CN111080682A - Point cloud data registration method and device - Google Patents
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- CN111080682A CN111080682A CN201911232830.6A CN201911232830A CN111080682A CN 111080682 A CN111080682 A CN 111080682A CN 201911232830 A CN201911232830 A CN 201911232830A CN 111080682 A CN111080682 A CN 111080682A
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
- G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
- G06T7/337—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods involving reference images or patches
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/70—Determining position or orientation of objects or cameras
- G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
- G06T7/74—Determining position or orientation of objects or cameras using feature-based methods involving reference images or patches
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10028—Range image; Depth image; 3D point clouds
Abstract
The disclosure relates to a method and a device for registering point cloud data, comprising the following steps: acquiring point cloud data and position data of a single-point GPS, wherein the point cloud data comprises a time stamp of each frame of point cloud data, and the position data comprises a time stamp of a reference position corresponding to each frame of point cloud data; registering two adjacent frames of point cloud data to obtain a first pose transformation parameter; determining an actual position corresponding to each frame of point cloud data according to the first pose transformation parameter; acquiring a reference position corresponding to a time stamp according to the time stamp corresponding to each frame of point cloud data; and determining target registration parameters according to the actual positions and the reference positions of the point cloud data of each frame, so that the difference between the actual position sum and each current position is minimum.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method and an apparatus for registering point cloud data, and a computer-readable storage medium.
Background
Laser Slam (instantaneous positioning And Mapping) map building is one of high-precision point cloud map building technologies, And a local map built by the laser Slam needs to be registered to a world coordinate system in industrial application.
In the control point-based global registration method, control points with obvious characteristics are measured through manual one-line mapping, local characteristic points expressing real control points are selected on a partial map of a Slam map, the characteristic points and the control points on the partial map form a one-to-one mapping relation, a normal equation set is constructed, transformation parameters are solved by adopting least square nonlinear optimization, and the partial map is registered to a global coordinate system. The global registration method based on the control points needs manual field measurement of the control points and selection of local control points on a map, so that the efficiency is low and the timeliness is poor.
In the related global registration technology, sensors such as a high-precision combined inertial navigation module and a real-time difference module are added into acquisition equipment, a real-time difference module is used for acquiring a real position coordinate, multi-sensor fusion is carried out in the laser Slam image building process, and a point cloud map under a global coordinate system is built.
Disclosure of Invention
The inventor thinks that: the related global registration technology is added with an expensive high-precision combined inertial navigation and real-time difference module, the hardware cost is high, multi-sensor data are fused, the complexity of a laser Slam algorithm is increased, the real-time performance is poor, and the registration efficiency is low.
In order to solve the technical problems, the method and the device have the advantages that the hardware cost is reduced, the real-time performance is high, and the registration efficiency is improved.
According to a first aspect of the present disclosure, there is provided a method of registering point cloud data, comprising: acquiring point cloud data and position data of a single-point Global Positioning System (GPS), wherein the point cloud data comprises a time stamp of each frame of point cloud data, and the position data comprises a time stamp of a reference position corresponding to each frame of point cloud data; for any group of two adjacent frames of point cloud data in the point cloud data, registering the two adjacent frames of point cloud data to obtain a first pose transformation parameter between the two adjacent frames of point cloud data; determining an actual position corresponding to each frame of point cloud data according to the first pose transformation parameter; for each frame of point cloud data, acquiring a reference position corresponding to a timestamp according to the timestamp corresponding to each frame of point cloud data; and determining target registration parameters according to the actual position and the reference position of each frame of point cloud data, so that the difference value between the actual position of each frame of point cloud data and the current position corresponding to each frame of point cloud data is minimum.
In some embodiments, determining the target registration parameter according to the actual position and the reference position of each frame of point cloud data so that the difference between the actual position of each frame of point cloud data and the current position corresponding to each frame of point cloud data is minimized comprises: for each frame of point cloud data, constructing a residual error equation set related to target registration parameters based on the actual position and the reference position; and optimizing the target registration parameters by using a least square algorithm to ensure that the difference value between the actual position of each frame of point cloud data and the current position corresponding to each frame of point cloud data is minimum.
