Movable type and stand type combined three-dimensional scanning method, device and equipment

Abstract

The invention is suitable for the technical field of three-dimensional scanning, and provides a movable type and stand type combined three-dimensional scanning method, the method is applied to three-dimensional scanning equipment, and the method comprises the following steps: obtaining target pose data, the target pose data being pose data acquired by the three-dimensional scanning equipment in a movable shooting process; acquiring relative pose data, wherein the relative pose data is pose data acquired by the three-dimensional scanning equipment in the process of carrying out stand type shooting in the loopback detection area; and optimizing the target pose data according to the relative pose data, and obtaining three-dimensional laser point cloud data according to the optimized target pose data. Therefore, the limitation of the SLAM algorithm in mobile shooting is made up by adopting a stand type shooting mode, so that the precision and accuracy of the three-dimensional laser point cloud data are improved.

Description

Technical Field

[0001] The present application belongs to the field of three-dimensional scanning technology, and in particular, relates to a three-dimensional scanning method, device and equipment combining mobile and stand-type scanning. Background Art

[0002] 3D reconstruction technology has been widely used in digital cities, machine vision and cultural relics protection. Traditional mobile 3D scanners can obtain the corresponding initial poses through the SLAM algorithm during the scanning process, and fuse the 3D point clouds of different points together through these poses to complete the 3D data fusion of the entire shooting scene. However, due to the limitations of the SLAM algorithm, the calculated poses are unstable, resulting in large areas unable to be looped and global optimization, and the final 3D point cloud data is not accurate. Summary of the invention

[0003] The embodiments of the present application provide a mobile and stand-mounted three-dimensional scanning method, device and equipment, which can solve the problem of low accuracy of three-dimensional laser point cloud data in the prior art.

[0004] In a first aspect, an embodiment of the present application provides a mobile and stand-type combined three-dimensional scanning method, which is applied to a three-dimensional scanning device and is characterized in that the method includes: Acquire target posture data, where the target posture data is posture data collected during mobile shooting by the three-dimensional scanning device; Acquire relative posture data, where the relative posture data is posture data collected by the three-dimensional scanning device during the standing-type shooting in the loop detection area; The target posture data is optimized according to the relative posture data, and three-dimensional laser point cloud data is obtained according to the optimized target posture data.

[0005] In a possible implementation manner of the first aspect, the three-dimensional scanning device includes a laser radar, an inertial navigation module, and a visual navigation module; Before obtaining the target pose data, it also includes: Perform the first calibration on the laser radar and inertial navigation module; Perform the second calibration on the LiDAR and visual navigation modules.

[0006] In a possible implementation manner of the first aspect, obtaining target posture data includes: Acquire first candidate pose data, where the first candidate pose is calculated based on laser radar data collected by the laser radar and IMU data collected by the inertial navigation module; Acquire second candidate pose data, where the second candidate pose is calculated based on the laser radar data collected by the visual navigation module and the video stream data collected by the visual navigation module; The first candidate pose data and the second candidate pose data are fused to obtain the target pose data.

[0007] In a possible implementation manner of the first aspect, optimizing the target pose data according to the relative pose data, and obtaining three-dimensional laser point cloud data according to the optimized target pose data includes: Taking the relative posture data as an anchor point, based on a preset algorithm, globally optimize the target posture data acquired by the three-dimensional scanning device before and after the loop closure detection; Generate three-dimensional laser point cloud data based on the globally optimized target pose data.

[0008] In a second aspect, an embodiment of the present application provides a mobile and stand-type combined three-dimensional scanning device, the device comprising: A first acquisition module is used to acquire target posture data, where the target posture data is posture data collected during the mobile shooting process of the three-dimensional scanning device; A second acquisition module acquires relative posture data, where the relative posture data is posture data collected by the three-dimensional scanning device during a standing shooting process in the loop detection area; The optimization module is used to optimize the target posture data according to the relative posture data, and obtain three-dimensional laser point cloud data according to the optimized target posture data.

[0009] In a possible implementation manner of the second aspect, the three-dimensional scanning device includes a laser radar, an inertial navigation module, and a visual navigation module; The device also includes: A first calibration module, used for performing a first calibration on the laser radar and the inertial navigation module; The second calibration module is used to perform a second calibration on the laser radar and the visual navigation module.

[0010] In a possible implementation manner of the second aspect, the first acquisition module includes: A first acquisition unit is used to acquire first candidate posture data, where the first candidate posture is calculated based on laser radar data collected by the laser radar and IMU data collected by the inertial navigation module; A second acquisition unit is configured to acquire second candidate pose data, where the second candidate pose is calculated based on the laser radar data collected by the visual navigation module and the video stream data collected by the visual navigation module; A fusion unit is used to fuse the first candidate pose data and the second candidate pose data to obtain the target pose data.

