WO2023000596A1 - 基于动态画幅的3d点云处理方法及装置 - Google Patents
基于动态画幅的3d点云处理方法及装置 Download PDFInfo
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- the invention relates to the technical field of laser scanning, in particular to a dynamic frame-based 3D point cloud processing method and device.
- the robot When the robot is operating, it is necessary to use the 3D point cloud corresponding to the current scene as a basis to determine the position of the grasping point of the object.
- the 3D point cloud it is usually obtained by scanning the current scene with a laser scanning device, etc., and then processing the scanned image.
- laser scanning it is easy to be disturbed by ambient light, reflected light on the surface of the measured object, etc., resulting in poor image quality parameters such as signal-to-noise ratio of the scanned image, which in turn affects the accuracy of the 3D point cloud.
- the present invention is proposed to provide a dynamic frame-based 3D point cloud processing method and device that overcomes the above problems or at least partially solves the above problems.
- a kind of 3D point cloud processing method based on dynamic frame comprises:
- each block according to the laser scanning range corresponding to the block, configure the laser scanning parameters corresponding to the block, and perform laser scanning on the block according to the laser scanning parameters to obtain the 3D point cloud of the block;
- the 3D point cloud of multiple blocks is spliced to obtain the 3D point cloud of the region of interest.
- the method further includes: collecting an image of the current scene by an image acquisition device to obtain a scene scan image of the current scene, and obtaining the scene scan image of the current scene Perform analysis to obtain image quality parameters of the scene scan image;
- Extracting the region of interest from the scene scan image of the current scene specifically includes: if the image quality parameter is smaller than a preset parameter threshold, extracting the region of interest from the scene scan image of the current scene.
- dividing the region of interest into multiple blocks, and determining the laser scanning range corresponding to each block further includes:
- the laser scanning range corresponding to each block is determined according to the position information of the block in the region of interest and the laser scanning range corresponding to the region of interest.
- laser scanning is performed on the block according to the laser scanning parameters, and the 3D point cloud of the block is obtained, which further includes:
- the rotation of the galvanometer in the laser scanning device is controlled, and the laser light reflected by the galvanometer is used to scan the block to obtain the 3D point cloud of the block.
- the laser scanning parameters include: laser scanning angle range, laser signal intensity and laser scanning speed.
- splicing the 3D point clouds of multiple blocks to obtain the 3D point clouds of the region of interest further includes:
- the 3D point clouds of any two adjacent blocks are intersected to obtain the overlapping area point cloud And the non-overlapping area point cloud; according to the point cloud quality of the overlapping area point cloud, select the target overlapping area point cloud for splicing from the overlapping area point cloud, and splice the target overlapping area point cloud and the non-overlapping area point cloud;
- a dynamic frame-based 3D point cloud processing device comprising:
- the block module is adapted to extract the region of interest from the scene scan image of the current scene, divide the region of interest into multiple blocks, and determine the laser scanning range corresponding to each block;
- the scanning module is adapted to configure laser scanning parameters corresponding to each block according to the laser scanning range corresponding to the block, and perform laser scanning on the block according to the laser scanning parameters to obtain 3D points of the block cloud;
- the splicing module is suitable for splicing 3D point clouds of multiple blocks to obtain a 3D point cloud of the region of interest.
- the device also includes:
- the acquisition module is adapted to perform image acquisition on the current scene through an image acquisition device to obtain a scene scan image of the current scene;
- the quality analysis module is suitable for analyzing the scene scan image of the current scene to obtain the image quality parameters of the scene scan image
- the blocking module is further adapted to: if the image quality parameter is smaller than the preset parameter threshold, extract the region of interest from the scene scan image of the current scene.
- the chunking module is further adapted to:
- the laser scanning range corresponding to each block is determined according to the position information of the block in the region of interest and the laser scanning range corresponding to the region of interest.
- the scanning module is further adapted to:
- the rotation of the galvanometer in the laser scanning device is controlled, and the laser light reflected by the galvanometer is used to scan the block to obtain the 3D point cloud of the block.
- the laser scanning parameters include: laser scanning angle range, laser signal intensity and laser scanning speed.
- the splicing module is further suitable for:
- the 3D point clouds of any two adjacent blocks are intersected to obtain the overlapping area point cloud And the non-overlapping area point cloud; according to the point cloud quality of the overlapping area point cloud, select the target overlapping area point cloud for splicing from the overlapping area point cloud, and splice the target overlapping area point cloud and the non-overlapping area point cloud;
- a computing device including: a processor, a memory, a communication interface, and a communication bus, and the processor, the memory, and the communication interface complete mutual communication through the communication bus;
- the memory is used to store at least one executable instruction, and the executable instruction causes the processor to execute the operations corresponding to the above dynamic frame-based 3D point cloud processing method.
- a computer storage medium is provided, and at least one executable instruction is stored in the storage medium, and the executable instruction causes a processor to perform operations corresponding to the above-mentioned dynamic frame-based 3D point cloud processing method.
- the region of interest in the scene scanning image is divided into multiple blocks, and a part of the range is intercepted from the total laser scanning range of the laser scanning device as the laser scanning range corresponding to each block, realizing dynamic Frame: According to the laser scanning range corresponding to each block, configure the laser scanning parameters corresponding to each block, and perform laser scanning on the block according to the laser scanning parameters, so that the laser energy is concentrated per unit time, which helps to obtain better results.
- the laser scanning effect effectively improves the signal-to-noise ratio; by splicing multiple 3D point clouds, the 3D point cloud of the region of interest can be easily obtained, and the accuracy of the 3D point cloud is effectively improved , which improves the quality of point cloud and optimizes the point cloud processing method.
- Fig. 1 shows a schematic flow chart of a 3D point cloud processing method based on a dynamic frame according to an embodiment of the present invention
- Fig. 2 shows a structural block diagram of a 3D point cloud processing device based on a dynamic frame according to an embodiment of the present invention
- Fig. 3 shows a schematic structural diagram of a computing device according to an embodiment of the present invention.
- Fig. 1 shows a schematic flow chart of a 3D point cloud processing method based on a dynamic frame according to an embodiment of the present invention. As shown in Fig. 1, the method includes the following steps:
- Step S101 extract the region of interest from the scene scan image of the current scene, divide the region of interest into multiple blocks, and determine the laser scanning range corresponding to each block.
