CN111080682B - Registration method and device for point cloud data - Google Patents
Registration method and device for point cloud data Download PDFInfo
- Publication number
- CN111080682B CN111080682B CN201911232830.6A CN201911232830A CN111080682B CN 111080682 B CN111080682 B CN 111080682B CN 201911232830 A CN201911232830 A CN 201911232830A CN 111080682 B CN111080682 B CN 111080682B
- Authority
- CN
- China
- Prior art keywords
- point cloud
- cloud data
- frame
- point
- registration
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- 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
-
- 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
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Processing Or Creating Images (AREA)
Abstract
The disclosure relates to a method and a device for registering point cloud data, comprising the following steps: acquiring point cloud data and single-point GPS (global positioning system) position data, 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 parameters; acquiring a reference position corresponding to the time stamp according to the time stamp corresponding to each frame 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 between the actual position and each previous position is minimum.
Description
Technical Field
The disclosure relates to the technical field of computers, and in particular relates to a point cloud data registration method and device and a computer storage medium.
Background
Laser Slam (Simultaneous Localization And Mapping, instant localization and mapping) mapping is one of the high-precision point cloud mapping technologies, and in industrial applications, it is required to register a local map constructed by the laser Slam under a world coordinate system.
In the global registration method based on control points, the control points with obvious characteristics are measured through manual linear mapping, local characteristic points expressing real control points are selected on a Slam map-built local map, the characteristic points and the control points on the local map form a one-to-one mapping relation, a normal equation set is constructed, and the local map is registered under a global coordinate system by adopting least square nonlinear optimization to solve transformation parameters. The global registration method based on the control points needs to manually measure the control points on site and select the local control points on the map, and has low efficiency and poor timeliness.
In the related global registration technology, sensors such as a high-precision combined inertial navigation and real-time difference module are added into acquisition equipment, real position coordinates are obtained by utilizing the real-time difference module, multi-sensor fusion is carried out in the laser Slam mapping process, and a point cloud map under a global coordinate system is constructed.
Disclosure of Invention
The inventors consider that: the related global registration technology is added with an expensive high-precision combined inertial navigation and real-time difference module, so that the hardware cost is high, the multi-sensor data are fused, the complexity of a laser Slam algorithm is increased, the instantaneity is poor, and the registration efficiency is low.
Aiming at the technical problems, the present disclosure provides a solution, which reduces hardware cost, has high real-time performance, and improves registration efficiency.
According to a first aspect of the present disclosure, there is provided a method of registering point cloud data, including: 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; registering two adjacent frames of point cloud data for any group of 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 parameters; for each frame of point cloud data, 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 of the point cloud data of each frame and the current position corresponding to the point cloud data of each frame 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 minimum includes: for each frame of point cloud data, constructing a residual equation set about target registration parameters based on the actual position and the reference position; and optimizing the target registration parameters by utilizing a least square algorithm, 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.
In some embodiments, determining the actual position corresponding to the each frame of point cloud data according to the first pose transformation parameters includes: calculating second pose transformation parameters between any frame of point cloud data and the first frame of point cloud data according to the first pose transformation parameters, wherein the second pose transformation parameters comprise a translation amount 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 quantity matrix.
In some embodiments, the method of registering 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 parameters to obtain a local point cloud image; and transforming each space point in the local point cloud image to a global coordinate system by utilizing the target registration parameters to obtain a global point cloud image.
In some embodiments, the method of registering point cloud data further comprises: and performing grid sampling on the point cloud data.
In some embodiments, performing grid sampling on the point cloud data includes: for each frame of point cloud data, creating a three-dimensional grid index with a fixed voxel threshold, wherein each spatial 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 closest to the center 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: and filtering the point cloud data.
In some embodiments, filtering the point cloud data includes: for each frame of point cloud data, calculating the average distance from the adjacent point of each space point to each space point; determining a Gaussian distribution function according to the average distance corresponding to each space point; and filtering out space points with average distances larger than the standard deviation of the Gaussian distribution function for each frame of point cloud data.
In some embodiments, the point cloud data is acquired by a lidar device.
In some embodiments, registering two adjacent frames of point cloud data includes: and registering the two adjacent frames of point cloud data by using a forward distributed transformation method NDT.