In some embodiments, determining an actual location corresponding to the each frame of point cloud data from the first pose transformation parameter comprises: calculating a second attitude transformation parameter between any frame of point cloud data and the first frame of point cloud data according to the first attitude transformation parameter, wherein the second attitude transformation parameter comprises a translation quantity matrix; and for each frame of point cloud data, determining an actual position corresponding to each frame of point cloud data according to the translation amount matrix.
In some embodiments, the method of registration of point cloud data further comprises: transforming each space point in each frame of point cloud data to a local coordinate system with the first frame of point cloud data as a coordinate origin by using the second pose transformation parameter to obtain a local point cloud picture; and transforming each space point in the local point cloud picture to a global coordinate system by using the target registration parameters to obtain a global point cloud picture.
In some embodiments, the method of registration of point cloud data further comprises: and grid sampling is carried out on the point cloud data.
In some embodiments, grid sampling the point cloud data comprises: for each frame of point cloud data, establishing a three-dimensional grid index by using a fixed voxel threshold value, wherein each space point in each three-dimensional grid has a unique index number; determining a center point of each three-dimensional grid; for each three-dimensional grid, calculating the distance between the space point corresponding to each index number and the center point of each three-dimensional grid; and determining the space point with the closest distance to the central point of each three-dimensional grid as the space point of each three-dimensional grid.
In some embodiments, the method of registration of point cloud data further comprises: and filtering the point cloud data.
In some embodiments, filtering the point cloud data comprises: for each frame of point cloud data, calculating the average distance between the adjacent points of each space point and each space point; determining a Gaussian distribution function according to the average distance corresponding to each space point; and for each frame of point cloud data, filtering out space points with the average distance larger than the standard deviation of the Gaussian distribution function.
In some embodiments, the point cloud data is acquired by a lidar device.
In some embodiments, registering two adjacent frames of point cloud data comprises: and registering two adjacent frames of point cloud data by utilizing a positive-distribution transform (NDT) method.
In some embodiments, calculating a second pose transformation parameter between any frame of point cloud data and the first frame of point cloud data from the first pose transformation parameter comprises: determining other frame point cloud data between the first frame point cloud data and the any frame point cloud data; and determining a second attitude transformation parameter between the any frame point cloud data and the first frame point cloud data according to the product of the other frame point cloud data of each frame and the first attitude transformation parameter between the any frame point cloud data and the first frame point cloud data.
According to a second aspect of the present disclosure, there is provided an apparatus for registering point cloud data, comprising: a first acquisition module configured to acquire point cloud data including a time stamp of each frame of point cloud data and position data of a single point Global Positioning System (GPS), the position data including a time stamp of a reference position corresponding to each frame of point cloud data; the registration module is configured to register any group of two adjacent frames of point cloud data in the point cloud data to obtain a first pose transformation parameter between the two adjacent frames of point cloud data; a first determining module configured to determine an actual position corresponding to each frame of point cloud data according to the first pose transformation parameter; a second obtaining module configured to obtain, for each frame of point cloud data, a reference position corresponding to a timestamp corresponding to the each frame of point cloud data; the second determination module is configured to determine the target registration parameters according to the actual position and the reference position of each frame of point cloud data, so that the difference value between the actual position of each frame of point cloud data and the current position corresponding to each frame of point cloud data is minimum.
According to a third aspect of the present disclosure, there is provided an apparatus for registering point cloud data, comprising: a memory; and a processor coupled to the memory, the processor configured to perform the method of registering point cloud data of any of the above embodiments based on instructions stored in the memory.
According to a fourth aspect of the present disclosure, a computer-storable medium has stored thereon computer program instructions which, when executed by a processor, implement the method of registering point cloud data as described in any of the embodiments above.
In the embodiment, the hardware cost is reduced, the real-time performance is high, and the registration efficiency is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description, serve to explain the principles of the disclosure.