- the image of the current scene may be collected by an image collection device such as 2D/3D to obtain a scene scan image of the current scene.
- the scene scan image may be a 2D image or a 3D image, which is not limited here. Considering that if the image quality parameter of the scene scan image is relatively good and it is a 3D image, then the 3D point cloud can be obtained directly according to the scene scan image, and there is no need to perform 3D point cloud processing based on the dynamic frame mode.
- the dynamic frame refers to dynamically intercepting part of the range from the total laser scanning range of the laser scanning device as the current laser scanning range, that is, as the laser scanning range corresponding to each block; It is necessary to analyze the scene scan image of the current scene, for example, analyzing the edge clarity in the scene scan image, the lack of midpoint in the image, etc., to obtain the image quality parameters of the scene scan image.
- the image quality parameter may include at least one of contrast, signal-to-noise ratio, edge sharpness, average brightness, histogram, and the like.
- the image quality parameter of the scene scan image is less than the preset parameter threshold; if the image quality parameter is less than the preset parameter threshold, it means that the image quality of the scene scan image is poor, and the 3D point cloud processing is performed based on the dynamic frame method , then perform step S101 to extract the region of interest (Region of Interest, ROI) from the scene scan image of the current scene; if the image quality parameter is greater than or equal to the preset parameter threshold, it indicates that the image quality of the scene scan image is better, directly based on 3D
- the 3D point cloud can be obtained from the scanned image of the scene, and there is no need to process the 3D point cloud based on the dynamic frame, and the method ends.
- those skilled in the art can set the preset parameter threshold according to actual needs, which is not limited here.
- the region of interest may be extracted from the scene scan image of the current scene according to the scanning requirements of the current scene. If the current scene needs to scan stacked containers such as pallets, material baskets, and cage cars, the area of the stacked container is extracted from the scanned image of the scene as the region of interest.
- stacked containers such as pallets, material baskets, and cage cars
- the laser scanning range corresponding to the region of interest can be determined according to the setting parameters of the laser scanning device.
- the laser scanning device may specifically be a 3D laser camera, and the setting parameters of the laser scanning device include the setting position of the laser scanning device, the total range of laser scanning and other parameters.
- the laser scanning device may be arranged at an upper position, such as directly above or obliquely above, for scanning information of the current scene.
- the laser scanning range corresponding to the region of interest may be determined according to the position information of the region of interest in the scene scanning image and the setting parameters of the laser scanning device.
- the laser scanning range corresponding to the region of interest is smaller than the total laser scanning range, and the laser scanning range can be specifically represented by a laser scanning angle range.
- the block parameter includes the number of blocks and overlapping ratio, etc., and the block parameter may be preset, or may be automatically calculated according to the image quality parameters of the scanned image of the scene.
- the laser scanning range corresponding to the region of interest is equivalent to intercepting a part of the laser scanning range of the laser scanning device as the laser scanning range corresponding to each block, and only scans the information in one block during one laser scanning process .
- the number of blocks is 4 and the overlap rate is 5%, it means that the region of interest needs to be divided into 4 blocks, and 5% of the areas of two adjacent blocks overlap.
- the four blocks are block 1, block 2, block 3 and block 4 in turn, and there is a 5% difference between block 1 and block 2.
- the areas are overlapping, 5% of the areas of block 2 and block 3 are overlapped, and 5% of the areas of block 3 and block 4 are overlapped.
- Step S102 for each block, configure laser scanning parameters corresponding to the block according to the laser scanning range corresponding to the block, and perform laser scanning on the block according to the laser scanning parameters to obtain a 3D point cloud of the block.
- the laser scanning parameters corresponding to the block can be configured for each block according to the laser scanning range corresponding to the block, wherein the laser scanning parameters include: laser The scanning angle range, laser signal intensity, laser scanning speed, and laser scanning parameters may also include other parameters, which are not limited here. In practical applications, in order to obtain a better scanning effect, a slower laser scanning speed can be used for scanning, so as to concentrate the laser energy per unit time and improve the signal-to-noise ratio.
- the laser scanning equipment includes a laser light source and a vibrating mirror based on MEMS (Micro-Electro-Mechanical System, micro-electromechanical system) technology, etc., wherein the vibrating mirror includes a vibrating mirror motor, and the vibrating mirror motor is also connected to a reflector.
- the vibrating mirror motor rotates according to the instructions of the laser scanning device, and the rotation of the vibrating mirror motor drives the mirror mirror connected to it to rotate, thereby adjusting the position of the mirror mirror.
- the rotation of the vibrating mirror in the laser scanning device can be controlled according to the laser scanning parameters corresponding to the block, and the laser reflected by the vibrating mirror can be used to scan the block to obtain the 3D point of the block cloud.
- the 3D point cloud includes pose information of each 3D point, and the pose information of each 3D point may specifically include information such as the coordinate value of each 3D point in the XYZ three-axis in space and the XYZ three-axis direction of each 3D point itself.
- Step S103 splicing the 3D point clouds of multiple blocks to obtain the 3D point cloud of the region of interest.
- each block is separately scanned by laser, what is obtained is the 3D point cloud of each block, not the 3D point cloud of the complete region of interest.
- the 3D point cloud is spliced to obtain the 3D point cloud of the region of interest.
- the overlapping area corresponds to two sets of 3D point clouds. Select a set of 3D point clouds with better point cloud quality from the set of 3D point clouds for splicing.
- the intersection processing is performed on the 3D point clouds of the two adjacent blocks, Obtain overlapping area point clouds and non-overlapping area point clouds; analyze the point cloud quality of overlapping area point clouds, such as analyzing point cloud noise ratio, point cloud density, point cloud thickness, and point cloud overlap, etc., to obtain overlapping area point clouds point cloud quality.
- point cloud noise is gross error, which can be divided into point-like gross error and cluster-like gross error from the spatial distribution; point cloud density refers to the density of laser data points.
- the point cloud thickness refers to the error of the point cloud elevation in the flat area of the 3D point cloud to be analyzed
- the point cloud overlap refers to the convex polygon of the 3D point cloud to be analyzed and the difference between the adjacent point cloud The ratio of the area where the convex polygons of the flight strips intersect to the convex polygon of the flight strips of the 3D point cloud to be evaluated.