In some embodiments, calculating a second pose transformation parameter between any frame of point cloud data and the first frame of point cloud data according to the first pose transformation parameter comprises: determining other frame point cloud data between the first frame point cloud data and any frame point cloud data; and determining a second pose transformation parameter between the point cloud data of any frame and the point cloud data of the first frame according to the product of the point cloud data of other frames of each frame and the first pose transformation parameter between the point cloud data of any frame and the point cloud data of the first frame.
According to a second aspect of the present disclosure, there is provided a registration apparatus of point cloud data, including: 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 two adjacent frames of point cloud data for any group of two adjacent frames of point cloud data to obtain first pose transformation parameters between the two adjacent frames of point cloud data; the first determining module is configured to determine an actual position corresponding to each frame of point cloud data according to the first pose transformation parameters; the second acquisition module is configured to acquire a reference position corresponding to the time stamp according to the time stamp corresponding to each frame of point cloud data for each frame of point cloud data; and the second determining module is configured to determine 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.
According to a third aspect of the present disclosure, there is provided a registration apparatus of point cloud data, including: 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 embodiments described above based on instructions stored in the memory.
According to a fourth aspect of the present disclosure, a computer-readable storage medium has stored thereon computer program instructions which, when executed by a processor, implement the method of registering point cloud data according to any of the embodiments described above.
In the embodiment, the hardware cost is reduced, the instantaneity 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 disclosure may be more clearly understood from the following detailed description taken in conjunction with the accompanying drawings in which:
fig. 1 illustrates a flowchart of a method of registration of point cloud data according to some embodiments of the present disclosure;
fig. 2 illustrates a block diagram of a registration apparatus of point cloud data according to some embodiments of the present disclosure;
fig. 3 shows a block diagram of a registration apparatus 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, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless it is specifically stated otherwise.
Meanwhile, it should be understood that the sizes of the respective parts shown in the drawings are not drawn in actual scale for 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 one 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 specific values should be construed as merely illustrative, and not a limitation. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
Fig. 1 illustrates a flow chart 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 step S110 to step S150.
In step S110, point cloud data and position data of a single point GPS (Global Positioning System ) are acquired. The point cloud data includes a timestamp of 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. And 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, space points are identified by X, Y, Z coordinates, and a local coordinate system formed by X, Y, Z takes the center of the laser radar as a coordinate origin. The single-point GPS acquires global location data in real time, the global location data being identified by longitude, latitude, altitude.
In step S120, for any one set of two adjacent frames of point cloud data, the two adjacent frames of point cloud data are registered, so as to obtain a first pose transformation parameter between the two adjacent frames of point cloud data. The first pose transformation parameters comprise a translation amount matrix and a rotation amount matrix between two adjacent frames of point cloud data.
For example, registration of two adjacent frames of point cloud data is achieved as follows.
And registering the adjacent two frames of point cloud data by using an NDT (Normal Distributions Transform) and a forward distributed transformation method.
Registering the adjacent two frames of point cloud data by using the forward distributed transformation method NDT comprises the following steps.
First, the space occupied by the current frame point cloud data is subdivided into a grid of cells.
Next, an approximate surface for each cell grid is calculated based on the distribution of spatial points within the cell grid.
Then, each space point of the next frame point cloud data is transformed to the space position of the current frame point cloud data, a cell grid in which each space point of the next frame point cloud data falls is calculated, and a probability distribution function of the response is calculated.
And finally, calculating optimal values of all the space points, finishing NDT point cloud matching, and outputting a first pose transformation parameter.
In step S130, an actual position corresponding to the point cloud data of each frame is determined according to the first pose transformation parameter.
For example, according to the first pose transformation parameter, determining the actual position corresponding to the point cloud data of each frame 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 pose 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 the laser odometer of the laser radar, and all the actual positions form the laser odometer of the laser radar and represent the motion trail of the laser radar under 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 the time stamp is acquired according to the time stamp 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 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, 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 includes the steps of:
first, for each frame of point cloud data, a set of residual equations for the target registration parameters is constructed based on the actual and reference positions.