[0011] In a possible implementation manner of the second aspect, the optimization module includes: A global optimization unit, used to use the relative posture data as an anchor point and, based on a preset algorithm, perform global optimization on the target posture data acquired by the three-dimensional scanning device before and after the loop closure detection; The generating unit is used to generate three-dimensional laser point cloud data according to the globally optimized target posture data.

[0012] In a third aspect, an embodiment of the present application provides a three-dimensional scanning device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the first aspect above when executing the computer program.

[0013] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect above is implemented.

[0014] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: By acquiring target posture data, the target posture data is the posture data collected during the mobile shooting of the three-dimensional scanning device; acquiring relative posture data, the relative posture data is the posture data collected during the stand-up shooting of the three-dimensional scanning device in the loop detection area; optimizing the target posture data according to the relative posture data, and obtaining three-dimensional laser point cloud data according to the optimized target posture data. It can be seen that the embodiment of the present application adopts the stand-up shooting method to make up for the limitations of the SLAM algorithm in mobile shooting, thereby improving the precision and accuracy of the three-dimensional laser point cloud data. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 is a schematic flow chart of a floor plan reconstruction method provided by the present application; Figure 2 is a schematic diagram of the structure of a floor plan reconstruction device provided in an embodiment of the present application; Figure 3It is a structural schematic diagram of the three-dimensional scanning device provided in an embodiment of the present application. Implementation

[0017] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0018] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0019] It should also be understood that the term “and / or” used in the specification and appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0020] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0021] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0022] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0023] The specific scheme of the embodiment of this application is introduced below.

[0024] See also Figure 1 , is a mobile and stand-type combined three-dimensional scanning method provided in this application, as an example but not a limitation, the method is applied to a three-dimensional scanning device, the method comprising the following steps: Step S101, obtaining target posture data.

[0025] Among them, the target posture data is the posture data collected by the three-dimensional scanning device during the mobile shooting process; the three-dimensional scanning device includes a laser radar, an inertial navigation module and a visual navigation module. The laser radar module uses the laser radar that comes with the three-dimensional scanning device. The inertial navigation module can be an inertial navigation module built into the laser radar or an inertial navigation module built into the three-dimensional scanning device. The visual navigation module can be one or more lens modules (ordinary lens, wide-angle lens, fisheye lens, etc.).

[0026] Before obtaining the target pose number, it also includes: Step S201, performing a first calibration on the laser radar and the inertial navigation module.

[0027] In specific applications, the first calibration of the laser radar and inertial navigation module includes: Step S201-1, obtain the laser three-dimensional point cloud by shooting laser radar at different angles at the same position.

[0028] Step S201-2, collecting the inclination angle of the inertial navigation unit in a stationary state, and calibrating the first external parameter of the laser radar relative to the inertial navigation module by the least square method.

[0029] It can be understood that there is a rigid body transformation between the laser radar and the inertial navigation module. By shooting the laser three-dimensional point cloud at different angles at the same position and collecting the inclination angle of the inertial navigation unit in a static state, the external parameters of the laser radar relative to the inertial navigation module are calibrated by the least squares method.

[0030] Step S202, performing a second calibration on the laser radar and the visual navigation module.

[0031] In specific applications, the laser radar and visual navigation module are calibrated for the second time, including: Step S202-1, obtain the two-dimensional image data and point cloud data obtained from the calibration plate at different positions and angles, wherein the two-dimensional image data is collected by the visual navigation module, the point cloud data is collected by the lidar, and the calibration plate is a checkerboard calibration plate with M×N corner points, and the corner points are the intersection points of the checkerboard on the calibration plate.

[0032] Step S202-2, calibrating the internal parameters of the visual navigation module based on the two-dimensional image data.

[0033] Exemplarily, the position data of each corner point in the two-dimensional image data is extracted; the angle pixel coordinates of the acquired corner point position data are matched with the standard calibration plate image; the rotation parameters of the two-dimensional image data relative to the standard calibration plate image are calculated by the SFM algorithm to obtain the internal parameters of the visual navigation module.

[0034] Step S202-3, obtain the positions of the four corner points in the point cloud data to obtain the two-dimensional chessboard corner points, and obtain the three-dimensional chessboard corner points corresponding to the two-dimensional chessboard corner points based on the pre-established matching relationship between the two-dimensional chessboard corner points and the three-dimensional chessboard corner points.

[0035] Exemplarily, the specific steps of obtaining the corner points of the two-dimensional chessboard include: Select the four corner points of the upper left, lower left, lower right, and upper right in the point cloud data counterclockwise; The plane is fitted according to the four corner points, and the positions of the projections of the corner points in the M×N point cloud data are evenly selected on the plane to obtain the two-dimensional checkerboard corner points.

[0036] Step S202-4, based on the acquired three-dimensional chessboard corner points, optimize the second external parameters of the visual navigation module and the laser radar.

[0037] Exemplarily, the specific steps of optimizing the external parameters of the visual navigation module and the laser radar include: The 3D point P_lidar generated by the lidar is obtained by rotating the extrinsic parameters to obtain the 3D point in the coordinate system of the visual navigation module, which is recorded as P_camera. Project P_camera to the image coordinate system through the internal parameters of the visual navigation module to obtain P_project, where the visual navigation module coordinate system is a three-dimensional rectangular coordinate system with its origin at the optical center of the lens, the X-axis and the Y-axis are parallel to the two sides of the image plane, the Z-axis is the optical axis of the lens, and is perpendicular to the image plane. The image coordinate system takes the center of the image as the origin, and the X-axis and the Y-axis are parallel to the two sides of the image. Optimize the external parameters of the visual navigation module and lidar through the cost function; Among them, the cost function is: .

[0038] Exemplarily, obtaining target posture data includes: Step S301, obtaining first candidate pose data.

[0039] Among them, the first candidate pose is calculated based on the lidar data collected by the lidar and the IMU data collected by the inertial navigation module.

[0040] Exemplarily, obtaining the first candidate pose data includes: aligning the lidar data and the IMU data according to the timestamp; pre-integrating the IMU data to form a series of estimated values ​​of the system state through forward propagation; using the rough state estimated in the previous step to compensate for the motion distortion of each point through back propagation, thereby achieving de-distortion of the original lidar data; using the incremental kd_tree (ikd_tree) to build a global pose map, and if the point cloud is dense, it can also be downsampled; aligning the newly scanned de-distorted original point cloud with the pose map constructed by the ikd_tree; using an iterative extended Kalman filter to estimate the state of the system; updating the pose map maintained by the ikd_tree to obtain the real-time lidar pose correction, and using the real-time lidar pose correction as the first candidate pose data.

[0041] Step S302, obtaining second candidate pose data.

[0042] The second candidate pose is calculated based on the lidar data collected by the visual navigation module and the video stream data collected by the visual navigation module.

[0043] Exemplarily, obtaining the second candidate pose data includes: The real-time pose information of the three-dimensional scanning device is calculated as the second candidate pose data through the visual inertial fusion algorithm. Specifically, the pre-integration algorithm of the SO3 manifold is used to integrate the corresponding inertial measurement data to be processed between adjacent visual key frames in the visual measurement data to be processed, and the first pose relationship between adjacent visual key frames is obtained; the feature points of the visual key frames in the visual measurement data to be processed are extracted according to the SPHORB algorithm, and the feature points are matched according to the AKAZE-FREAK algorithm. Based on the feature points of the matching relationship, the change matrix T is calculated according to the reprojection minimization principle, and the second pose relationship of each visual key frame is estimated; based on the sliding window, the first pose relationship between adjacent visual key frames and the second pose relationship of each visual key frame are motion restored to obtain the pose information of the three-dimensional scanning device as the second candidate pose data.

[0044] Step S303: Fusing the first candidate pose data and the second candidate pose data to obtain target pose data.

[0045] Exemplarily, the laser radar data and the IMU data are synchronized at the millisecond level in time, and the video stream data and the inertial navigation data are also synchronized at the millisecond level in time. After the first candidate pose data and the second candidate pose data are acquired in real time, the first pose information and the second pose information are aligned in time and then fused. Positioning fusion solves a pose so that the weighted sum of the visual residual, the inertial navigation residual, and the laser radar point cloud residual is minimized. After positioning fusion, the optimized candidate pose information is passed to the laser radar and visual navigation module, and the difference between the optimized pose and the original pose is calculated. The candidate pose information is optimized by pose propagation to obtain the target pose data.

[0046] Step S102, obtaining relative posture data.

[0047] Among them, the relative posture data is the posture data collected by the three-dimensional scanning device during the standing shooting process in the loop detection area.

[0048] Exemplarily, obtaining relative posture data includes: taking the loop interval as a key frame for frame-standing shooting and collecting data, matching the feature points of the key frame, and calculating the nearest neighbor match through the kd-tree model. The distance between the two nearest neighbor points is d1, and the distance to the second closest matching point is d2. If the ratio of the two is less than a threshold, it can be considered that the matching pair is successful. Geometric constraints are imposed on the matching pairs of the key frames to eliminate errors. The epipolar geometry is calculated for the matching pairs of each key frame, the F matrix is ​​estimated, and the matching pairs are optimized and improved through the ransac algorithm.

[0049] Geometric constraints: The F matrix can link the pixel coordinates between images in the video stream and contains the intrinsic parameter information of the camera. The pixel coordinates of each key frame must satisfy:

[0050] The RANSAC eight-point method is used to calculate the basic matrix and eliminate the matching pairs that do not meet the basic matrix. After all the matching pairs of key frames are eliminated, accurate key frames are formed as relative pose data.

[0051] Step S103, optimizing the target posture data according to the relative posture data, and obtaining three-dimensional laser point cloud data according to the optimized target posture data.

[0052] Exemplarily, optimizing the target posture data according to the relative posture data, and obtaining the three-dimensional laser point cloud data according to the optimized target posture data includes: Step S401, taking the relative posture data as an anchor point, based on a preset algorithm, globally optimize the target posture data acquired by the three-dimensional scanning device before and after the loop closure detection, wherein the preset algorithm refers to the SFM algorithm.

[0053] Step S402, generating three-dimensional laser point cloud data according to the globally optimized target pose data.

[0054] It can be understood that the SFM algorithm is used to calculate the precise relative pose before and after the loop, and the relative pose is used as an anchor point to perform global optimization of all poses and corresponding point clouds of the SLAM algorithm before and after the loop using the ICP algorithm, thereby obtaining the final accurate and high-precision three-dimensional laser point cloud data.

[0055] In the embodiment of the present application, by acquiring target posture data, the target posture data is the posture data collected during the mobile shooting of the three-dimensional scanning device; acquiring relative posture data, the relative posture data is the posture data collected during the stand-up shooting of the three-dimensional scanning device in the loop detection area; optimizing the target posture data according to the relative posture data, and obtaining three-dimensional laser point cloud data according to the optimized target posture data. It can be seen that the embodiment of the present application adopts the stand-up shooting method to make up for the limitations of the SLAM algorithm in mobile shooting, thereby improving the precision and accuracy of the three-dimensional laser point cloud data.

[0056] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0057] Corresponding to the floor plan reconstruction method described in the above embodiment, Figure 2 A structural block diagram of a mobile and stand-type three-dimensional scanning device provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0058] Reference Figure 2 , the device comprises: A first acquisition module 21 is used to acquire target posture data, where the target posture data is posture data collected during the mobile shooting process of the three-dimensional scanning device; A second acquisition module 22, acquiring relative posture data, wherein the relative posture data is posture data collected by the three-dimensional scanning device during the standing shooting process in the loop detection area; The optimization module 23 is used to optimize the target posture data according to the relative posture data, and obtain three-dimensional laser point cloud data according to the optimized target posture data.

[0059] In a possible implementation, the three-dimensional scanning device includes a laser radar, an inertial navigation module, and a visual navigation module; The device also includes: A first calibration module, used for performing a first calibration on the laser radar and the inertial navigation module; The second calibration module is used to perform a second calibration on the laser radar and the visual navigation module.

[0060] In a possible implementation, the first acquisition module includes: A first acquisition unit is used to acquire first candidate posture data, where the first candidate posture is calculated based on laser radar data collected by the laser radar and IMU data collected by the inertial navigation module; A second acquisition unit is configured to acquire second candidate pose data, where the second candidate pose is calculated based on the laser radar data collected by the visual navigation module and the video stream data collected by the visual navigation module; A fusion unit is used to fuse the first candidate pose data and the second candidate pose data to obtain the target pose data.

[0061] In a possible implementation, the optimization module includes: A global optimization unit, used to use the relative posture data as an anchor point and, based on a preset algorithm, perform global optimization on the target posture data acquired by the three-dimensional scanning device before and after the loop closure detection; The generating unit is used to generate three-dimensional laser point cloud data according to the globally optimized target posture data.

[0062] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0063] Figure 3 This is a schematic diagram of the structure of the three-dimensional scanning device provided in the embodiment of the present application. Figure 3 As shown, the three-dimensional scanning device 3 of this embodiment includes: at least one processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the at least one processor 30, and when the processor 30 executes the computer program 32, the steps in any of the above-mentioned method embodiments are implemented.

[0064] The three-dimensional scanning device 3 may be a computing device such as a cloud-based three-dimensional scanning device. The three-dimensional scanning device may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art will appreciate that Figure 3 It is only an example of the three-dimensional scanning device 3 and does not constitute a limitation on the three-dimensional scanning device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, for example, it may also include input and output devices, network access devices, etc.

[0065] The processor 30 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0066] In some embodiments, the memory 31 may be an internal storage unit of the three-dimensional scanning device 3, such as a hard disk or memory of the three-dimensional scanning device 3. In other embodiments, the memory 31 may also be an external storage device of the three-dimensional scanning device 3, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the three-dimensional scanning device 3. Further, the memory 31 may also include both an internal storage unit and an external storage device of the three-dimensional scanning device 3. The memory 31 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory 31 may also be used to temporarily store data that has been output or is to be output.

[0067] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0068] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0069] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the three-dimensional scanning device, a recording medium, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), an electric carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0070] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0071] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0072] In the embodiments provided in the present application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0073] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0074] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A mobile and stand-type combined three-dimensional scanning method, the method is applied to a three-dimensional scanning device, characterized in that: The method includes: Acquire target posture data, where the target posture data is posture data collected during mobile shooting by the three-dimensional scanning device; Acquire relative posture data, where the relative posture data is posture data collected by the three-dimensional scanning device during the standing shooting process in the loop detection area; The target posture data is optimized according to the relative posture data, and three-dimensional laser point cloud data is obtained according to the optimized target posture data.

2. The mobile and stand-type combined three-dimensional scanning method according to claim 1, characterized in that: The three-dimensional scanning device includes a laser radar, an inertial navigation module and a visual navigation module; Before obtaining the target pose data, it also includes: Perform the first calibration on the laser radar and inertial navigation module; Perform the second calibration on the LiDAR and visual navigation modules.

3. The mobile and stand-type combined three-dimensional scanning method according to claim 2, characterized in that: Get target pose data, including: Acquire first candidate pose data, where the first candidate pose is calculated based on laser radar data collected by the laser radar and IMU data collected by the inertial navigation module; Acquire second candidate pose data, where the second candidate pose is calculated based on the laser radar data collected by the visual navigation module and the video stream data collected by the visual navigation module; The first candidate pose data and the second candidate pose data are fused to obtain the target pose data.

4. The mobile and stand-type combined three-dimensional scanning method according to claim 1, characterized in that: Optimizing the target posture data according to the relative posture data, and obtaining three-dimensional laser point cloud data according to the optimized target posture data, including: Taking the relative posture data as an anchor point, based on a preset algorithm, globally optimize the target posture data acquired by the three-dimensional scanning device before and after the loop closure detection; Generate three-dimensional laser point cloud data based on the globally optimized target pose data.

5. A mobile and stand-type three-dimensional scanning device, which is applied to three-dimensional scanning equipment, characterized in that: The device includes: A first acquisition module is used to acquire target posture data, where the target posture data is posture data collected during the mobile shooting process of the three-dimensional scanning device; A second acquisition module acquires relative posture data, where the relative posture data is posture data collected by the three-dimensional scanning device during a standing shooting process in the loop detection area; The optimization module is used to optimize the target posture data according to the relative posture data, and obtain three-dimensional laser point cloud data according to the optimized target posture data.

6. The mobile and stand-type combined three-dimensional scanning device according to claim 5, characterized in that: The three-dimensional scanning device includes a laser radar, an inertial navigation module and a visual navigation module; The device also includes: A first calibration module, used for performing a first calibration on the laser radar and the inertial navigation module; The second calibration module is used to perform a second calibration on the laser radar and the visual navigation module.

7. The mobile and stand-type combined three-dimensional scanning device according to claim 6, wherein the first acquisition module comprises: A first acquisition unit is used to acquire first candidate posture data, where the first candidate posture is calculated based on laser radar data collected by the laser radar and IMU data collected by the inertial navigation module; A second acquisition unit is configured to acquire second candidate pose data, where the second candidate pose is calculated based on the laser radar data collected by the visual navigation module and the video stream data collected by the visual navigation module; A fusion unit is used to fuse the first candidate pose data and the second candidate pose data to obtain the target pose data.

8. The mobile and stand-type combined three-dimensional scanning device according to claim 5, wherein the optimization module comprises: A global optimization unit, used to use the relative posture data as an anchor point and, based on a preset algorithm, perform global optimization on the target posture data acquired by the three-dimensional scanning device before and after the loop closure detection; The generating unit is used to generate three-dimensional laser point cloud data according to the globally optimized target posture data.

9. A three-dimensional scanning device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 4 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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