map may be applied to a computer device. The method for constructing the high-precision map can comprise the following steps:
The laser radar is arranged in an automatically driven vehicle, the laser radar can periodically collect surrounding environments to obtain laser radar data, and a point cloud set is generated according to each frame of laser radar data.
After the computer device acquires the point cloud set, the point cloud is preprocessed, and the point cloud is divided into ground points (ground) and non-ground points (non-ground). There are many methods for extracting ground points from the point cloud, and the method is not limited in this embodiment. For non-ground points, the computer device may divide the non-ground points into corner points and surface points according to curvature. Thus, a set of points cloud includes ground points, corner points, and face points.
The computer equipment can perform modeling according to the characteristics of the point clouds of each category to obtain a point cloud distance model, and calculate the distance between the point clouds of the same category in the current frame and the previous frame by using the point cloud distance model. In this embodiment, a point cloud distance model created for a ground point is referred to as a ground point distance model, a point cloud distance model created for a corner is referred to as a corner distance model, and a point cloud distance model created for a ground point is referred to as a surface point distance model. The following describes the calculation modes of the three point cloud distance models respectively.
(1) The point cloud distance model is a ground point distance model;
specifically, obtaining the point cloud distance model, and calculating the distance between the similar point clouds in the current frame and the previous frame according to the point cloud distance model may include: when the point cloud distance model is a ground point distance model, calculating the plane distance from each ground point in the point cloud set of the current frame to the ground in the previous frame, wherein the ground is composed of the ground points in the point cloud set of the previous frame. The ground point distance model is used for estimating the plane distance formed by the current ground point to the ground points in the target point cloud set.
Assuming that the current frame is the (k+1) th frame, the corresponding point cloud set is X k+1 The former frame is the kth frame, and the corresponding point cloud set is X k For ground point i, where i ε X k+1 Search for X through nearest neighbor k Three similar ground points j, l and p, and these three ground points j, l and p may form a ground, so that the plane distance of the current ground point i from the ground
(2) The point cloud distance model is a corner distance model;
specifically, obtaining the point cloud distance model, and calculating the distance between the similar point clouds in the current frame and the previous frame according to the point cloud distance model may include: when the point cloud distance model is a corner distance model, calculating the corner line distance from each corner in the point cloud set of the current frame to the corner of the previous frame. The angular point distance model is used for estimating angular line distance from the current angular point to the target point cloud.
Assuming that the current frame is the (k+1) th frame, the corresponding point cloud set is X k+1 The former frame is the kth frame, and the corresponding point cloud set is X k For corner i, where i ε X k+1 Search for X through nearest neighbor k Two similar angular points j and l, and the two angular points j and l can form a straight line, so that the distance from the current angular point i to the angle line of the connecting line
(3) The point cloud distance model is a face point distance model;
specifically, obtaining the point cloud distance model, and calculating the distance between the similar point clouds in the current frame and the previous frame according to the point cloud distance model may include: when the point cloud distance model is a face point distance model, calculating the plane distance between each face point in the point cloud set of the current frame and the plane in the previous frame, wherein the plane is formed by the face points in the point cloud set of the previous frame. The surface point distance model is used for estimating the plane distance formed by the surface points in the cloud set of the current surface points to the target points.
Assuming that the current frame is the (k+1) th frame, the corresponding point cloud set is X k+1 The former frame is the kth frame, and the corresponding point cloud set is X k For a face point i, where i ε X k+1 Search for X through nearest neighbor k The three similar surface points j, l and p can form a plane, so that the plane distance from the current surface point i to the plane is equal to the plane distance
And 103, creating an error function according to the distance, and calculating the pose of the current frame relative to the previous frame according to the error function.
Calculating the distance between the point clouds of the same type in the current frame and the previous frame may also be referred to as motion estimation, the purpose of which is to calculate the pose of the transformation.
The computer device constructs a transformation relation f (x) for the point clouds of different features such that:
f c (X (k+1，i) ，T k+1 )＝d c ，i∈ε k+1
f g (X (k+1，i) ，T k+1 )＝d g ，i∈δ k+1
wherein ε k+1 Represents the set of corner points in the set of point clouds of the current frame,
representing the set of face points, delta, in the set of point clouds of the current frame k+1 Representing the set of ground points in the set of point clouds of the current frame.
The computer device can construct an error function f (T k+1 ) =d, minimizing d to 0 by LM (levenberg-marquardt) method to solve for optimal pose transformation
And 104, creating a high-precision map according to the pose.
After the pose is obtained, the computer equipment can eliminate errors through loop detection and global optimization, and finally a high-precision map is generated.
In summary, according to the method for constructing the high-precision map provided by the embodiment of the application, the point cloud in the point cloud set is divided into three categories of the ground point, the corner point and the surface point, and then the high-precision map is created, so that the diversity of features can be increased, the constraint of the z axis can be increased through independent processing of the ground point, and the problem of excessively fast z-axis divergence is relieved.
SLAM technology includes back-end optimization in addition to front-end odometry. The back-end optimization usually eliminates the accumulated error by adding a loop, and the loop usually adopts matching methods such as ICP, however, if the accumulated error between two frames of laser radar data is too large, it is difficult to calculate the correct loop pose. Therefore, we can also use global matching method to estimate pose, such as Cartographer, which uses CSM (Correlation Scan Match, correlation scan matching) to estimate pose, but the time consumption is greatly increased on the estimation of 6 degrees of freedom, and the overall calculation efficiency of mapping is reduced. In order to solve the problem of excessive consumption of loop matching resources, the present embodiment provides a loop detection method, and the specific flow please refer to fig. 2:
The computer device may perform loop detection on the point cloud set by a number of methods, which is not limited in this embodiment.
If a loop is detected, the computer device obtains a point-cloud set of two frames of lidar data, and the transformation between the two point-cloud sets needs to be calculated.
Assuming that the two frames of laser radar data detected are the mth frame and the nth frame of laser radar data, the mth and nth point cloud sets are corresponding. The computer device may fix one point cloud set as a target point cloud set, calculating a transformation of the other point cloud set relative to the target point cloud set. The computing process is divided into two parts, wherein the first part is used for matching the ground points in the mth and nth point cloud sets, and the second part is used for matching the corner points in the mth and nth point cloud sets.
In solving the first part, the computer device processes the ground points using Normal-Icp methods to preferentially estimate transformed values for the three degrees of freedom [ z, roll, pitch ]. Where z represents the z-axis, roll represents roll angle, pitch represents pitch angle.
And 203, calculating conversion values of x-axis, y-axis and yaw angle between the m-th frame and the n-th frame of laser radar data by taking the conversion values of the z-axis, the roll angle and the pitch angle as initial values.
Specifically, the computer device may compress the corner points and the face points in the mth point cloud set and the nth point cloud set into a two-dimensional map with the transformed values of the z-axis, the roll angle and the pitch angle as initial values; and matching the two-dimensional map by using a CSM scanning matching algorithm to obtain conversion values of an x axis, a y axis and a yaw angle between the laser radar data of the m frame and the laser radar data of the n frame.
The computer equipment can fix the transformation values of the z-axis, the roll angle and the pitch angle as initial values, and further process corner points and face points. After compressing the corner points and the face points into a two-dimensional map, the computer device may use CSM to match to calculate transformed values for the three degrees of freedom [ x, y, yaw ]. Where x represents the x-axis, y represents the y-axis, and yaw represents the yaw angle.
And 204, calculating a final conversion value between the m-th frame and the n-th frame of laser radar data by using an ICP algorithm by taking the conversion values of the x-axis, the y-axis, the z-axis, the yaw angle, the roll angle and the pitch angle as initial values.
The computer equipment combines the transformation values obtained by the two calculations to finally obtain the transformation values with 6 degrees of freedom of [ x, y, z, roll, pitch, yaw ]. Because of discretization errors in the calculation process, the computer equipment also needs to take the transformation value of 6 degrees of freedom as an initial value, and then carries out ICP matching on the m-th frame and the n-th frame of laser radar data, so that the final and accurate transformation of the two frames of laser radar data can be obtained. The matching method of the loop can improve the matching success rate and the accuracy under larger accumulated errors on one hand; on the other hand, the calculation time is greatly shortened, and the efficiency is improved.
If loop-back matching is introduced, step 104 may be replaced with: and creating a high-precision map according to the pose and the final transformation value.
In the embodiment, by introducing the ground points into the loop matching link, the estimation of the pose with 6 degrees of freedom can be decoupled, the matching success rate and the accuracy under the condition of larger accumulated error can be realized, and the calculation time can be shortened.
Referring to fig. 3, a block diagram of a high-precision map construction apparatus according to an embodiment of the present application is shown, where the high-precision map construction apparatus may be applied to a computer device. The high-precision map construction device may include:
an acquisition module 310, configured to acquire a point cloud set of laser radar data of a current frame and a previous frame, where the point cloud set includes ground points, corner points, and surface points;
the obtaining module 310 is further configured to obtain a point cloud distance model, and calculate a distance between similar point clouds in the current frame and the previous frame according to the point cloud distance model;
a calculation module 320, configured to create an error function according to the distance, and calculate a pose of the current frame relative to the previous frame according to the error function;
the creation module 330 is configured to create a high-precision map according to the pose.
In an alternative embodiment, the computing module 320 is further configured to:
when the point cloud distance model is a ground point distance model, calculating the plane distance from each ground point in the point cloud set of the current frame to the ground in the previous frame, wherein the ground is formed by the ground points in the point cloud set of the previous frame.
In an alternative embodiment, the computing module 320 is further configured to:
when the point cloud distance model is a corner distance model, calculating the corner line distance from each corner in the point cloud set of the current frame to the corner of the previous frame.
In an alternative embodiment, the computing module 320 is further configured to:
when the point cloud distance model is a face point distance model, calculating the plane distance between each face point in the point cloud set of the current frame and the plane in the previous frame, wherein the plane is formed by the face points in the point cloud set of the previous frame.
Referring to fig. 4, in an alternative embodiment, the apparatus further includes:
the detection module 340 is configured to perform loop detection on the point cloud set of each frame of laser radar data;
the matching module 350 is configured to match ground points in the mth point cloud set and the nth point cloud set if a loop exists between the mth point cloud set and the nth point cloud set, so as to obtain a conversion value of a z-axis, a roll angle and a pitch angle between the mth frame and the nth frame of laser radar data;
the calculation module 320 is further configured to calculate conversion values of x-axis, y-axis and yaw angle between the mth frame and the nth frame of lidar data, using the conversion values of the z-axis, the roll angle and the pitch angle as initial values;
the calculation module 320 is further configured to calculate a final conversion value between the mth frame and the nth frame of lidar data using an ICP algorithm with the conversion values of the x-axis, the y-axis, the z-axis, the yaw angle, the roll angle, and the pitch angle as initial values.
In an alternative embodiment, the computing module 320 is further configured to:
the transformation values of the z-axis, the roll angle and the pitch angle are used as initial values, and the corner points and the face points in the mth point cloud set and the nth point cloud set are compressed into a two-dimensional map;
and matching the two-dimensional map by using a CSM scanning matching algorithm to obtain conversion values of an x axis, a y axis and a yaw angle between the laser radar data of the m frame and the laser radar data of the n frame.
In an alternative embodiment, creation module 330 is further configured to:
and creating a high-precision map according to the pose and the final transformation value.
In summary, according to the device for constructing the high-precision map, the point cloud in the point cloud set is divided into three categories of the ground point, the corner point and the surface point, and then the high-precision map is created, so that the diversity of features can be increased, the constraint of the z axis can be increased through independent processing of the ground point, and the problem that the z axis diverges too fast is solved.
By introducing the ground points into the loop matching link, the estimation of the pose with 6 degrees of freedom can be decoupled, the matching success rate and accuracy under the condition of larger accumulated error are realized, and the calculation time is shortened.
One embodiment of the present application provides a computer-readable storage medium having stored therein at least one instruction that is loaded and executed by a processor to implement a method of constructing a high-precision map as described above.
One embodiment of the present application provides a computer device including a processor and a memory having at least one instruction stored therein, the instruction being loaded and executed by the processor to implement a method of constructing a high-precision map as described above.
It should be noted that: in the high-precision map construction device provided in the above embodiment, only the division of the above functional modules is used for illustration when the high-precision map is constructed, and in practical application, the above functional allocation may be performed by different functional modules according to needs, that is, the internal structure of the high-precision map construction device is divided into different functional modules, so as to complete all or part of the functions described above. In addition, the device for constructing the high-precision map provided in the above embodiment and the method embodiment for constructing the high-precision map belong to the same concept, and detailed implementation processes of the device are shown in the method embodiment, and are not repeated here.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program for instructing relevant hardware, where the program may be stored in a computer readable storage medium, and the storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The foregoing description is not intended to limit the embodiments of the present application, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the embodiments of the present application are intended to be included within the scope of the embodiments of the present application.
Claims (10)
1. The method for constructing the high-precision map is characterized by comprising the following steps of:
acquiring a point cloud set of laser radar data of a current frame and a previous frame, wherein the point cloud set comprises ground points, corner points and face points;
acquiring a point cloud distance model, and calculating the distance between the same type of point clouds in the current frame and the previous frame according to the point cloud distance model;
creating an error function according to the distance, and calculating the pose of the current frame relative to the previous frame according to the error function;
and creating a high-precision map according to the pose.
2. The method for constructing a high-precision map according to claim 1, wherein the obtaining a point cloud distance model, and calculating the distance between the current frame and the similar point clouds in the previous frame according to the point cloud distance model, comprises:
and when the point cloud distance model is a ground point distance model, calculating the plane distance between each ground point in the point cloud set of the current frame and the ground in the previous frame, wherein the ground is formed by the ground points in the point cloud set of the previous frame.
3. The method for constructing a high-precision map according to claim 1, wherein the obtaining a point cloud distance model, and calculating the distance between the current frame and the similar point clouds in the previous frame according to the point cloud distance model, comprises:
and when the point cloud distance model is an angular point distance model, calculating the angular line distance from each angular point in the point cloud set of the current frame to the angular point of the previous frame.
4. The method for constructing a high-precision map according to claim 1, wherein the obtaining a point cloud distance model, and calculating the distance between the current frame and the similar point clouds in the previous frame according to the point cloud distance model, comprises:
and when the point cloud distance model is a face point distance model, calculating the plane distance between each face point in the point cloud set of the current frame and the plane in the previous frame, wherein the plane is formed by the face points in the point cloud set of the previous frame.
5. The method of constructing a high-precision map according to any one of claims 1 to 4, further comprising:
performing loop detection on the point cloud set of each frame of laser radar data;
if a loop exists between the mth point cloud set and the nth point cloud set, matching ground points in the mth point cloud set and the nth point cloud set to obtain a conversion value of a z-axis, a roll angle and a pitch angle between mth frame and nth frame laser radar data;
taking the conversion values of the z-axis, the roll angle and the pitch angle as initial values, and calculating the conversion values of the x-axis, the y-axis and the yaw angle between the laser radar data of the mth frame and the laser radar data of the nth frame;
and calculating a final conversion value between the m-th frame and the n-th frame of laser radar data by using the conversion values of the x-axis, the y-axis, the z-axis, the yaw angle, the roll angle and the pitch angle as initial values through an ICP algorithm.
6. The method according to claim 5, wherein calculating the conversion values of the x-axis, y-axis and yaw angle between the mth frame and the nth frame of lidar data using the conversion values of the z-axis, roll angle and pitch angle as initial values comprises:
the transformation values of the z-axis, the roll angle and the pitch angle are used as initial values, and the corner points and the face points in the mth point cloud set and the nth point cloud set are compressed into a two-dimensional map;
and matching the two-dimensional map by using a CSM scanning matching algorithm to obtain conversion values of an x axis, a y axis and a yaw angle between the laser radar data of the mth frame and the laser radar data of the nth frame.
7. The method of claim 5, wherein creating the high-precision map from the pose comprises:
and creating the high-precision map according to the pose and the final transformation value.
8. A high-precision map construction apparatus, characterized in that the apparatus comprises:
the acquisition module is used for acquiring a point cloud set of the laser radar data of the current frame and the previous frame, wherein the point cloud set comprises ground points, corner points and face points;
the acquisition module is further used for acquiring a point cloud distance model, and calculating the distance between the current frame and the similar point cloud in the previous frame according to the point cloud distance model;
the calculating module is used for creating an error function according to the distance and calculating the pose of the current frame relative to the previous frame according to the error function;
and the creation module is used for creating a high-precision map according to the pose.
9. A computer readable storage medium having stored therein at least one instruction that is loaded and executed by a processor to implement the method of constructing a high precision map according to any one of claims 1 to 7.
10. A computer device comprising a processor and a memory, the memory having stored therein at least one instruction that is loaded and executed by the processor to implement the method of constructing a high precision map according to any one of claims 1 to 7.
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