Then, the target registration parameters are optimized by utilizing a least square algorithm, 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. For example, the target registration parameters include three translational amounts and three rotational amounts for three directions of X, Y, Z. In some embodiments, in the initial value of the target registration parameter, three translational amounts and three rotational amounts are each set to 0. The present disclosure utilizes a least squares algorithm for nonlinear optimization solution.
According to the method and the device, the target registration parameters are solved by combining the point cloud data and the position data acquired by the single-point GPS, so that the hardware cost is reduced, and the real-time performance and the registration efficiency of registration 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 particular scene.
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 image.
And then, transforming each space point in the local point cloud image into a global coordinate system by utilizing the target registration parameters to obtain the global point cloud image. The global coordinate system is a three-dimensional coordinate system with the earth's geodetic center as the origin of coordinates.
In some embodiments, the method of registering point cloud data further comprises the following steps.
And performing grid sampling on the point cloud data. Through grid sampling, partial space points in the point cloud data of each frame can be deleted, so that the aim of thinning the point cloud data is fulfilled, and the registration precision and efficiency are improved.
For example, the mesh sampling of the 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, each spatial point in each three-dimensional grid having a unique index number. For example, the fixed voxel threshold is 0.1 meters.
Next, a center point of each three-dimensional grid is determined.
Then, for each three-dimensional grid, the distance between the spatial point corresponding to each index number and the center point of each three-dimensional grid is calculated.
Finally, the spatial point closest to the center point of each three-dimensional grid is determined as the spatial point of each three-dimensional grid.
In some embodiments, the method of registering point cloud data further comprises the following steps.
And filtering the point cloud data. Noise in the point cloud data is removed through filtering processing, and the accuracy and efficiency of registration are improved.
For example, the filtering process of the point cloud data is realized as follows.
First, for each frame of point cloud data, an average distance from an adjacent point of each spatial point to each spatial point is calculated.
Then, a gaussian distribution function is determined from the average distance corresponding to each spatial point.
And finally, for each frame of point cloud data, filtering out spatial points with average distance larger than the standard deviation of the Gaussian distribution function.
Fig. 2 illustrates a block diagram of a registration apparatus of point cloud data according to some embodiments of the present disclosure.
As shown in fig. 2, the registration apparatus 2 of 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 acquisition module 21 is configured to acquire point cloud data including a time stamp of each frame of point cloud data and position data of the 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, for example, to perform step S110 shown in fig. 1.
The registration module 22 is configured to register two adjacent frames of point cloud data for any one set of two adjacent frames of point cloud data, to obtain 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 determining module 23 is configured to determine an actual position corresponding to each frame of point cloud data according to the first pose transformation parameters, for example, to perform step S130 shown in fig. 1.
The second acquisition module 24 is configured to acquire, for each frame of point cloud data, a reference position corresponding to the time stamp according to the time stamp corresponding to each frame of point cloud data, for example, to perform step S140 shown in fig. 1.
The second determining module 25 is configured to determine the target registration parameter according to the actual position and the reference position of each frame of point cloud data such 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, performing step S150 shown in fig. 1.
Fig. 3 shows a block diagram of a registration apparatus 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, wherein the memory 31 is configured to store instructions for performing a corresponding embodiment of the method for registering point cloud data. The processor 32 is configured to perform the 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 be in 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.
Memory 410 may include, for example, system memory, non-volatile storage media, and the like. The system memory stores, for example, an operating system, application programs, 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 example, instructions to perform a corresponding embodiment of at least one of the methods of registration of point cloud data. Non-volatile storage media include, but are not limited to, disk storage, optical storage, flash memory, and the like.
Processor 420 may be implemented as discrete hardware components such as a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gates or transistors, and the like. Accordingly, each of the modules, such as the judgment module and the determination module, may be implemented by a Central Processing Unit (CPU) executing instructions of the corresponding steps in the memory, or may be implemented by a dedicated circuit that performs the corresponding steps.
Bus 400 may employ any of a variety of bus architectures. For example, bus structures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, and a Peripheral Component Interconnect (PCI) bus.