The present disclosure may be more clearly understood from the following detailed description, taken with reference to the accompanying drawings, in which:
fig. 1 illustrates a flow diagram of a method of registration of point cloud data according to some embodiments of the present disclosure;
FIG. 2 illustrates a block diagram of an apparatus for registration of point cloud data according to some embodiments of the present disclosure;
FIG. 3 shows a block diagram of an apparatus for registration of point cloud data according to further embodiments of the present disclosure;
FIG. 4 illustrates a block diagram of a computer system for implementing some embodiments of the present disclosure.
Detailed Description
Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.
Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the disclosure, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
Fig. 1 illustrates a flow diagram of a method of registration of point cloud data according to some embodiments of the present disclosure.
As shown in fig. 1, the registration method of the point cloud data includes steps S110 to S150.
In step S110, point cloud data and position data of a Global Positioning System (GPS) are acquired. The point cloud data includes a timestamp for each frame of point cloud data. The location data includes a time stamp of a reference location corresponding to each frame of point cloud data. For example, the point cloud data is acquired by a lidar device. And acquiring local single-frame point cloud data with the center of the laser radar as an origin in the acquisition process of the laser radar equipment. The single frame point cloud is the point cloud data of each frame. The most basic expression unit of the single-frame point cloud data is space point data, the space point is identified by X, Y, Z coordinates, and a local coordinate system formed by X, Y, Z takes the center of a laser radar as a coordinate origin. A single point GPS acquires global position data in real time, which is identified by longitude, latitude, and altitude.
In step S120, for any group of two adjacent frames of point cloud data in the point cloud data, the two adjacent frames of point cloud data are registered to obtain a first pose transformation parameter between the two adjacent frames of point cloud data. The first orientation and posture transformation parameters comprise a translation amount matrix and a rotation amount matrix between two adjacent frames of point cloud data.
For example, the registration of two adjacent frames of point cloud data is realized as follows.
And (4) carrying out registration on two adjacent frames of point cloud data by utilizing NDT (Normal distribution Transform).
The registration of two adjacent frames of point cloud data by utilizing a positive-distribution transform (NDT) method comprises the following steps.
Firstly, subdividing the space occupied by the current frame point cloud data into a cell grid.
Second, an approximate surface for each cell grid is computed based on the distribution of spatial points within the cell grid.
Then, each space point of the next frame of point cloud data is transformed to the space position of the current frame of point cloud data, a cell grid into which each space point of the next frame of point cloud data falls is calculated, and a probability distribution function of response is calculated.
And finally, calculating the optimal values of all the spatial points, completing the NDT point cloud matching, and outputting a first pose transformation parameter.
In step S130, an actual position corresponding to each frame of point cloud data is determined according to the first pose transformation parameter.
For example, according to the first pose transformation parameter, determining the actual position corresponding to each frame of point cloud data includes the following steps:
first, a second pose transformation parameter between any frame of point cloud data and the first frame of point cloud data is calculated. The second attitude transformation parameters include a translation amount matrix.
Then, for each frame of point cloud data, determining an actual position corresponding to each frame of point cloud data according to the translation amount matrix. The actual positions are all position points in a laser odometer of the laser radar, and the actual positions form the laser odometer of the laser radar and represent the movement track of the laser radar in a local coordinate system in the process of collecting point cloud data.
In step S140, for each frame of point cloud data, a reference position corresponding to a timestamp is obtained according to the timestamp corresponding to each frame of point cloud data.
In step S150, a target registration parameter is determined according to the actual position and the reference position of each frame of point cloud data, so that a difference between the actual position of each frame of point cloud data and the current position corresponding to each frame of point cloud data is minimized.
For example, determining the target registration parameters according to the actual position and the reference position of each frame of point cloud data so that the difference between the actual position of each frame of point cloud data and the current position corresponding to each frame of point cloud data is minimum includes the following steps:
firstly, for each frame of point cloud data, a residual error equation set related to target registration parameters is constructed based on an actual position and a reference position.
And then, optimizing the target registration parameters by using a least square algorithm to ensure that the difference value between the actual position of each frame of point cloud data and the current position corresponding to each frame of point cloud data is minimum. For example, the target registration parameters include X, Y, Z three translational amounts and three rotational amounts for three directions. In some embodiments, of the initial values of the target registration parameters, three translation amounts and three rotation amounts are each set to 0. The present disclosure utilizes a least squares algorithm for non-linear optimization solution.
According to the method and the device, the point cloud data and the position data acquired by the single-point GPS are combined, the target registration parameters are solved, the hardware cost is reduced, and the registration real-time performance and the registration efficiency are improved. In addition, the full-automatic registration is realized, manual participation is not needed, and the labor cost is reduced. The present disclosure provides a coarse registration method that can be applied to a specific scenario.
In some embodiments, the method of registering point cloud data further comprises the following steps.
Firstly, transforming each space point in each frame of point cloud data to a local coordinate system with the first frame of point cloud data as a coordinate origin by using a second pose transformation parameter to obtain a local point cloud picture.
And then, transforming each space point in the local point cloud picture to a global coordinate system by using the target registration parameters to obtain a global point cloud picture. The global coordinate system is a three-dimensional coordinate system with the earth center as a coordinate origin.
In some embodiments, the method of registering point cloud data further comprises the following steps.
And grid sampling is carried out on the point cloud data. Partial space points in each frame of point cloud data can be deleted through grid sampling, so that the aim of rarefiing the point cloud data is fulfilled, and the registration precision and efficiency are improved.
For example, grid sampling of point cloud data is achieved as follows.
First, for each frame of point cloud data, a three-dimensional grid index is created with a fixed voxel threshold, with each spatial point in each three-dimensional grid having a unique index number. For example, the fixed voxel threshold is 0.1 meters.
Second, the center point of each three-dimensional mesh is determined.
Then, for each three-dimensional grid, calculating the distance between the spatial point corresponding to each index number and the central point of each three-dimensional grid.
And finally, determining the space point with the closest distance to the central point of each three-dimensional grid as the space point of each three-dimensional grid.
In some embodiments, the method of registering point cloud data further comprises the following steps.
And carrying out filtering processing on the point cloud data. Through filtering processing, noise in the point cloud data is eliminated, and registration accuracy and efficiency are improved.
The filtering process of the point cloud data is realized, for example, as follows.
First, for each frame of point cloud data, an average distance between neighboring points of each spatial point to each spatial point is calculated.
Then, a gaussian distribution function is determined based on the average distance corresponding to each spatial point.
And finally, filtering out the space points with the average distance larger than the standard deviation of the Gaussian distribution function for each frame of point cloud data.
Fig. 2 illustrates a block diagram of an apparatus for registration of point cloud data according to some embodiments of the present disclosure.
As shown in fig. 2, the registration apparatus 2 for point cloud data includes a first acquisition module 21, a registration module 22, a first determination module 23, a second acquisition module 24, and a second determination module 25.
The first acquiring module 21 is configured to acquire point cloud data and position data of a single point global positioning system GPS, the point cloud data including a time stamp of each frame of point cloud data, and the position data including a time stamp of a reference position corresponding to each frame of point cloud data, for example, perform step S110 shown in fig. 1.
The registration module 22 is configured to register any set of two adjacent frames of point cloud data in the point cloud data, resulting in a first pose transformation parameter between the two adjacent frames of point cloud data, for example, perform step S120 shown in fig. 1.
The first determination module 23 is configured to determine an actual position corresponding to each frame of point cloud data according to the first pose transformation parameter, for example, to perform step S130 shown in fig. 1.
The second obtaining module 24 is configured to, for each frame of point cloud data, obtain a reference position corresponding to a timestamp according to the timestamp corresponding to each frame of point cloud data, for example, perform step S140 shown in fig. 1.
The second determining module 25 is configured to determine the target registration parameters according to the actual position and the reference position of each frame of point cloud data, so that the difference between the actual position of each frame of point cloud data and the current position corresponding to each frame of point cloud data is minimized, for example, perform step S150 shown in fig. 1.
FIG. 3 illustrates a block diagram of an apparatus for registration of point cloud data according to further embodiments of the present disclosure.
As shown in fig. 3, the registration apparatus 3 of point cloud data includes a memory 31; and a processor 32 coupled to the memory 31, the memory 31 being configured to store instructions for performing embodiments of the method for registering point cloud data. The processor 32 is configured to perform a method of registration of point cloud data in any of the embodiments of the present disclosure based on instructions stored in the memory 31.
FIG. 4 illustrates a block diagram of a computer system for implementing some embodiments of the present disclosure.
As shown in FIG. 4, computer system 40 may take the form of a general purpose computing device. Computer system 40 includes a memory 410, a processor 420, and a bus 400 that connects the various system components.
The memory 410 may include, for example, system memory, non-volatile storage media, and the like. The system memory stores, for example, an operating system, an application program, a Boot Loader (Boot Loader), and other programs. The system memory may include volatile storage media such as Random Access Memory (RAM) and/or cache memory. The non-volatile storage medium stores, for instance, instructions to perform corresponding embodiments of at least one of the methods of registration of point cloud data. Non-volatile storage media include, but are not limited to, magnetic disk storage, optical storage, flash memory, and the like.
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable apparatus to produce a machine, such that the execution of the instructions by the processor results in an apparatus that implements the functions specified in the flowchart and/or block diagram block or blocks.
These computer-readable program instructions may also be stored in a computer-readable memory that can direct a computer to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart and/or block diagram block or blocks.
The present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects.
By the point cloud data registration method and device and the computer storage medium in the embodiment, the hardware cost is reduced, the real-time performance is high, and the registration efficiency is improved.
Thus far, a method and apparatus for registration of point cloud data, a computer-readable storage medium, according to the present disclosure have been described in detail. Some details that are well known in the art have not been described in order to avoid obscuring the concepts of the present disclosure. It will be fully apparent to those skilled in the art from the foregoing description how to practice the presently disclosed embodiments.
Claims (14)
1. A method of registration of point cloud data, comprising:
acquiring point cloud data and position data of a single-point Global Positioning System (GPS), wherein the point cloud data comprises a time stamp of each frame of point cloud data, and the position data comprises a time stamp of a reference position corresponding to each frame of point cloud data;
for any group of two adjacent frames of point cloud data in the point cloud data, registering the two adjacent frames of point cloud data to obtain a first pose transformation parameter between the two adjacent frames of point cloud data;
determining an actual position corresponding to each frame of point cloud data according to the first pose transformation parameter;
for each frame of point cloud data, acquiring a reference position corresponding to a timestamp according to the timestamp corresponding to each frame of point cloud data;
and determining target registration parameters according to the actual position and the reference position of each frame of point cloud data, so that the difference value between the actual position of each frame of point cloud data and the current position corresponding to each frame of point cloud data is minimum.
2. The point cloud data registration method of claim 1, wherein determining target registration parameters according to the actual position and the reference position of each frame of point cloud data so that a difference between the actual position of each frame of point cloud data and the current position corresponding to each frame of point cloud data is minimized comprises:
for each frame of point cloud data, constructing a residual error equation set related to target registration parameters based on the actual position and the reference position;
and optimizing the target registration parameters by using a least square algorithm to ensure that the difference value between the actual position of each frame of point cloud data and the current position corresponding to each frame of point cloud data is minimum.
3. The method for registering point cloud data of claim 1, wherein determining an actual location corresponding to the each frame of point cloud data according to the first pose transformation parameter comprises:
calculating a second attitude transformation parameter between any frame of point cloud data and the first frame of point cloud data according to the first attitude transformation parameter, wherein the second attitude transformation parameter comprises a translation quantity matrix;
and for each frame of point cloud data, determining an actual position corresponding to each frame of point cloud data according to the translation amount matrix.
4. The method of registration of point cloud data of claim 1, further comprising:
transforming each space point in each frame of point cloud data to a local coordinate system with the first frame of point cloud data as a coordinate origin by using the second pose transformation parameter to obtain a local point cloud picture;
and transforming each space point in the local point cloud picture to a global coordinate system by using the target registration parameters to obtain a global point cloud picture.
5. The method of registration of point cloud data of claim 1, further comprising:
and grid sampling is carried out on the point cloud data.
6. The method for registration of point cloud data of claim 5, wherein grid sampling the point cloud data comprises:
for each frame of point cloud data, establishing a three-dimensional grid index by using a fixed voxel threshold value, wherein each space point in each three-dimensional grid has a unique index number;
determining a center point of each three-dimensional grid;
for each three-dimensional grid, calculating the distance between the space point corresponding to each index number and the center point of each three-dimensional grid;
and determining the space point with the closest distance to the central point of each three-dimensional grid as the space point of each three-dimensional grid.
7. The method of registration of point cloud data of claim 1, further comprising:
and filtering the point cloud data.
8. The registration method of point cloud data of claim 7, wherein filtering the point cloud data comprises:
for each frame of point cloud data, calculating the average distance between the adjacent points of each space point and each space point;
determining a Gaussian distribution function according to the average distance corresponding to each space point;
and for each frame of point cloud data, filtering out space points with the average distance larger than the standard deviation of the Gaussian distribution function.
9. The method for registration of point cloud data of claim 1, wherein the point cloud data is acquired by a lidar device.
10. The registration method of point cloud data of claim 1, wherein registering two adjacent frames of point cloud data comprises:
and registering two adjacent frames of point cloud data by utilizing a positive-distribution transform (NDT) method.
11. The registration method of point cloud data of claim 3, wherein calculating second pose transformation parameters between any frame of point cloud data and a first frame of point cloud data according to the first pose transformation parameters comprises:
determining other frame point cloud data between the first frame point cloud data and the any frame point cloud data;
and determining a second attitude transformation parameter between the any frame point cloud data and the first frame point cloud data according to the product of the other frame point cloud data of each frame and the first attitude transformation parameter between the any frame point cloud data and the first frame point cloud data.
12. An apparatus for registering point cloud data, comprising:
a first acquisition module configured to acquire point cloud data including a time stamp of each frame of point cloud data and position data of a single point Global Positioning System (GPS), the position data including a time stamp of a reference position corresponding to each frame of point cloud data;
the registration module is configured to register any group of two adjacent frames of point cloud data in the point cloud data to obtain a first pose transformation parameter between the two adjacent frames of point cloud data;
a first determining module configured to determine an actual position corresponding to each frame of point cloud data according to the first pose transformation parameter;
a second obtaining module configured to obtain, for each frame of point cloud data, a reference position corresponding to a timestamp corresponding to the each frame of point cloud data;
the second determination module is configured to determine the target registration parameters according to the actual position and the reference position of each frame of point cloud data, so that the difference value between the actual position of each frame of point cloud data and the current position corresponding to each frame of point cloud data is minimum.
13. An apparatus for registering point cloud data, comprising:
a memory; and
a processor coupled to the memory, the processor configured to perform the method of registration of point cloud data of any of claims 1-11 based on instructions stored in the memory.
14. A computer-storable medium having stored thereon computer program instructions which, when executed by a processor, implement a method of registration of point cloud data according to any of claims 1 to 11.
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| CN113192197A (en) * | 2021-05-24 | 2021-07-30 | 北京京东乾石科技有限公司 | Method, device, equipment and storage medium for constructing global point cloud map |
| WO2021223465A1 (en) * | 2020-05-06 | 2021-11-11 | 北京嘀嘀无限科技发展有限公司 | High-precision map building method and system |
| CN114757980A (en) * | 2022-05-12 | 2022-07-15 | 苏州轻棹科技有限公司 | Offline registration method for continuous frame point cloud data |
| WO2022227096A1 (en) * | 2021-04-30 | 2022-11-03 | 深圳市大疆创新科技有限公司 | Point cloud data processing method, and device and storage medium |
| CN113192197B (en) * | 2021-05-24 | 2024-04-05 | 北京京东乾石科技有限公司 | Global point cloud map construction method, device, equipment and storage medium |
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