- the target overlapping area point cloud After analyzing the point cloud quality of the overlapping area point cloud, according to the point cloud quality of the overlapping area point cloud, select the target overlapping area point cloud for splicing from the overlapping area point cloud, wherein the target overlapping area point cloud is two sets A set of 3D point clouds with better point cloud quality in the point cloud of the overlapping area; the point cloud of the target overlapping area and the point cloud of the non-overlapping area are spliced, and specifically, the 3D point cloud fusion process can be performed to complete the splicing.
- the splicing processing of all the segmented 3D point clouds is completed, so as to obtain the 3D point cloud of the region of interest.
- the region of interest in the scene scanning image is divided into multiple blocks, and a part of the range is intercepted from the total range of laser scanning of the laser scanning device as the corresponding block.
- the laser scanning range corresponding to each block configure the laser scanning parameters corresponding to each block, and perform laser scanning on the block according to the laser scanning parameters, so that the laser energy per unit time Concentration helps to obtain a better laser scanning effect and effectively improves the signal-to-noise ratio; by splicing multiple 3D point clouds of blocks, the 3D point cloud of the region of interest can be easily obtained, and effectively
- the accuracy of 3D point cloud is improved, the quality of point cloud is improved, and the processing method of point cloud is optimized.
- FIG. 2 shows a structural block diagram of a dynamic frame-based 3D point cloud processing device according to an embodiment of the present invention. As shown in FIG.
- the blocking module 210 is adapted to: extract the region of interest from the scene scan image of the current scene, divide the region of interest into multiple blocks, and determine the laser scanning range corresponding to each block.
- the scanning module 220 is adapted to: for each block, according to the laser scanning range corresponding to the block, configure the laser scanning parameters corresponding to the block, perform laser scanning on the block according to the laser scanning parameters, and obtain the 3D image of the block. point cloud.
- the splicing module 230 is suitable for: splicing the multiple segmented 3D point clouds to obtain the 3D point cloud of the region of interest.
- the device further includes: an acquisition module 240, adapted to acquire an image of the current scene through an image acquisition device to obtain a scene scan image of the current scene; a quality analysis module 250, adapted to analyze the scene scan image of the current scene , to get the image quality parameters of the scene scan image. Then the blocking module 210 is further adapted to: if the image quality parameter is smaller than the preset parameter threshold, extract the region of interest from the scene scan image of the current scene.
- the block module 210 is further adapted to: determine the laser scanning range corresponding to the region of interest according to the setting parameters of the laser scanning device; obtain the block parameters, and divide the region of interest into multiple blocks according to the block parameters , and record the position information of each block in the region of interest; for each block, according to the position information of the block in the region of interest and the laser scanning range corresponding to the region of interest, determine the corresponding laser scanning range.
- the scanning module 220 is further adapted to: control the rotation of the vibrating mirror in the laser scanning device according to the laser scanning parameters, and use the laser reflected by the vibrating mirror to perform laser scanning on the block to obtain the 3D point cloud of the block.
- the laser scanning parameters include: laser scanning angle range, laser signal intensity and laser scanning speed.
- the splicing module 230 is further adapted to: for the 3D point cloud of any two adjacent blocks, according to the position information of the two adjacent blocks in the region of interest, the 3D point cloud of the two adjacent blocks According to the point cloud quality of the overlapping area point cloud, the target overlapping area point cloud for splicing is selected from the overlapping area point cloud, and the target overlapping area point cloud is The cloud is spliced with the point cloud of the non-overlapping area; the 3D point cloud of the area of interest is obtained.
- the region of interest in the scene scanning image is divided into multiple blocks, and part of the range is intercepted from the total range of laser scanning of the laser scanning device as the corresponding block.
- the laser scanning range corresponding to each block configure the laser scanning parameters corresponding to each block, and perform laser scanning on the block according to the laser scanning parameters, so that the laser energy per unit time Concentration helps to obtain a better laser scanning effect and effectively improves the signal-to-noise ratio; by splicing multiple 3D point clouds of blocks, the 3D point cloud of the region of interest can be easily obtained, and effectively
- the accuracy of 3D point cloud is improved, the quality of point cloud is improved, and the processing method of point cloud is optimized.
- the present invention also provides a non-volatile computer storage medium.
- the computer storage medium stores at least one executable instruction, and the executable instruction can execute the dynamic frame-based 3D point cloud processing method in any method embodiment above.
- FIG. 3 shows a schematic structural diagram of a computing device according to an embodiment of the present invention, and the specific embodiment of the present invention does not limit the specific implementation of the computing device.
- the computing device may include: a processor (processor) 302, a communication interface (Communications Interface) 304, a memory (memory) 306, and a communication bus 308.
- processor processor
- communication interface Communication Interface
- memory memory
- the processor 302 , the communication interface 304 , and the memory 306 communicate with each other through the communication bus 308 .
- the communication interface 304 is used to communicate with network elements of other devices such as clients or other servers.
- the processor 302 is configured to execute the program 310, and specifically, may execute relevant steps in the above embodiment of the dynamic frame-based 3D point cloud processing method.
- the program 310 may include program codes including computer operation instructions.
- the processor 302 may be a central processing unit CPU, or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present invention.
- the one or more processors included in the computing device may be of the same type, such as one or more CPUs, or may be different types of processors, such as one or more CPUs and one or more ASICs.
- the memory 306 is used to store the program 310 .
- the memory 306 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.
- the program 310 may be specifically configured to enable the processor 302 to execute the dynamic frame-based 3D point cloud processing method in any of the above method embodiments.
- each step in the program 310 refer to the corresponding description of the corresponding steps and units in the above-mentioned embodiment of dynamic frame-based 3D point cloud processing, and details are not repeated here.
- the specific working process of the above-described devices and modules can refer to the corresponding process description in the foregoing method embodiments, and details are not repeated here.
- modules in the device in the embodiment can be adaptively changed and arranged in one or more devices different from the embodiment.
- Modules or units or components in the embodiments may be combined into one module or unit or component, and furthermore may be divided into a plurality of sub-modules or sub-units or sub-assemblies.
- All features disclosed in this specification including accompanying claims, abstract and drawings) and any method or method so disclosed may be used in any combination, except that at least some of such features and/or processes or units are mutually exclusive. All processes or units of equipment are combined.
- Each feature disclosed in this specification may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
- the various component embodiments of the present invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof.
- a microprocessor or a digital signal processor (DSP) may be used in practice to implement some or all functions of some or all components in the embodiments of the present invention.
- DSP digital signal processor
- the present invention can also be implemented as an apparatus or an apparatus program (for example, a computer program and a computer program product) for performing a part or all of the methods described herein.
- Such a program for realizing the present invention may be stored on a computer-readable medium, or may be in the form of one or more signals.
- Such a signal may be downloaded from an Internet site, or provided on a carrier signal, or provided in any other form.
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Abstract
本发明公开了一种基于动态画幅的3D点云处理方法及装置，其中，该方法包括：从当前场景的场景扫描图像中提取感兴趣区域，将感兴趣区域划分成多个分块，并确定每个分块对应的激光扫描范围；针对每个分块，根据该分块对应的激光扫描范围，配置该分块对应的激光扫描参数，依据激光扫描参数对该分块进行激光扫描，得到该分块的3D点云；对多个分块的3D点云进行拼接处理，得到感兴趣区域的3D点云。该方案从激光扫描设备的激光扫描总范围中截取部分范围作为各个分块对应的激光扫描范围，对各个分块进行激光扫描，有效地提高了信噪比，通过对多个分块的3D点云进行拼接，能够便捷地得到感兴趣区域的3D点云，有效地提高了3D点云的精准度。
Description
优先权声明
本申请要求2021年7月22日递交的、申请号为CN202110832563.7、名称为“基于动态画幅的3D点云处理方法及装置”的中国发明专利的优先权，上述专利的所有内容在此全部引入。
本发明涉及激光扫描技术领域，具体涉及一种基于动态画幅的3D点云处理方法及装置。
随着工业智能化的发展，通过机器人代替人工对物体(例如工业零件、箱体等)进行操作的情况越来越普及。在机器人操作时，需要以当前场景对应的3D点云作为依据，从而确定物体的抓取点位置等。对于3D点云，通常是利用激光扫描设备等对当前场景进行扫描，而后对扫描得到的图像进行处理而得到。然而，在激光扫描的过程中，很容易受到环境光、被测物体表面反射光等干扰，导致扫描图像的例如信噪比等图像质量参数较差，进而影响3D点云的精准度。
发明内容
鉴于上述问题，提出了本发明以便提供一种克服上述问题或者至少部分地解决上述问题的基于动态画幅的3D点云处理方法及装置。
根据本发明的一个方面，提供了一种基于动态画幅的3D点云处理方法，该方法包括：
从当前场景的场景扫描图像中提取感兴趣区域，将感兴趣区域划分成多个分块，并确定每个分块对应的激光扫描范围；
针对每个分块，根据该分块对应的激光扫描范围，配置该分块对应的激光扫描参数，依据激光扫描参数对该分块进行激光扫描，得到该分块的3D点云；
对多个分块的3D点云进行拼接处理，得到感兴趣区域的3D点云。
进一步地，在从当前场景的场景扫描图像中提取感兴趣区域之前，该方法还包括：通过图像采集设备对当前场景进行图像采集，得到当前场景的场景扫描图像，并对当前场景的场景扫描图像进行分析，得到场景扫描图像的图像质量参数；
从当前场景的场景扫描图像中提取感兴趣区域具体为：若图像质量参数小于预设参数阈值，则从当前场景的场景扫描图像中提取感兴趣区域。
进一步地，将感兴趣区域划分成多个分块，并确定每个分块对应的激光扫描范围进一步包括：
根据激光扫描设备的设置参数，确定感兴趣区域对应的激光扫描范围；
获取分块参数，按照分块参数，将感兴趣区域划分成多个分块，并记录每个分块在感兴趣区域中的位置信息；
针对每个分块，根据该分块在感兴趣区域中的位置信息以及感兴趣区域对应的激光扫描范围，确定每个分块对应的激光扫描范围。
进一步地，依据激光扫描参数对该分块进行激光扫描，得到该分块的3D点云进一步包括：
依据激光扫描参数，控制激光扫描设备中振镜的转动，利用振镜反射出的激光对该分块进行激光扫描，得到该分块的3D点云。
进一步地，激光扫描参数包括：激光扫描角度范围、激光信号强度以及激光扫描速度。
进一步地，对多个分块的3D点云进行拼接处理，得到感兴趣区域的3D点云进一步包括：
针对任两个相邻分块的3D点云，根据两个相邻分块在感兴趣区域中的位置信息，对两个相邻分块的3D点云进行取交集处理，得到重叠区域点云以及非重叠区域点云；依据重叠区域点云的点云质量，从重叠区域点云中选择用于拼接的目标重叠区域点云，将目标重叠区域点云与非重叠区域点云进行拼接处理；
得到感兴趣区域的3D点云。
根据本发明的另一方面，提供了一种基于动态画幅的3D点云处理装置，该装置包括：
分块模块，适于从当前场景的场景扫描图像中提取感兴趣区域，将感兴趣区域划分成多个分块，并确定每个分块对应的激光扫描范围；
扫描模块，适于针对每个分块，根据该分块对应的激光扫描范围，配置该分块对应的激光扫描参数，依据激光扫描参数对该分块进行激光扫描，得到该分块的3D点云；
拼接模块，适于对多个分块的3D点云进行拼接处理，得到感兴趣区域的3D点云。
进一步地，该装置还包括：
采集模块，适于通过图像采集设备对当前场景进行图像采集，得到当前场景的场景扫描图像；
质量分析模块，适于对当前场景的场景扫描图像进行分析，得到场景扫描图像的图像质量参数；
分块模块进一步适于：若图像质量参数小于预设参数阈值，则从当前场景的场景扫描图像中提取感兴趣区域。
进一步地，分块模块进一步适于：
根据激光扫描设备的设置参数，确定感兴趣区域对应的激光扫描范围；
获取分块参数，按照分块参数，将感兴趣区域划分成多个分块，并记录每个分块在感兴趣区域中的位置信息；
针对每个分块，根据该分块在感兴趣区域中的位置信息以及感兴趣区域对应的激光扫描范围，确定每个分块对应的激光扫描范围。
进一步地，扫描模块进一步适于：
依据激光扫描参数，控制激光扫描设备中振镜的转动，利用振镜反射出的激光对该分块进行激光扫描，得到该分块的3D点云。
进一步地，激光扫描参数包括：激光扫描角度范围、激光信号强度以及激光扫描速度。
进一步地，拼接模块进一步适于：
针对任两个相邻分块的3D点云，根据两个相邻分块在感兴趣区域中的位置信息，对两个相邻分块的3D点云进行取交集处理，得到重叠区域点云以及非重叠区域点云；依据重叠区域点云的点云质量，从重叠区域点云中选择用于拼接的目标重叠区域点云，将目标重叠区域点云与非重叠区域点云进行拼接处理；
得到感兴趣区域的3D点云。
根据本发明的又一方面，提供了一种计算设备，包括：处理器、存储器、通信接口和通信总线，处理器、存储器和通信接口通过通信总线完成相互间的通信；
存储器用于存放至少一可执行指令，可执行指令使处理器执行上述基于动态画幅的3D点云处理方法对应的操作。
根据本发明的再一方面，提供了一种计算机存储介质，存储介质中存储有至少一可执行指令，可执行指令使处理器执行如上述基于动态画幅的3D点云处理方法对应的操作。
根据本发明提供的技术方案，将场景扫描图像中的感兴趣区域划分成多个分块，从激光扫描设备的激光扫描总范围中截取部分范围作为各个分块对应的激光扫描范围，实现了动态画幅；根据每个分块对应的激光扫描范围，配置每个分块对应的激光扫描参数，依据激光扫描参数对该分块进行激光扫描，使单位时间内激光能量集中，有助于获得较好的激光扫描效果，有效地提高了信噪比；通过对多个分块的3D点云进行拼接，即可便捷地得到感兴趣区域的3D点云，并且有效地提高了3D点云的精准度，提升了点云质量，优化了点云处理方式。
上述说明仅是本发明技术方案的概述，为了能够更清楚了解本发明的技术手段，而可依照说明书的内容予以实施，并且为了让本发明的上述和其它目的、特征和优点能够更明显易懂，以下特举本发明的具体实施方式。
通过阅读下文优选实施方式的详细描述，各种其他的优点和益处对于本领域普通技术人员将变得清楚明了。附图仅用于示出优选实施方式的目的，而并不认为是对本发明的限制。而且在整个附图中，用相同的参考符号表示相同的部件。在附图中：
图1示出了根据本发明一个实施例的基于动态画幅的3D点云处理方法的流程示意图；
图2示出了根据本发明一个实施例的基于动态画幅的3D点云处理装置的结构框图；
图3示出了根据本发明实施例的一种计算设备的结构示意图。
下面将参照附图更详细地描述本公开的示例性实施例。虽然附图中显示了本公开的示例性实施例，然而应当理解，可以以各种形式实现本公开而不应被这里阐述的实施例所限制。相反，提供这些实施例是为了能够更透彻地理解本公开，并且能够将本公开的范围完整的传达给本领域的技术人员。
图1示出了根据本发明一个实施例的基于动态画幅的3D点云处理方法的流程示意图， 如图1所示，该方法包括如下步骤：
步骤S101，从当前场景的场景扫描图像中提取感兴趣区域，将感兴趣区域划分成多个分块，并确定每个分块对应的激光扫描范围。
其中，可通过2D/3D等图像采集设备对当前场景进行图像采集，得到当前场景的场景扫描图像，场景扫描图像可以为2D图像，也可以为3D图像，此处不做限定。考虑到如果场景扫描图像的图像质量参数已经比较好了且为3D图像，则可直接依据场景扫描图像得到3D点云，无需再基于动态画幅的方式进行3D点云处理，在本实施例中，动态画幅是指从激光扫描设备的激光扫描总范围中动态地截取部分范围作为当前激光扫描范围，也就是作为各个分块对应的激光扫描范围；那么在得到了当前场景的场景扫描图像之后，还需对当前场景的场景扫描图像进行分析，例如分析场景扫描图像中的边缘清晰情况、图像中点的缺失情况等，得到场景扫描图像的图像质量参数。图像质量参数可包括对比度、信噪比、边缘锐利度、平均亮度、直方图等中的至少一种。
具体地，可判断场景扫描图像的图像质量参数是否小于预设参数阈值；若图像质量参数小于预设参数阈值，说明场景扫描图像的图像质量较差，则基于动态画幅的方式进行3D点云处理，那么执行步骤S101从当前场景的场景扫描图像中提取感兴趣区域(Region of Interest，ROI)；若图像质量参数大于或等于预设参数阈值，说明场景扫描图像的图像质量较好，直接依据3D的场景扫描图像得到3D点云即可，无需再基于动态画幅的方式进行3D点云处理，则该方法结束。其中，本领域技术人员可根据实际需要设置预设参数阈值，此处不做限定。
在步骤S101中，可根据当前场景的扫描需求等，从当前场景的场景扫描图像中提取感兴趣区域。若当前场景是需要对托盘、料筐、笼车等码放容器进行扫描，则从场景扫描图像中提取码放容器区域作为感兴趣区域。
在确定了感兴趣区域之后，可根据激光扫描设备的设置参数，确定感兴趣区域对应的激光扫描范围。其中，激光扫描设备具体可为3D激光相机，激光扫描设备的设置参数包括激光扫描设备的设置位置、激光扫描总范围等参数。其中，激光扫描设备可设置于上方位置处，例如正上方或者斜上方位置处，用于扫描当前场景的信息。具体地，可根据感兴趣区域在场景扫描图像中的位置信息以及激光扫描设备的设置参数，确定感兴趣区域对应的激光扫描范围。感兴趣区域对应的激光扫描范围小于激光扫描总范围，激光扫描范围具体可以以激光扫描角度范围进行表示。
接着获取分块参数，按照分块参数，将感兴趣区域划分成多个分块，为了便于确定每个分块对应的激光扫描范围以及便于后续进行点云拼接，在本实施例中，还需记录每个分块在感兴趣区域中的位置信息。其中，分块参数包括分块数量以及重叠率等，分块参数可为预先设置的，也可为根据场景扫描图像的图像质量参数等自动计算得到的。然后针对每个分块，根据该分块在感兴趣区域中的位置信息以及感兴趣区域对应的激光扫描范围，确定每个分块对应的激光扫描范围，每个分块对应的激光扫描范围小于感兴趣区域对应的激光扫描范围，相当于从激光扫描设备的激光扫描总范围中截取部分范围作为各个分块对应的激光扫描范围，在一次激光扫描过程中仅对一个分块中的信息进行扫描。
例如当分块数量为4，重叠率为5％时，说明需要将感兴趣区域划分成4个分块，相邻两个分块存在5％的区域是重叠的。按照预设方向(如从左到右的方向)，这4个分块依次为分块1、分块2、分块3和分块4，其中，分块1和分块2存在5％的区域是重叠的，分块2和分块3存在5％的区域是重叠的，分块3和分块4存在5％的区域是重叠的。
步骤S102，针对每个分块，根据该分块对应的激光扫描范围，配置该分块对应的激光扫描参数，依据激光扫描参数对该分块进行激光扫描，得到该分块的3D点云。
在确定了每个分块对应的激光扫描范围之后，即可针对每个分块，根据该分块对应的激光扫描范围，配置该分块对应的激光扫描参数，其中，激光扫描参数包括：激光扫描角度范围、激光信号强度以及激光扫描速度，激光扫描参数还可包括其他参数，此处不做限定。在实际应用中，为了获得较好的扫描效果，可采用较慢的激光扫描速度进行扫描，以使单位时间内激光能量集中，提高信噪比。
激光扫描设备包括激光光源以及基于MEMS(Micro-Electro-Mechanical System，微机电系统)工艺的振镜等，其中，振镜包括振镜电机，振镜电机上还连接有反射镜片。振镜电机根据激光扫描设备的指令进行转动，振镜电机的转动带动其所连接的反射镜片进行转动，从而调整反射镜片的位置。针对每个分块，可依据该分块对应的激光扫描参数，控制激光扫描设备中振镜的转动，利用振镜反射出的激光对该分块进行激光扫描，从而得到该分块的3D点云。3D点云包括各个3D点的位姿信息，各个3D点的位姿信息具体可包括各个3D点在空间的XYZ三轴的坐标值以及各个3D点自身的XYZ三轴方向等信息。
步骤S103，对多个分块的3D点云进行拼接处理，得到感兴趣区域的3D点云。
由于在本实施例中是单独对各个分块进行了激光扫描，所得到的是各个分块的3D点云，并不是完整的感兴趣区域的3D点云，因此还需对多个分块的3D点云进行拼接，以得 到感兴趣区域的3D点云。
考虑到在分块过程中是依据重叠率进行分块的，使得两个相邻分块的3D点云存在重叠区域，也就是说，该重叠区域对应有两套3D点云，需要从这两套3D点云中选择一套点云质量较优的3D点云用于拼接。
具体地，针对任两个相邻分块的3D点云，根据这两个相邻分块在感兴趣区域中的位置信息，对这两个相邻分块的3D点云进行取交集处理，得到重叠区域点云以及非重叠区域点云；对重叠区域点云的点云质量进行分析，例如分析点云噪声比、点云密度、点云厚度和点云重叠度等，得到重叠区域点云的点云质量。其中，点云噪声即粗差，从空间分布上可以分为点状粗差和簇状粗差；点云密度是指激光数据点的密度，随着激光扫描技术的发展，每平方米可达上百个点；点云厚度是指待分析的3D点云中平坦区域中点云高程的误差；点云重叠度是指待分析的3D点云的航带的凸多边形和相邻点云的航带的凸多边形相交的区域面积与待评价的3D点云的航带的凸多边形的比值。
在分析得到重叠区域点云的点云质量之后，依据重叠区域点云的点云质量，从重叠区域点云中选择用于拼接的目标重叠区域点云，其中，目标重叠区域点云为两套重叠区域点云中点云质量较优的一套3D点云；将目标重叠区域点云与非重叠区域点云进行拼接处理，具体地可进行3D点云融合处理完成拼接。按照上述处理方式，完成对所有分块的3D点云的拼接处理，从而得到感兴趣区域的3D点云。
根据本实施例提供的基于动态画幅的3D点云处理方法，将场景扫描图像中的感兴趣区域划分成多个分块，从激光扫描设备的激光扫描总范围中截取部分范围作为各个分块对应的激光扫描范围，实现了动态画幅；根据每个分块对应的激光扫描范围，配置每个分块对应的激光扫描参数，依据激光扫描参数对该分块进行激光扫描，使单位时间内激光能量集中，有助于获得较好的激光扫描效果，有效地提高了信噪比；通过对多个分块的3D点云进行拼接，即可便捷地得到感兴趣区域的3D点云，并且有效地提高了3D点云的精准度，提升了点云质量，优化了点云处理方式。
图2示出了根据本发明一个实施例的基于动态画幅的3D点云处理装置的结构框图，如图2所示，该装置包括：分块模块210、扫描模块220和拼接模块230。
分块模块210适于：从当前场景的场景扫描图像中提取感兴趣区域，将感兴趣区域划分成多个分块，并确定每个分块对应的激光扫描范围。
扫描模块220适于：针对每个分块，根据该分块对应的激光扫描范围，配置该分块对 应的激光扫描参数，依据激光扫描参数对该分块进行激光扫描，得到该分块的3D点云。
拼接模块230适于：对多个分块的3D点云进行拼接处理，得到感兴趣区域的3D点云。
可选地，该装置还包括：采集模块240，适于通过图像采集设备对当前场景进行图像采集，得到当前场景的场景扫描图像；质量分析模块250，适于对当前场景的场景扫描图像进行分析，得到场景扫描图像的图像质量参数。那么分块模块210进一步适于：若图像质量参数小于预设参数阈值，则从当前场景的场景扫描图像中提取感兴趣区域。
可选地，分块模块210进一步适于：根据激光扫描设备的设置参数，确定感兴趣区域对应的激光扫描范围；获取分块参数，按照分块参数，将感兴趣区域划分成多个分块，并记录每个分块在感兴趣区域中的位置信息；针对每个分块，根据该分块在感兴趣区域中的位置信息以及感兴趣区域对应的激光扫描范围，确定每个分块对应的激光扫描范围。
可选地，扫描模块220进一步适于：依据激光扫描参数，控制激光扫描设备中振镜的转动，利用振镜反射出的激光对该分块进行激光扫描，得到该分块的3D点云。其中，激光扫描参数包括：激光扫描角度范围、激光信号强度以及激光扫描速度。
可选地，拼接模块230进一步适于：针对任两个相邻分块的3D点云，根据两个相邻分块在感兴趣区域中的位置信息，对两个相邻分块的3D点云进行取交集处理，得到重叠区域点云以及非重叠区域点云；依据重叠区域点云的点云质量，从重叠区域点云中选择用于拼接的目标重叠区域点云，将目标重叠区域点云与非重叠区域点云进行拼接处理；得到感兴趣区域的3D点云。
根据本实施例提供的基于动态画幅的3D点云处理装置，将场景扫描图像中的感兴趣区域划分成多个分块，从激光扫描设备的激光扫描总范围中截取部分范围作为各个分块对应的激光扫描范围，实现了动态画幅；根据每个分块对应的激光扫描范围，配置每个分块对应的激光扫描参数，依据激光扫描参数对该分块进行激光扫描，使单位时间内激光能量集中，有助于获得较好的激光扫描效果，有效地提高了信噪比；通过对多个分块的3D点云进行拼接，即可便捷地得到感兴趣区域的3D点云，并且有效地提高了3D点云的精准度，提升了点云质量，优化了点云处理方式。
本发明还提供了一种非易失性计算机存储介质，计算机存储介质存储有至少一可执行指令，可执行指令可执行上述任意方法实施例中的基于动态画幅的3D点云处理方法。
图3示出了根据本发明实施例的一种计算设备的结构示意图，本发明具体实施例并不对计算设备的具体实现做限定。
如图3所示，该计算设备可以包括：处理器(processor)302、通信接口(Communications Interface)304、存储器(memory)306、以及通信总线308。
其中：
处理器302、通信接口304、以及存储器306通过通信总线308完成相互间的通信。
通信接口304，用于与其它设备比如客户端或其它服务器等的网元通信。
处理器302，用于执行程序310，具体可以执行上述基于动态画幅的3D点云处理方法实施例中的相关步骤。
具体地，程序310可以包括程序代码，该程序代码包括计算机操作指令。
处理器302可能是中央处理器CPU，或者是特定集成电路ASIC(Application Specific Integrated Circuit)，或者是被配置成实施本发明实施例的一个或多个集成电路。计算设备包括的一个或多个处理器，可以是同一类型的处理器，如一个或多个CPU；也可以是不同类型的处理器，如一个或多个CPU以及一个或多个ASIC。
存储器306，用于存放程序310。存储器306可能包含高速RAM存储器，也可能还包括非易失性存储器(non-volatile memory)，例如至少一个磁盘存储器。
程序310具体可以用于使得处理器302执行上述任意方法实施例中的基于动态画幅的3D点云处理方法。程序310中各步骤的具体实现可以参见上述基于动态画幅的3D点云处理实施例中的相应步骤和单元中对应的描述，在此不赘述。所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的设备和模块的具体工作过程，可以参考前述方法实施例中的对应过程描述，在此不再赘述。
在此提供的算法和显示不与任何特定计算机、虚拟系统或者其它设备固有相关。各种通用系统也可以与基于在此的示教一起使用。根据上面的描述，构造这类系统所要求的结构是显而易见的。此外，本发明也不针对任何特定编程语言。应当明白，可以利用各种编程语言实现在此描述的本发明的内容，并且上面对特定语言所做的描述是为了披露本发明的最佳实施方式。
在此处所提供的说明书中，说明了大量具体细节。然而，能够理解，本发明的实施例可以在没有这些具体细节的情况下实践。在一些实例中，并未详细示出公知的方法、结构和技术，以便不模糊对本说明书的理解。
类似地，应当理解，为了精简本公开并帮助理解各个发明方面中的一个或多个，在上面对本发明的示例性实施例的描述中，本发明的各个特征有时被一起分组到单个实施 例、图、或者对其的描述中。然而，并不应将该公开的方法解释成反映如下意图：即所要求保护的本发明要求比在每个权利要求中所明确记载的特征更多的特征。更确切地说，如权利要求书所反映的那样，发明方面在于少于前面公开的单个实施例的所有特征。因此，遵循具体实施方式的权利要求书由此明确地并入该具体实施方式，其中每个权利要求本身都作为本发明的单独实施例。
本领域那些技术人员可以理解，可以对实施例中的设备中的模块进行自适应性地改变并且把它们设置在与该实施例不同的一个或多个设备中。可以把实施例中的模块或单元或组件组合成一个模块或单元或组件，以及此外可以把它们分成多个子模块或子单元或子组件。除了这样的特征和/或过程或者单元中的至少一些是相互排斥之外，可以采用任何组合对本说明书(包括伴随的权利要求、摘要和附图)中公开的所有特征以及如此公开的任何方法或者设备的所有过程或单元进行组合。除非另外明确陈述，本说明书(包括伴随的权利要求、摘要和附图)中公开的每个特征可以由提供相同、等同或相似目的的替代特征来代替。
此外，本领域的技术人员能够理解，尽管在此所述的一些实施例包括其它实施例中所包括的某些特征而不是其它特征，但是不同实施例的特征的组合意味着处于本发明的范围之内并且形成不同的实施例。例如，在权利要求书中，所要求保护的实施例的任意之一都可以以任意的组合方式来使用。
本发明的各个部件实施例可以以硬件实现，或者以在一个或者多个处理器上运行的软件模块实现，或者以它们的组合实现。本领域的技术人员应当理解，可以在实践中使用微处理器或者数字信号处理器(DSP)来实现根据本发明实施例中的一些或者全部部件的一些或者全部功能。本发明还可以实现为用于执行这里所描述的方法的一部分或者全部的设备或者装置程序(例如，计算机程序和计算机程序产品)。这样的实现本发明的程序可以存储在计算机可读介质上，或者可以具有一个或者多个信号的形式。这样的信号可以从因特网网站上下载得到，或者在载体信号上提供，或者以任何其他形式提供。
应该注意的是上述实施例对本发明进行说明而不是对本发明进行限制，并且本领域技术人员在不脱离所附权利要求的范围的情况下可设计出替换实施例。在权利要求中，不应将位于括号之间的任何参考符号构造成对权利要求的限制。单词“包含”不排除存在未列在权利要求中的元件或步骤。位于元件之前的单词“一”或“一个”不排除存在多个这样的元件。本发明可以借助于包括有若干不同元件的硬件以及借助于适当编程的计算机来实现。在列举了若干装置的单元权利要求中，这些装置中的若干个可以是通过 同一个硬件项来具体体现。单词第一、第二、以及第三等的使用不表示任何顺序。可将这些单词解释为名称。
Claims (14)
- 一种基于动态画幅的3D点云处理方法，所述方法包括：从当前场景的场景扫描图像中提取感兴趣区域，将所述感兴趣区域划分成多个分块，并确定每个分块对应的激光扫描范围；针对每个分块，根据该分块对应的激光扫描范围，配置该分块对应的激光扫描参数，依据所述激光扫描参数对该分块进行激光扫描，得到该分块的3D点云；对多个分块的3D点云进行拼接处理，得到所述感兴趣区域的3D点云。
- 根据权利要求1所述的方法，其中，在所述从当前场景的场景扫描图像中提取感兴趣区域之前，所述方法还包括：通过图像采集设备对当前场景进行图像采集，得到当前场景的场景扫描图像，并对当前场景的场景扫描图像进行分析，得到所述场景扫描图像的图像质量参数；所述从当前场景的场景扫描图像中提取感兴趣区域具体为：若所述图像质量参数小于预设参数阈值，则从当前场景的场景扫描图像中提取感兴趣区域。
- 根据权利要求1所述的方法，其中，所述将所述感兴趣区域划分成多个分块，并确定每个分块对应的激光扫描范围进一步包括：根据激光扫描设备的设置参数，确定所述感兴趣区域对应的激光扫描范围；获取分块参数，按照所述分块参数，将所述感兴趣区域划分成多个分块，并记录每个分块在所述感兴趣区域中的位置信息；针对每个分块，根据该分块在所述感兴趣区域中的位置信息以及所述感兴趣区域对应的激光扫描范围，确定每个分块对应的激光扫描范围。
- 根据权利要求1所述的方法，其中，所述依据所述激光扫描参数对该分块进行激光扫描，得到该分块的3D点云进一步包括：依据所述激光扫描参数，控制激光扫描设备中振镜的转动，利用所述振镜反射出的激光对该分块进行激光扫描，得到该分块的3D点云。
- 根据权利要求1-4任一项所述的方法，其中，所述激光扫描参数包括：激光扫描角度范围、激光信号强度以及激光扫描速度。
- 根据权利要求1所述的方法，其中，所述对多个分块的3D点云进行拼接处理，得到所述感兴趣区域的3D点云进一步包括：针对任两个相邻分块的3D点云，根据所述两个相邻分块在所述感兴趣区域中的位置信息，对所述两个相邻分块的3D点云进行取交集处理，得到重叠区域点云以及非重叠区域点云；依据所述重叠区域点云的点云质量，从所述重叠区域点云中选择用于拼接的目标重叠区域点云，将所述目标重叠区域点云与所述非重叠区域点云进行拼接处理；得到所述感兴趣区域的3D点云。
- 一种基于动态画幅的3D点云处理装置，所述装置包括：分块模块，适于从当前场景的场景扫描图像中提取感兴趣区域，将所述感兴趣区域划分成多个分块，并确定每个分块对应的激光扫描范围；扫描模块，适于针对每个分块，根据该分块对应的激光扫描范围，配置该分块对应的激光扫描参数，依据所述激光扫描参数对该分块进行激光扫描，得到该分块的3D点云；拼接模块，适于对多个分块的3D点云进行拼接处理，得到所述感兴趣区域的3D点云。
- 根据权利要求7所述的装置，其中，所述装置还包括：采集模块，适于通过图像采集设备对当前场景进行图像采集，得到当前场景的场景扫描图像；质量分析模块，适于对当前场景的场景扫描图像进行分析，得到所述场景扫描图像的图像质量参数；所述分块模块进一步适于：若所述图像质量参数小于预设参数阈值，则从当前场景的场景扫描图像中提取感兴趣区域。
- 根据权利要求7所述的装置，其中，所述分块模块进一步适于：根据激光扫描设备的设置参数，确定所述感兴趣区域对应的激光扫描范围；获取分块参数，按照所述分块参数，将所述感兴趣区域划分成多个分块，并记录每个分块在所述感兴趣区域中的位置信息；针对每个分块，根据该分块在所述感兴趣区域中的位置信息以及所述感兴趣区域对应的激光扫描范围，确定每个分块对应的激光扫描范围。
- 根据权利要求7所述的装置，其中，所述扫描模块进一步适于：依据所述激光扫描参数，控制激光扫描设备中振镜的转动，利用所述振镜反射出的激光对该分块进行激光扫描，得到该分块的3D点云。
- 根据权利要求7-10任一项所述的装置，其中，所述激光扫描参数包括：激光扫描角度范围、激光信号强度以及激光扫描速度。
- 根据权利要求7所述的装置，其中，所述拼接模块进一步适于：针对任两个相邻分块的3D点云，根据所述两个相邻分块在所述感兴趣区域中的位置信息，对所述两个相邻分块的3D点云进行取交集处理，得到重叠区域点云以及非重叠区域点云；依据所述重叠区域点云的点云质量，从所述重叠区域点云中选择用于拼接的目标重叠区域点云，将所述目标重叠区域点云与所述非重叠区域点云进行拼接处理；得到所述感兴趣区域的3D点云。
- 一种计算设备，包括：处理器、存储器、通信接口和通信总线，所述处理器、所述存储器和所述通信接口通过所述通信总线完成相互间的通信；所述存储器用于存放至少一可执行指令，所述可执行指令使所述处理器执行如权利要求1-6中任一项所述的基于动态画幅的3D点云处理方法对应的操作。
- 一种计算机存储介质，所述存储介质中存储有至少一可执行指令，所述可执行指令使处理器执行如权利要求1-6中任一项所述的基于动态画幅的3D点云处理方法对应的操作。
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|---|---|---|---|---|
| CN116708683A (zh) * | 2023-08-01 | 2023-09-05 | 文博安全科技有限公司 | 一种用于壁画的数字化自动采集系统及方法 |
| CN116708683B (zh) * | 2023-08-01 | 2023-10-10 | 文博安全科技有限公司 | 一种用于壁画的数字化自动采集系统及方法 |
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