Computer system 40 may also include an input-output interface 430, a network interface 440, a storage interface 450, and the like. These interfaces 430, 440, 450 and the memory 410 and processor 420 may be connected by a bus 400. The input output interface 430 may provide a connection interface for input output devices such as a display, mouse, keyboard, etc. Network interface 440 provides a connection interface for various networking devices. The storage interface 450 provides a connection interface for external storage devices such as floppy disks, U disks, SD cards, 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 instructions, which execute via the processor, create means for implementing 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 method and the device for registering the point cloud data and the computer storage medium, hardware cost is reduced, instantaneity is high, and registering efficiency is improved.
Thus far, the registration method and apparatus of the point cloud data, and the computer-readable medium according to the present disclosure have been described in detail. In order to avoid obscuring the concepts of the present disclosure, some details known in the art are not described. How to implement the solutions disclosed herein will be fully apparent to those skilled in the art from the above description.
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;
registering two adjacent frames of point cloud data for any group of 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 parameters;
for each frame of point cloud data, 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 positions of the point cloud data of each frame and the reference positions corresponding to the point cloud data of each frame is minimum.
2. The method for registering point cloud data according to claim 1, wherein 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 reference position corresponding to each frame of point cloud data is minimum comprises:
for each frame of point cloud data, constructing a residual equation set about target registration parameters based on the actual position and the reference position;
and optimizing the target registration parameters by utilizing a least square algorithm, so that the difference between the actual position of each frame of point cloud data and the reference position corresponding to each frame of point cloud data is minimum.
3. The method of registering point cloud data according to claim 1, wherein determining an actual position corresponding to each frame of point cloud data according to the first pose transformation parameter comprises:
calculating second pose transformation parameters between any frame of point cloud data and the first frame of point cloud data according to the first pose transformation parameters, wherein the second pose transformation parameters comprise a translation amount 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 quantity matrix.
4. The method of registration of point cloud data of claim 3, 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 parameters to obtain a local point cloud image;
and transforming each space point in the local point cloud image to a global coordinate system by utilizing the target registration parameters to obtain a global point cloud image.
5. The method of registration of point cloud data of claim 1, further comprising:
and performing grid sampling on the point cloud data.
6. The method of registration of point cloud data of claim 5, wherein grid sampling the point cloud data comprises:
for each frame of point cloud data, creating a three-dimensional grid index with a fixed voxel threshold, wherein each spatial 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 closest to the center 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 method of registration 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 from the adjacent point of each space point to each space point;
determining a Gaussian distribution function according to the average distance corresponding to each space point;
and filtering out space points with average distances larger than the standard deviation of the Gaussian distribution function for each frame of point cloud data.
9. The method of registration of point cloud data of claim 1, wherein the point cloud data is acquired by a lidar device.
10. The method of registration of point cloud data of claim 1, wherein registering two adjacent frames of point cloud data comprises:
and registering the two adjacent frames of point cloud data by using a forward distributed transformation method NDT.
11. A method of registering point cloud data as claimed in claim 3, wherein calculating a second pose transformation parameter between any frame of point cloud data and a 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 any frame point cloud data;
and determining a second pose transformation parameter between the point cloud data of any frame and the point cloud data of the first frame according to the product of the point cloud data of other frames of each frame and the first pose transformation parameter between the point cloud data of any frame and the point cloud data of the first frame.
12. A registration apparatus of 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 two adjacent frames of point cloud data for any group of two adjacent frames of point cloud data to obtain first pose transformation parameters between the two adjacent frames of point cloud data;
the first determining module is configured to determine an actual position corresponding to each frame of point cloud data according to the first pose transformation parameters;
the second acquisition module is configured to acquire a reference position corresponding to the time stamp according to the time stamp corresponding to each frame of point cloud data for each frame of point cloud data;
and the second determining module is configured to determine target registration parameters according to the actual positions and the reference positions of the point cloud data of each frame so as to minimize the difference between the actual positions of the point cloud data of each frame and the reference positions corresponding to the point cloud data of each frame.
13. A registration apparatus of 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 according to any of claims 1 to 11 based on instructions stored in the memory.
14. A computer-readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement a method of registering point cloud data according to any of claims 1 to 11.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911232830.6A CN111080682B (en) | 2019-12-05 | 2019-12-05 | Registration method and device for point cloud data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911232830.6A CN111080682B (en) | 2019-12-05 | 2019-12-05 | Registration method and device for point cloud data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111080682A CN111080682A (en) | 2020-04-28 |
| CN111080682B true CN111080682B (en) | 2023-09-01 |
Family
ID=70312964
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911232830.6A Active CN111080682B (en) | 2019-12-05 | 2019-12-05 | Registration method and device for point cloud data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111080682B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111882977B (en) * | 2020-05-06 | 2022-04-29 | 北京嘀嘀无限科技发展有限公司 | High-precision map construction method and system |
| CN112710318B (en) * | 2020-12-14 | 2024-05-17 | 深圳市商汤科技有限公司 | Map generation method, path planning method, electronic device, and storage medium |
| 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 |
| CN114757980B (en) * | 2022-05-12 | 2024-03-26 | 苏州轻棹科技有限公司 | Offline registration method for continuous frame point cloud data |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104236548A (en) * | 2014-09-12 | 2014-12-24 | 清华大学 | Indoor autonomous navigation method for micro unmanned aerial vehicle |
| CN104778688A (en) * | 2015-03-27 | 2015-07-15 | 华为技术有限公司 | Method and device for registering point cloud data |
| CN105184852A (en) * | 2015-08-04 | 2015-12-23 | 百度在线网络技术（北京）有限公司 | Laser-point-cloud-based urban road identification method and apparatus |
| CN107063258A (en) * | 2017-03-07 | 2017-08-18 | 重庆邮电大学 | A kind of mobile robot indoor navigation method based on semantic information |
| CN107123135A (en) * | 2017-01-06 | 2017-09-01 | 大连理工大学 | A kind of undistorted imaging method of unordered three-dimensional point cloud |
| CN107767456A (en) * | 2017-09-22 | 2018-03-06 | 福州大学 | A kind of object dimensional method for reconstructing based on RGB D cameras |
| CN108230247A (en) * | 2017-12-29 | 2018-06-29 | 达闼科技（北京）有限公司 | Generation method, device, equipment and the application program of three-dimensional map based on high in the clouds |
| CN108267141A (en) * | 2016-12-30 | 2018-07-10 | 乐视汽车（北京）有限公司 | Road Point Cloud Processing system |
| CN108303710A (en) * | 2018-06-12 | 2018-07-20 | 江苏中科院智能科学技术应用研究院 | Drawing method is built in the more scene positioning of unmanned plane based on three-dimensional laser radar |
| CN108332759A (en) * | 2018-01-12 | 2018-07-27 | 浙江国自机器人技术有限公司 | A kind of map constructing method and system based on 3D laser |
| CN108955670A (en) * | 2017-05-25 | 2018-12-07 | 百度在线网络技术（北京）有限公司 | Information acquisition method and device |
| CN109211236A (en) * | 2017-06-30 | 2019-01-15 | 沈阳新松机器人自动化股份有限公司 | navigation locating method, device and robot |
| CN109507677A (en) * | 2018-11-05 | 2019-03-22 | 浙江工业大学 | A kind of SLAM method of combination GPS and radar odometer |
| CN109848996A (en) * | 2019-03-19 | 2019-06-07 | 西安交通大学 | Extensive three-dimensional environment map creating method based on figure optimum theory |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10565457B2 (en) * | 2017-08-23 | 2020-02-18 | Tusimple, Inc. | Feature matching and correspondence refinement and 3D submap position refinement system and method for centimeter precision localization using camera-based submap and LiDAR-based global map |
-
2019
- 2019-12-05 CN CN201911232830.6A patent/CN111080682B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104236548A (en) * | 2014-09-12 | 2014-12-24 | 清华大学 | Indoor autonomous navigation method for micro unmanned aerial vehicle |
| CN104778688A (en) * | 2015-03-27 | 2015-07-15 | 华为技术有限公司 | Method and device for registering point cloud data |
| CN105184852A (en) * | 2015-08-04 | 2015-12-23 | 百度在线网络技术（北京）有限公司 | Laser-point-cloud-based urban road identification method and apparatus |
| CN108267141A (en) * | 2016-12-30 | 2018-07-10 | 乐视汽车（北京）有限公司 | Road Point Cloud Processing system |
| CN107123135A (en) * | 2017-01-06 | 2017-09-01 | 大连理工大学 | A kind of undistorted imaging method of unordered three-dimensional point cloud |
| CN107063258A (en) * | 2017-03-07 | 2017-08-18 | 重庆邮电大学 | A kind of mobile robot indoor navigation method based on semantic information |
| CN108955670A (en) * | 2017-05-25 | 2018-12-07 | 百度在线网络技术（北京）有限公司 | Information acquisition method and device |
| CN109211236A (en) * | 2017-06-30 | 2019-01-15 | 沈阳新松机器人自动化股份有限公司 | navigation locating method, device and robot |
| CN107767456A (en) * | 2017-09-22 | 2018-03-06 | 福州大学 | A kind of object dimensional method for reconstructing based on RGB D cameras |
| CN108230247A (en) * | 2017-12-29 | 2018-06-29 | 达闼科技（北京）有限公司 | Generation method, device, equipment and the application program of three-dimensional map based on high in the clouds |
| CN108332759A (en) * | 2018-01-12 | 2018-07-27 | 浙江国自机器人技术有限公司 | A kind of map constructing method and system based on 3D laser |
| CN108303710A (en) * | 2018-06-12 | 2018-07-20 | 江苏中科院智能科学技术应用研究院 | Drawing method is built in the more scene positioning of unmanned plane based on three-dimensional laser radar |
| CN109507677A (en) * | 2018-11-05 | 2019-03-22 | 浙江工业大学 | A kind of SLAM method of combination GPS and radar odometer |
| CN109848996A (en) * | 2019-03-19 | 2019-06-07 | 西安交通大学 | Extensive three-dimensional environment map creating method based on figure optimum theory |
Non-Patent Citations (1)
| Title |
|---|
| 胡健军.基于单线激光的三维场景重建算法的研究.《中国优秀硕士学位论文全文数据库 信息科技辑》.2019,(第1期),第I138-3048页. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111080682A (en) | 2020-04-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111080682B (en) | Registration method and device for point cloud data | |
| JP6862409B2 (en) | Map generation and moving subject positioning methods and devices | |
| EP3505869B1 (en) | Method, apparatus, and computer readable storage medium for updating electronic map | |
| CN109285220B (en) | Three-dimensional scene map generation method, device, equipment and storage medium | |
| US20200226782A1 (en) | Positioning method, positioning apparatus, positioning system, storage medium, and method for constructing offline map database | |
| CN110764111B (en) | Conversion method, device, system and medium of radar coordinates and geodetic coordinates | |
| CN109946701B (en) | Point cloud coordinate conversion method and device | |
| CN111161353B (en) | Vehicle positioning method, device, readable storage medium and computer equipment | |
| CN112034431B (en) | External parameter calibration method and device for radar and RTK | |
| CN113048980A (en) | Pose optimization method and device, electronic equipment and storage medium | |
| CN110795978B (en) | Road surface point cloud data extraction method and device, storage medium and electronic equipment | |
| CN110889899A (en) | Method and device for generating digital earth surface model | |
| CN116091724A (en) | Building digital twin modeling method | |
| CN117590362B (en) | Multi-laser radar external parameter calibration method, device and equipment | |
| CN113959437A (en) | Measuring method and system for mobile measuring equipment | |
| CN110322553B (en) | Method and system for lofting implementation of laser radar point cloud mixed reality scene | |
| CN115830073A (en) | Map element reconstruction method, map element reconstruction device, computer equipment and storage medium | |
| CN115790621A (en) | High-precision map updating method and device and electronic equipment | |
| KR20200032776A (en) | System for information fusion among multiple sensor platforms | |
| CN114004949A (en) | Airborne point cloud assisted mobile measurement system arrangement parameter calibration method and system | |
| CN114742141A (en) | Multi-source information data fusion studying and judging method based on ICP point cloud | |
| CN113960614A (en) | Elevation map construction method based on frame-map matching | |
| CN113503883A (en) | Method for collecting data for constructing map, storage medium and electronic equipment | |
| CN117036511B (en) | Calibration method and device for multi-type sensor, computer equipment and storage medium | |
| CN112965076B (en) | Multi-radar positioning system and method for robot |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |