Downsampling method and device for point cloud data, computer readable storage medium, electronic equipment and computer program product

Abstract

A point cloud data downsampling method and apparatus, a computer readable storage medium, an electronic device, and a computer program product, the method comprising: acquiring an original point cloud data set, the original point cloud data set being related to a point cloud acquisition device and comprising data of at least one spatial point; determining an area range of a target segmentation unit, wherein a set of the target segmentation units is used for dividing a detection space of the point cloud acquisition equipment and forming at least one part of a spherical space; based on the region range of the target segmentation unit, determining spatial points falling into the region range of the target segmentation unit in the original point cloud data set; and performing down-sampling processing on the spatial points falling into the area range of the target segmentation unit, and summarizing down-sampling results of one or more target segmentation units to obtain a down-sampling point cloud data set. The new point cloud data downsampling method provided by the scheme of the invention is helpful to alleviate the distortion condition after point cloud data downsampling, and improves the detection accuracy of the point cloud acquisition device.

Description

Technical Field

[0001] The present disclosure relates to the technical field of point cloud data processing, and in particular to a method and device for downsampling point cloud data, a computer-readable storage medium, an electronic device, and a computer program product. Background Art

[0002] At present, point cloud acquisition equipment is widely used in industrial equipment, autonomous driving and other fields, and can provide high-precision three-dimensional information of the surrounding environment. Taking Laser Radar (also known as Light Detection and Ranging, LIDAR) as an example, Laser Radar has become an indispensable positioning and measurement tool in various application scenarios due to its high-precision ranging capability and strong environmental adaptability.

[0003] The detection results of the point cloud acquisition device are usually represented in the form of point clouds. A point cloud represents the detection result obtained by the light of the point cloud acquisition device (for example, the laser emitted by the lidar) acting on a spatial point in the space and then reflecting. These detection results can be collectively referred to as point cloud data. The larger the volume of the point cloud acquired each time, the more detailed the detection of the space. This causes the amount of calculation to increase linearly or even exponentially as the number of point clouds increases when the processor processes the point cloud data fed back by the point cloud acquisition device. Therefore, before subsequent processing, the original point cloud data needs to be downsampled to greatly improve the computing efficiency.

[0004] However, the existing downsampling processing solutions for point cloud data have many defects, and there is an urgent need to provide a new point cloud data downsampling solution. Summary of the invention

[0005] The technical problem solved by the present disclosure is to provide an improved downsampling solution for point cloud data, so that the distribution of the downsampled point cloud data in space is closer to the distribution of the original point cloud data in space.

[0006] To solve the above technical problems, an embodiment of the present disclosure provides a downsampling method for point cloud data, comprising: acquiring an original point cloud data set, wherein the original point cloud data set is related to a point cloud acquisition device and includes data of at least one spatial point; determining an area range of a target segmentation unit, wherein a set of target segmentation units is used to divide the detection space of the point cloud acquisition device and form at least a part of a spherical space; based on the area range of the target segmentation unit, determining the spatial points in the original point cloud data set that fall within the area range of the target segmentation unit; performing downsampling processing on the spatial points that fall within the area range of the target segmentation unit respectively, and summarizing the downsampling results of one or more target segmentation units to obtain a downsampled point cloud data set.

[0007] Optionally, the farther away from the point cloud acquisition device, the larger the volume of the target segmentation unit.

[0008] Optionally, the area of ​​each target segmentation unit includes an area located between a first spherical surface and a second spherical surface with the point cloud acquisition device as the center, within a range defined by a first horizontal angle and a second horizontal angle, and within a range defined by a first vertical angle and a second vertical angle, wherein the first spherical surface and the second spherical surface are respectively defined by a first radius and a second radius, the first horizontal angle, the second horizontal angle, the first vertical angle, and the second vertical angle are all center angles, and for different target segmentation units, at least one of the first radius, the second radius, the first horizontal angle, the second horizontal angle, the first vertical angle, and the second vertical angle is different.

[0009] Optionally, each of the target segmentation units has the same radius increment, or the radius increment of the target segmentation unit is positively correlated with the distance from the target segmentation unit to the point cloud acquisition device, wherein the radius increment is the radius increment between the first radius and the second radius.

[0010] Optionally, a horizontal angle increment between the first horizontal angle and the second horizontal angle of each target segmentation unit is associated with a horizontal scanning angle of the point cloud acquisition device.

[0011] Optionally, a vertical angle increment between the first vertical angle and the second vertical angle of each target segmentation unit is associated with a vertical scanning angle of the point cloud acquisition device.

[0012] Optionally, when the radius increment of the target segmentation unit is positively correlated with the distance from the target segmentation unit to the point cloud acquisition device, the radius increment is related to the product of the first radius and the horizontal angle increment.

[0013] Optionally, determining the area range of the target segmentation unit includes: obtaining polar coordinate position information of the target space point, where the target space point is selected from the original point cloud data set; and determining the area range of the target segmentation unit corresponding to the target space point based on the radius value, horizontal angle value and vertical angle value of the polar coordinate position information of the target space point.

[0014] Optionally, determining the area range of the target segmentation unit includes: determining the area range of the target segmentation unit based on information of one or more preset candidate segmentation units.

[0015] Optionally, after downsampling the spatial points falling within the area range of the target segmentation unit, the method further includes: removing data of the spatial points falling within the area range of the target segmentation unit from the original point cloud data set.

[0016] Optionally, the data of the spatial point includes polar coordinate position information or Cartesian coordinate position information of the spatial point. In the case where the data of the spatial point includes Cartesian coordinate position information of the spatial point, the method further includes: for each spatial point in the original point cloud data set, converting the Cartesian coordinate position information of the spatial point into polar coordinate position information.

[0017] Optionally, downsampling is performed on the spatial points falling within the area range of the target segmentation unit, including: determining the centroid or centroid of the spatial points falling within the area range of the target segmentation unit, and using the data of the centroid or centroid as the downsampled point cloud data corresponding to the target segmentation unit.

[0018] Optionally, the method also includes: determining the total number of spatial points falling within the area range of the target segmentation unit; when the total number meets a preset condition, performing the downsampling process on the spatial points falling within the area range of the target segmentation unit; when the total number does not meet the preset condition, the downsampled point cloud dataset does not include data on the spatial points falling within the area range of the target segmentation unit.

[0019] Optionally, the downsampled point cloud dataset includes multiple sampling points, and the method further includes: determining target sampling points with the same horizontal angle value and vertical angle value based on the polar coordinate position information of the multiple sampling points; for the target sampling points with the same horizontal angle value and vertical angle value, retaining the target sampling point with the smallest radius value, and removing other target sampling points from the downsampled point cloud dataset.

[0020] In order to solve the above technical problems, the embodiments of the present disclosure also provide a downsampling device for point cloud data, including: an acquisition module, used to acquire an original point cloud data set, wherein the original point cloud data set is related to a point cloud acquisition device and includes data of at least one spatial point; a first determination module, used to determine the area range of a target segmentation unit, wherein a set of target segmentation units is used to divide the detection space of the point cloud acquisition device and form at least a part of a spherical space; a second determination module, used to determine the spatial points in the original point cloud data set that fall within the area range of the target segmentation unit based on the area range of the target segmentation unit; and a processing module, used to perform downsampling processing on the spatial points that fall within the area range of the target segmentation unit respectively, and summarize the downsampling results of one or more target segmentation units to obtain a downsampled point cloud data set.

[0021] To solve the above technical problems, an embodiment of the present disclosure further provides a computer-readable storage medium, which is a non-volatile storage medium or a non-transient storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are executed.

[0022] To solve the above technical problems, an embodiment of the present disclosure further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program that can be executed on the processor, and the processor executes the steps of the above method when running the computer program.

[0023] In order to solve the above technical problems, the embodiments of the present disclosure further provide a computer program product, including a computer program / instruction, which implements the steps of the above method when executed by a processor.

[0024] Compared with the prior art, the technical solution of the embodiment of the present disclosure has the following beneficial effects:

[0025] An embodiment of the present disclosure provides a method for downsampling point cloud data, comprising: acquiring an original point cloud data set, the original point cloud data set being related to a point cloud acquisition device and comprising data of at least one spatial point; determining an area range of a target segmentation unit, a set of target segmentation units being used to divide a detection space of the point cloud acquisition device and forming at least a part of a spherical space; based on the area range of the target segmentation unit, determining spatial points in the original point cloud data set that fall within the area range of the target segmentation unit; performing downsampling processing on the spatial points that fall within the area range of the target segmentation unit respectively, and summarizing the downsampling results of one or more target segmentation units to obtain a downsampled point cloud data set.

[0026] The existing downsampling scheme does not consider the scattering characteristics of the light of the point cloud acquisition device in space when determining the minimum spatial unit of downsampling, resulting in the distribution of the downsampled point cloud data in space being different from the distribution of the point cloud data in space in the original point cloud data set, and even distortion, which affects the detection accuracy of the point cloud acquisition device. In comparison, the present embodiment designs the target segmentation unit according to the scattering characteristics of the light of the point cloud acquisition device in space, wherein the light enables the point cloud acquisition device to acquire three-dimensional point cloud data, and the set of target segmentation units constitutes at least a part of a spherical space that conforms to the fan-shaped scattering characteristics of the light, and the spherical space takes the point cloud acquisition device as the center. Therefore, downsampling operations are performed in units of target segmentation units, which can ensure that the distribution of the downsampled point cloud data in space is close to or even consistent with the distribution of the original point cloud data in space, thereby alleviating the distortion of the point cloud data after downsampling and improving the detection accuracy of the point cloud acquisition device. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is an equivalent schematic diagram of a detection space of a point cloud acquisition device according to an embodiment of the present disclosure;

[0028] Figure 2 yes Figure 1 Schematic diagram of the segmentation unit;

[0029] Figure 3 is a flow chart of a method for downsampling point cloud data according to an embodiment of the present disclosure;

[0030] Figure 4 It is a structural schematic diagram of a downsampling device for point cloud data according to an embodiment of the present disclosure;

[0031] Figure 5 It is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0032] As mentioned in the background technology, the existing downsampling processing solutions for point cloud data have many defects, which lead to serious distortion of point cloud data after downsampling, seriously affecting the detection accuracy of point cloud acquisition equipment.

[0033] Specifically, point cloud acquisition equipment may include laser radar, structured light scanner, time-of-flight sensor, binocular camera, etc. Point cloud acquisition equipment acquires point cloud data through light, which may include the following two types of acquisition methods:

[0034] The first type is that the point cloud acquisition device emits a light beam that reflects off an object and acquires point cloud data based on the reflected signal. For example, a lidar measures the distance between an object and the sensor by emitting a laser pulse and receiving a reflected signal, calculating the time required for the laser pulse to be emitted and returned and converting it into distance, thereby generating three-dimensional point cloud data. The point cloud data acquisition method of time-of-flight sensors and ultrasonic sensors is similar to that of lidar. For another example, a structured light scanner projects a known pattern of light onto the surface of an object, captures the changes in the reflected light pattern through a camera, and calculates the three-dimensional shape of the object based on the changes in the pattern to form three-dimensional point cloud data.

[0035] The second type is point cloud acquisition equipment that receives light that carries object information and then acquires point cloud data. For example, a stereo camera (such as a binocular camera) uses two or more cameras to take images of the same scene from different angles using the pinhole imaging principle. The light received by each camera for imaging carries relevant information about the scene. The depth information of the object is calculated by comparing the parallax in the image to generate three-dimensional point cloud data.

[0036] The point cloud data obtained by any of the above methods needs to be downsampled. At present, there are mainly the following methods for point cloud downsampling:

[0037] Uniform downsampling: By selecting the farthest point as the sampling point, try to preserve the geometric details and reduce the loss in the process of point number reduction. This method has a high time complexity and may sample outrageous noise points.

[0038] Voxel downsampling: Voxelize the 3D space and select a point in each voxel as a sampling point. This method is efficient, but not as uniform as uniform sampling.

[0039] Curvature downsampling: The number of sampling points is large in areas with large curvature, preserving geometric features. This method has high computational efficiency and strong stability.

[0040] Random sampling: Randomly select a portion of points in the point cloud as the sampling result. It is simple and fast, but may lead to inaccurate results.

[0041] Nearest neighbor sampling: Calculate the distance between each point and the surrounding points, and select the point with the largest or smallest distance as the sampling result. This method can retain key features, but may ignore detailed information.

[0042] The common laser radars currently available on the market generally scan the environment through a fan-shaped or array laser, and then sample and generate a point cloud. Since the laser beam has a fan-shaped scattering characteristic, that is, it is fan-shaped and divergent in three-dimensional space, the farther away from the laser source, the greater the interval between the laser beam rays, and the sparser the measured point cloud. Therefore, the point cloud measured by the laser radar is generally non-uniformly distributed in the detection space. Furthermore, the vertical resolution or horizontal resolution of some laser radars is not fixed, that is, the point cloud distribution within a surface at a specific distance from the laser source is also uneven. Similarly, the light beams emitted by time-of-flight sensors and structured light scanners also have scattering characteristics, and the measured point cloud distribution is also non-uniform. The binocular camera uses the principle of pinhole imaging, so the light beam used for imaging also has a scattering characteristic. The farther away from the camera, the lower the resolution and the sparser the point cloud obtained. However, the commonly used downsampling methods are mostly uniform sampling or processing based on mathematical characteristics, such as voxel-based implementation. Voxels can be understood as three-dimensional grid maps with fixed resolution. These downsampling methods do not design segmentation units or voxels based on the scattering characteristics of the point cloud acquisition device. This causes the distribution of the point cloud data in space after downsampling using the existing technology to be inconsistent with the distribution of the original point cloud data in space, and even leads to distortion. It cannot meet the application requirements in some occasions with high requirements for data distribution.

[0043] To solve the above technical problems, an embodiment of the present disclosure provides a downsampling method for point cloud data, which is related to a point cloud acquisition device, including: acquiring an original point cloud data set, the original point cloud data set including data of at least one spatial point; determining an area range of a target segmentation unit, a set of target segmentation units being used to divide the detection space of the point cloud acquisition device and form at least a part of a spherical space; based on the area range of the target segmentation unit, determining the spatial points in the original point cloud data set that fall within the area range of the target segmentation unit; performing downsampling processing on the spatial points that fall within the area range of the target segmentation unit respectively, and summarizing the downsampling results of one or more target segmentation units to obtain a downsampled point cloud data set.

[0044] This embodiment designs the target segmentation unit according to the scattering characteristics of the light of the point cloud acquisition device in space, the light enables the point cloud acquisition device to acquire three-dimensional point cloud data, and the set of target segmentation units constitutes at least a part of a spherical space that conforms to the fan-shaped scattering characteristics of the light, and the spherical space takes the point cloud acquisition device as the center. Therefore, by performing downsampling operation based on the target segmentation unit as a unit, it can ensure that the distribution of the downsampled point cloud data in space is close to or even consistent with the distribution of the original point cloud data in space, thereby alleviating the distortion of the point cloud data after downsampling and improving the detection accuracy of the point cloud acquisition device.

[0045] The segmentation unit in the embodiment of the present disclosure refers to the minimum division granularity of the detection space of the point cloud acquisition device. The detection space is the area covered by the light of the point cloud acquisition device in the space. The segmentation unit of the embodiment of the present disclosure is equivalent to the voxel in the prior art to a certain extent, but the difference between the two is: first, the segmentation unit is based on the scattering characteristics of the light of the point cloud acquisition device, and the segmentation result of the detection space of the point cloud acquisition device is segmented, and the collection of segmentation units is spherical in the space with the point cloud acquisition device as the center, while the voxel is the smallest unit that constitutes the virtual three-dimensional grid map (usually a cube), without considering the scattering characteristics of the light of the point cloud acquisition device; second, the volume distribution of each segmentation unit in the detection space can be non-uniform, while the volume of all voxels in the three-dimensional grid map is consistent; third, the shape of the segmentation unit can be a spherical terrace, while the shape of the voxel is a cube. That is to say, in the embodiments disclosed herein, firstly, by designing a segmentation unit that conforms to the scattering characteristics of light of the point cloud acquisition device, relative to the sampling method in the prior art, the distribution of the downsampled point cloud data in space can be made closer to the distribution of the original point cloud data in space; further, the volume distribution of each segmentation unit can be non-uniform as needed, so that the downsampled point cloud data is similar to the original point cloud data and both are non-uniformly distributed in space.

[0046] refer to Figure 1In the embodiment of the present disclosure, the detection space around the point cloud acquisition device is virtualized as a spherical space 100 with the point cloud acquisition device as the sphere center O and the point cloud acquisition device as the radius R, and the segmentation unit 103 is used to divide the spherical space 100, that is, the segmentation unit 103 is used to sample the point cloud data located in the spherical space 100. The point cloud acquisition device as the sphere center O can be, for example, a light element of the point cloud acquisition device as the sphere center O, and the light element can be a light emitting element, such as a laser source of a laser radar, or a light receiving element, such as an image sensor of a binocular camera; the radius R of the spherical space 100 can be, for example, the maximum detection distance of the point cloud acquisition device, or the distance between the farthest spatial point in the point cloud data and the point cloud acquisition device, or other values. Further, the area of ​​the segmentation unit 103 includes an area between a first spherical surface 101 and a second spherical surface 102 with a point cloud acquisition device (e.g., a laser source of a laser radar) as a sphere center O, within a range defined by a first horizontal angle and a second horizontal angle (recorded as a horizontal angle increment △α=|first horizontal angle-second horizontal angle|), and within a range defined by a first vertical angle and a second vertical angle (recorded as a vertical angle increment △β=|first vertical angle-second vertical angle|). The first spherical surface 101 is defined by a first radius, the second spherical surface 102 is defined by a second radius, the first horizontal angle, the second horizontal angle, the first vertical angle, and the second vertical angle are all sphere center angles, the horizontal angle is an azimuth in a three-dimensional polar coordinate system, that is, an angle between a line connecting the sphere center and a point and the positive direction of the X axis, and the vertical angle is a polar angle in a three-dimensional polar coordinate system, that is, an angle between a line connecting the sphere center and a point and the positive direction of the Z axis. In other words, the segmentation unit 103 is a minimum partitioning granularity obtained by segmenting the spherical space 100 according to the three dimensions of radius increment △r, horizontal angle increment △α and vertical angle increment △β, where △r=|first radius-second radius|. Figure 1 The specific structure of a segmentation unit 103 in the spherical space 100 is exemplarily shown by color filling.

[0047] For example, when Δr, Δα, and Δβ corresponding to each segmentation unit 103 are the same, in the polar coordinate system, the region range of each segmentation unit 103 can be defined as:

[0048]

[0049] Among them, r is the radius value, k is the interval division index in the radius direction, such as the first radius of the kth radius interval is The second radius is ; α is the horizontal angle, m is the interval division index corresponding to the horizontal angle, such as the first horizontal angle corresponding to the mth horizontal angle interval is The second horizontal angle is ; β is the vertical angle, n is the interval division index corresponding to the vertical angle, such as the first vertical angle corresponding to the nth vertical angle interval is The second vertical angle is .

[0050] Further, for different segmentation units 103 , at least one of the first radius, the second radius, the first horizontal angle, the second horizontal angle, the first vertical angle, and the second vertical angle is different. Figure 2 Two segmentation units 103 (respectively referred to as a first segmentation unit 103a and a second segmentation unit 103b) are exemplarily shown, and both have the same first horizontal angle, second horizontal angle, first vertical angle, and second vertical angle, that is, the first segmentation unit 103a and the second segmentation unit 103b have the same horizontal angle increment △α and vertical angle increment △β. Figure 2 The first segmentation unit 103a and the second segmentation unit 103b have different first radii and different second radii. For example, the first segmentation unit 103a has front and rear boundaries defined along the radius r direction by a first radius having a length of d1 and a second radius having a length of d2, and the second segmentation unit 103b has front and rear boundaries defined along the radius r direction by a first radius having a length of d3 and a second radius having a length of d4. Figure 2 In the example shown, the first segmentation unit 103a and the second segmentation unit 103b are adjacent to each other along the extension direction of the radius r, and the corresponding d1 <d2=d3<d4。

[0051] Furthermore, the farther away from the point cloud acquisition device (ie, the sphere center O), the larger the volume of the segmentation unit 103. Figure 2 , the volume of the second segmentation unit 103b which is farther from the point cloud acquisition device is larger than the volume of the first segmentation unit 103a which is closer to the point cloud acquisition device. Under the premise that the distance from the segmentation unit 103 to the point cloud acquisition device (i.e., the center of the sphere O) is fixed, the larger the volume of the segmentation unit 103, the more spatial points in the original point cloud data set collected by the point cloud acquisition device fall into the segmentation unit 103, and the corresponding downsampling rate of the original point cloud data set is higher. Therefore, considering that the travel of the light of the point cloud acquisition device in the detection space is conically divergent from the point cloud acquisition device to the outside, the farther away from the point cloud acquisition device, the larger the volume of the area covered by the light in the space, therefore, the volume of the segmentation unit also changes accordingly with the change of the distance from the segmentation unit to the point cloud acquisition device. Compared with the fixed volume voxel segmentation method of the prior art, the division of the detection space by the segmentation unit of this embodiment is more in line with the light scattering characteristics of the point cloud acquisition device, and the distribution of the point cloud data in the space after the downsampling operation is performed in units of the segmentation unit is more in line with the distribution of the original point cloud data in the space.

[0052] It is understandable that, according to actual needs, the volumes of the segmentation units 103 located at different distances from the point cloud acquisition device may be made the same.

[0053] In some embodiments, continue to refer to Figure 1 and Figure 2 The shape of the segmentation unit 103 can be a spherical cap terrace, the upper bottom surface and the lower bottom surface of the spherical cap terrace are spherical caps with different radii (i.e., a part of the first spherical surface 101 and a part of the second spherical surface 102) with the point cloud acquisition device as the sphere center O, and the side edges of the spherical cap terrace belong to a part of the ray with the point cloud acquisition device (i.e., the sphere center O) as the starting point, specifically including a first side edge whose extension line passes through the sphere center O, has a first horizontal angle with the X-axis, has a first vertical angle with the Z-axis and is located between the first spherical surface 101 and the second spherical surface 102, and an extension line A second side edge passes through the center O, has a second horizontal angle with the X-axis, has a first vertical angle with the Z-axis, and is located between the first spherical surface 101 and the second spherical surface 102; an extension line passes through the center O, has a first horizontal angle with the X-axis, has a second vertical angle with the Z-axis, and is located between the first spherical surface 101 and the second spherical surface 102; and an extension line passes through the center O, has a second horizontal angle with the X-axis, has a second vertical angle with the Z-axis, and is located between the first spherical surface 101 and the second spherical surface 102. The first side edge, the second side edge, the first spherical surface 101 and the second spherical surface 102 together form the first side surface of the spherical crown step, the first side edge, the third side edge, the first spherical surface 101 and the second spherical surface 102 together form the second side surface of the spherical crown step, the second side edge, the fourth side edge, the first spherical surface 101 and the second spherical surface 102 together form the third side surface of the spherical crown step, and the third side edge, the fourth side edge, the first spherical surface 101 and the second spherical surface 102 together form the fourth side surface of the spherical crown step.

[0054] In some embodiments, each segmentation unit 103 may have the same radius increment △r. That is, for each spherical pyramid, the spherical pyramid may be divided at equal intervals along the radius R to obtain a plurality of segmentation units 103. Thus, along the radius R (for example, in the laser emission direction of the laser radar), the radius increment △r of each segmentation unit remains consistent, and in this case, semi-uniform downsampling may be achieved.

[0055] Alternatively, the radius increment △r of the segmentation unit 103 may be positively correlated with the distance from the segmentation unit 103 to the point cloud acquisition device (i.e., the sphere center O), that is, along the radial direction, the farther the segmentation unit 103 is from the sphere center O, the larger the radius increment △r is, and adaptive downsampling may be performed at this time. For example, the farther the radius R is from the sphere center O, the larger the radius increment △r of the segmentation unit 103 is, such as Figure 2In another example, when the radius increment △r of the segmentation unit 103 is positively correlated with the distance from the segmentation unit 103 to the point cloud acquisition device (i.e., the sphere center O), the radius increment △r is related to the product of the first radius and the horizontal angle increment △α, that is, , where · represents multiplication, and d represents the first radius. It can be understood that △r can also be defined in other ways, which are not specifically limited here.

[0056] In some embodiments, the point cloud acquisition device acquires point cloud data by emitting light beams, specifically, by emitting light beams along the horizontal scanning direction and / or the vertical scanning direction to scan the surrounding space, wherein the scanning direction may be the rotation direction of the point cloud acquisition device in the horizontal plane or the vertical plane, or the arrangement direction of the light beam array in the horizontal plane or the vertical plane. For example, one of the horizontal scanning direction and the vertical scanning direction is the rotation direction of the point cloud acquisition device, and the other is the arrangement direction of the light beam array emitted by the point cloud acquisition device; or, the horizontal scanning direction and the vertical scanning direction are both the rotation direction of the point cloud acquisition device. The angle between adjacent light beams emitted (simultaneously or successively) by the point cloud acquisition device along the horizontal scanning direction is recorded as the horizontal scanning angle, and the angle between adjacent light beams emitted (simultaneously or successively) along the vertical scanning direction is recorded as the vertical scanning angle.

[0057] Taking laser radar as an example, the laser radar scans two of the four sides of the spherical crown terrace in the horizontal scanning direction and scans the remaining two of the four sides in the vertical scanning direction. Figure 1 The spherical space 100 is divided into a plurality of spherical pyramids 104 (eg Figure 2 As shown in FIG. 1 ), the spherical pyramid 104 is further divided along the radius R to obtain the segmentation unit 103. Thus, the operation mode of the laser radar sector scanning is simulated, and the geometric body composed of the two spherical caps and the scanning beams of the laser in different directions is positioned as a spherical cap terrace, and the original point cloud data is downsampled in units of spherical cap terraces, so that the distribution of the downsampled point cloud data in the detection space is consistent with the distribution of the original point cloud data in the detection space, that is, the closer to the laser source, the more sampling points (i.e., point cloud data, corresponding to spatial points) are retained; the farther away from the laser source, the fewer sampling points are retained.

[0058] In some embodiments, the horizontal angle increment △α between the first horizontal angle and the second horizontal angle of each segmentation unit is associated with the horizontal scanning angle of the point cloud acquisition device. Specifically, the horizontal scanning angle can be a preset parameter of the point cloud acquisition device, such as a factory setting parameter. For example, when the horizontal scanning angle is fixed, the horizontal angle increment △α can be equal to the horizontal scanning angle, so that the division accuracy of the segmentation unit and the horizontal scanning resolution of the point cloud acquisition device are consistent, which is conducive to better reflecting and retaining the distribution of point cloud data in the detection space; for another example, the horizontal angle increment △α can be an integer multiple of the horizontal scanning angle, which is conducive to reducing the number of segmentation units obtained by division, further improving the downsampling rate, and reducing the amount of data processing in subsequent calculations.

[0059] Alternatively, each segmentation unit 103 may have the same horizontal angle increment Δα, thereby being able to adapt to different detection scenarios and improving the compatibility of this embodiment.

[0060] In some embodiments, the vertical angle increment △β between the first vertical angle and the second vertical angle of each segmentation unit is associated with the vertical scanning angle of the point cloud acquisition device. Specifically, the vertical scanning angle can be a preset parameter of the point cloud acquisition device, such as a factory setting parameter. For example, the vertical angle increment △β can be equal to the vertical scanning angle to ensure that the division accuracy of the segmentation unit and the vertical scanning resolution of the point cloud acquisition device are consistent, and the distribution of the point cloud data in the detection space is retained as much as possible. For another example, the vertical angle increment △β can be taken as an integer multiple of the vertical scanning angle, which is conducive to reducing the number of segmentation units obtained by division, further improving the downsampling rate, and reducing the amount of data processing in subsequent calculations.

[0061] Alternatively, each segmentation unit 103 may have the same vertical angle increment Δβ.

[0062] In practical applications, the vertical angle increment △β (and / or the horizontal angle increment △α) can be flexibly set to a constant value or an adaptive value according to user needs to meet different detection scenarios and downsampling requirements.

[0063] In some embodiments, the horizontal scanning angle of the point cloud acquisition device in the horizontal scanning direction may be unevenly distributed, that is, the value of the horizontal scanning angle of the point cloud acquisition device in the horizontal scanning direction may be variable. Accordingly, the horizontal angle increment △α changes with the change of the horizontal scanning angle. For example, a comparison table of the horizontal angle increment △α may be pre-generated to record the specific values ​​of the horizontal angle increment △α corresponding to different horizontal angle regions in the spherical space 100.

[0064] Similarly, the vertical scanning angle of the point cloud acquisition device in the vertical scanning direction may be unevenly distributed, that is, the value of the vertical scanning angle of the point cloud acquisition device in the vertical scanning direction may be variable. Accordingly, the vertical angle increment △β changes with the change of the vertical scanning angle. For example, a comparison table of the vertical angle increment △β may be generated in advance to record the specific values ​​of the vertical angle increment △β corresponding to different vertical angle regions in the spherical space 100.

[0065] The above introduces the division method of the spherical space 100 and the segmentation unit 103 and the definition of the specific area range. It can be understood that in the actual processing process, it is not necessary to determine the area range information of all the segmentation units 103 in the spherical space 100. In other words, the actual processing process may only involve some of the segmentation units 103 in the spherical space 100. In one example, all the segmentation units 103 in the spherical space 100 can be traversed to determine the spatial points in the point cloud data that fall into the segmentation unit 103. In this case, it is necessary to determine the area range information of all the segmentation units 103, that is, the segmentation units 103 to be processed constitute a complete spherical space 100. In another example, in order to improve efficiency, the traversal range can be narrowed according to the information of the detected target object, that is, only some of the segmentation units 103 in the spherical space 100 are traversed, that is, the segmentation units 103 to be processed constitute a part of the complete spherical space 100. In another example, a spatial point can be selected first, and the corresponding segmentation unit 103 can be determined based on the information of the spatial point and the relevant information of the pre-stored segmentation unit 103, which can also improve efficiency. That is, the segmentation unit 103 to be processed constitutes a part of the complete spherical space 100.

[0066] For ease of understanding, in the embodiment of the present disclosure, the segmentation unit whose area range needs to be determined for downsampling processing is referred to as a candidate segmentation unit. It can be understood that the set of candidate segmentation units forms at least a part of the spherical space 100. In order to determine the area range of each candidate segmentation unit, the relevant information of the candidate segmentation unit needs to be stored in the memory of the electronic device for future use. The relevant information of the candidate segmentation unit may include information on the area range of the candidate segmentation unit, such as the first radius, the second radius, the first horizontal angle, the second horizontal angle, the first vertical angle and the second vertical angle, or the coordinate information of each vertex of the spherical crown terrace, and the coordinate information may be the position coordinates in the polar coordinate system or the Cartesian coordinate system with the center O as the origin; the relevant information of the candidate segmentation unit may also include information for determining the area range of the candidate segmentation unit, for example, when the radius increment △r, the horizontal angle increment △α, and the vertical angle increment △β are fixed, it is only necessary to pre-store the initial values ​​of the radius, the horizontal angle, and the vertical angle, and the information of △α, △β, and △r.

[0067] Further, in the embodiment of the present disclosure, the candidate segmentation unit processed each time (determining its area range and performing downsampling) is called a target segmentation unit, and determining the area range of the target segmentation unit is to determine the area range of the candidate segmentation unit to be processed currently. It can be understood that the set of target segmentation units also forms at least a part of the spherical space 100.

[0068] In order to make the above-mentioned objects, features and beneficial effects of the present disclosure more obvious and easy to understand, the specific embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0069] Figure 3 It is a flowchart of a method for downsampling point cloud data according to an embodiment of the present disclosure.

[0070] This implementation scheme can be applied to point cloud data processing application scenarios to downsample the original point cloud data acquired by a point cloud acquisition device to reduce the amount of calculation for subsequent data processing.

[0071] This implementation scheme may be executed by a processor of an electronic device. The electronic device may be a computer, a point cloud acquisition device, or other device including a processor. The processor may be a central processing unit (CPU) of the electronic device, or may be a newly added component in the electronic device specifically used to execute this implementation scheme, such as a functional module specifically used to perform downsampling processing.

[0072] Specifically, refer to Figure 3 The downsampling method of point cloud data in this embodiment may include the following steps (abbreviated as S):

[0073] S302, acquiring an original point cloud data set, where the original point cloud data set is related to a point cloud acquisition device and includes data of at least one spatial point.

[0074] More specifically, the original point cloud data set may include one or more original point cloud data, and the original point cloud data refers to the point cloud data acquired by the point cloud acquisition device and to be downsampled using the embodiment of the present disclosure. The point cloud data is used to represent the data of the spatial points detected by the light of the point cloud acquisition device in the detection space, and may specifically include position coordinates, reflection intensity, color, etc.

[0075] Furthermore, the processor may communicate with a detection module of the point cloud acquisition device or a memory of the electronic device to acquire an original point cloud data set detected by the point cloud acquisition device. For example, the detection module may be a sensor.

[0076] In some embodiments, the data of the spatial point may include Cartesian coordinate position information of the spatial point. For example, the position coordinates of each spatial point in the original point cloud data set may be represented based on a Cartesian coordinate system.

[0077] Further, after S302, the downsampling method of this embodiment may further include the step of: for the i-th spatial point in the original point cloud data set, converting the Cartesian coordinate position information of the i-th spatial point into: Convert to polar coordinates . Among them, α and β are converted to the corresponding radian interval according to the coordinate point sign.

[0078] (1)

[0079] By converting the coordinate information of each spatial point in the original point cloud data set into a polar coordinate system representation, it is helpful to determine the area range of the target segmentation unit where the spatial point falls in the subsequent steps, such as Figure 2 shown.

[0080] In one variation, the data of the spatial point may include polar coordinate position information of the spatial point, that is, the coordinate position of the spatial point may be represented based on a polar coordinate system. In this variation, the original point cloud data set originally output by the point cloud acquisition device may directly execute subsequent steps (such as S304 described below) without preprocessing such as coordinate system conversion, and the downsampling efficiency of executing this embodiment is higher.

[0081] Further reference Figure 3 The downsampling method for point cloud acquisition equipment described in this embodiment may include the following steps:

[0082] S304, determining the area range of the target segmentation unit, where the set of target segmentation units is used to divide the detection space of the point cloud acquisition device and form at least a part of the spherical space.

[0083] As described above, determining the region range of the target segmentation unit is to determine the region range of the candidate segmentation unit to be processed currently.

[0084] Specifically, the region range of the target segmentation unit may include a three-dimensional space surrounded by the outline of the target segmentation unit, that is, a three-dimensional space surrounded by a radius increment △r, a horizontal angle increment △α, and a vertical angle increment △β.

[0085] In some embodiments, in order to improve the downsampling efficiency, a target space point, that is, a space point to be processed currently, may be given first, and then the region range of the candidate segmentation unit to which the target space point belongs, that is, the region range of the target segmentation unit, may be determined. Specifically, S304 may include the steps of: obtaining polar coordinate position information of the target space point, the target space point being selected from the original point cloud data set; and determining the region range of the target segmentation unit corresponding to the target space point based on the radius value, horizontal angle value, and vertical angle value of the polar coordinate position information of the target space point.

[0086] Specifically, the target spatial point can be randomly selected from the original point cloud dataset. Alternatively, the target spatial point can be the most edge spatial point in the original point cloud dataset, such as the spatial point with the largest (or smallest) radius value, the largest (or smallest) horizontal angle value, and / or the largest (or smallest) vertical angle value.

[0087] Furthermore, the region range of the target segmentation unit corresponding to the target space point may be determined according to the polar coordinate position information of the target space point and the relevant information of the pre-stored candidate segmentation units.

[0088] For example, when the radius increment △r, the horizontal angle increment △α, and the vertical angle increment △β are fixed, the area range of the target segmentation unit can be calculated in real time based on the polar coordinate position information of the target space point, the initial values ​​of the radius, horizontal angle, and vertical angle in the relevant information of the candidate segmentation unit, the radius increment △r, the horizontal angle increment △α, and the vertical angle increment △β. For example, when the initial value of the radius is 0, the radius value of the polar coordinate position information of the target space point can be divided by the radius increment △r, and the calculation result can be rounded up to obtain the first radius and the second radius of the target segmentation unit, that is, the calculation result is rounded down to obtain the first radius of the target segmentation unit, and the calculation result is rounded up to obtain the second radius of the target segmentation unit.

[0089] For another example, the relevant information of the candidate segmentation unit may include the area range information of multiple candidate segmentation units. The candidate segmentation unit in which the target space point falls can be searched from these candidate segmentation units based on the polar coordinate position information of the target space point. The candidate segmentation unit found is the target segmentation unit, and the area range information of the candidate segmentation unit is the area range information of the target segmentation unit.

[0090] This embodiment uses the spatial points in the original point cloud data set as indexes and determines the area range of the target segmentation unit based on the coordinate position of the spatial points, which is helpful to avoid blindly determining the area range of the target segmentation unit where no spatial points in the original point cloud data set actually fall into. As a result, the set of target segmentation units can be determined with less data calculation, improving the downsampling efficiency and reducing the data processing pressure of the processor.

[0091] In a variation example, S304 may specifically include the step of: determining the area range of the target segmentation unit based on information of one or more preset candidate segmentation units.

[0092] Specifically, the processor may access the memory of the electronic device to call pre-stored relevant information of the candidate segmentation units, where the relevant information includes information of one or more candidate segmentation units.

[0093] For example, a candidate segmentation unit may be selected from one or more candidate segmentation units to obtain an initial target segmentation unit, and the area range of each target segmentation unit may be determined one by one according to a certain traversal rule starting from the initial target segmentation unit. The area range of the subsequent target segmentation unit may be obtained by, for example, adding a horizontal angle increment △α in the horizontal scanning direction, adding a vertical angle increment △β in the vertical scanning direction, and adding a radius increment △r along a ray with a point cloud acquisition device as an endpoint on the basis of the area range of the previous target segmentation unit; or the area range of the subsequent target segmentation unit may be directly obtained from pre-stored information.

[0094] For the selection of the initial target segmentation unit, the target space point may be randomly selected from the original point cloud data set, and the candidate segmentation unit in which the target space point falls is determined as the initial target segmentation unit. Alternatively, the first radius, the second radius, the first horizontal angle, the second horizontal angle, the first vertical angle, and the second vertical angle of the initial target segmentation unit may be pre-set so as to directly determine the region range of the initial target segmentation unit when executing S304.

[0095] For another example, the area occupied by the set of candidate segmentation units in the spherical space 100 can be determined based on the maximum scanning range of the point cloud acquisition device that needs to be downsampled this time, and all candidate segmentation units in the spherical space 100 that fall within the maximum scanning range can be determined as target segmentation units in turn.

[0096] This variation example determines the target segmentation unit based on the relevant information of the preset candidate segmentation units. Since the area range of the candidate segmentation unit is known information, the real-time data processing amount of the processor can be greatly reduced, which is conducive to improving the execution efficiency of the entire downsampling scheme.

[0097] Further reference Figure 3 The downsampling method of point cloud data in this embodiment may include the following steps:

[0098] S306, based on the area range of the target segmentation unit, determine the spatial points in the original point cloud data set that fall within the area range of the target segmentation unit.

[0099] Specifically, according to the regional range of the target segmentation unit characterized in the polar coordinate system and the polar coordinate position information of each spatial point in the original point cloud dataset, the spatial points falling within the regional range of the target segmentation unit are determined.

[0100] For example, continuing to refer to Figure 2 , assuming that the second segmentation unit 103b is the target segmentation unit, and there is a spatial point A in the original point cloud dataset with polar coordinate position information {rA, αA, βA}, and d3 < rA < d4, αA is within the interval of the horizontal angle increment Δα of the second segmentation unit 103b, and βA is within the interval of the vertical angle increment Δβ of the second segmentation unit 103b, then it can be determined that the spatial point A falls into Figure 2 the second segmentation unit 103b shown.

[0101] Further, continuing to refer to Figure 3 , the downsampling method of the point cloud data in this embodiment may include the following steps:

[0102] S308, perform downsampling processing on the spatial points falling within the regional range of the target segmentation unit respectively, and summarize the downsampling results of one or more target segmentation units to obtain a downsampled point cloud dataset .

[0103] Specifically, the centroid or center of mass of the spatial points falling within the regional range of the target segmentation unit can be determined, and the data of the centroid or center of mass is used as the downsampled point cloud data corresponding to the target segmentation unit. Among them, the center of mass of the spatial points can be determined based on algorithms such as the bounding box algorithm, such as the AABB bounding box (Axis-aligned bounding box), Sphere, Oriented bounding box OBB (Oriented bounding box), and Fixed directions hulls or k-DOP.

[0104] For example, the centroid of all spatial points falling within the regional range of the target segmentation unit can be calculated based on formula (2) :

[0105] (2)

[0106] where J is the total number of spatial points falling within the regional range of the target segmentation unit, j is the j-th spatial point among the J spatial points, and 0 ≤ j ≤ J.

[0107] The downsampling process is used to reduce the number of spatial points falling within the regional range of the target segmentation unit. For example, refer to Figure 2, assuming that the second segmentation unit 103b is the target segmentation unit, there are 11 spatial points falling into the second segmentation unit 103b before the downsampling process, and the number of spatial points in the second segmentation unit 103b can be reduced to 1 after the downsampling process of S308. As a result, the volume of point cloud data that needs to be calculated in subsequent data processing is reduced. Further, when S308 performs downsampling processing, the centroid or centroid of the spatial points falling into the area range of the target segmentation unit is determined as the downsampled point cloud data corresponding to the target segmentation unit, so that the downsampled point cloud data can reflect the distribution of the spatial points falling into the area range of the target segmentation unit to a certain extent.

[0108] In some embodiments, S304 to S308 may be executed in a sequential loop. Further, after each execution of S308, the data of the spatial points falling within the area range of the target segmentation unit may be removed from the original point cloud data set.

[0109] For example, starting from the initial target segmentation unit, the original point cloud data set can be traversed to determine the spatial points that fall into the area range of the current target segmentation unit, and the centroid of the spatial points that fall into the area range of the current target segmentation unit can be calculated to obtain the downsampling result of the current target segmentation unit. Then, the spatial points that fall into the area range of the current target segmentation unit are removed from the original point cloud data set, and the next target segmentation unit is determined based on the current target segmentation unit as the updated current segmentation unit, and the above steps are repeated until the original point cloud data set is traversed or all target segmentation units are traversed.

[0110] For another example, the target spatial point can be selected from the original point cloud dataset, the target segmentation unit in which the target spatial point falls is determined, and then the original point cloud dataset is traversed to determine all spatial points that fall into the target segmentation unit, and the centroid of the spatial points that fall into the area of ​​the target segmentation unit is calculated to obtain the downsampling result of the current target segmentation unit. Then, the spatial points that fall into the area of ​​the target segmentation unit are removed from the original point cloud dataset, and the target spatial point is selected from the updated original point cloud dataset, and the above steps are repeated until the original point cloud dataset is traversed (that is, the updated original point cloud dataset is empty).

[0111] In a specific implementation, after S306 and before S308, the downsampling method described in this embodiment may also include the steps of: determining the total number J of spatial points falling within the area range of the target segmentation unit; when the total number J meets the preset conditions, downsampling the spatial points falling within the area range of the target segmentation unit; when the total number J does not meet the preset conditions, the downsampled point cloud data set does not include the data of the spatial points falling within the area range of the target segmentation unit.

[0112] Specifically, the preset condition may be a preset point filtering threshold Q. The specific value of the point filtering threshold Q can be set as needed, such as taking values from 0 to 5. For example, Q = 3.

[0113] Furthermore, preliminary filtering can be performed based on the point filtering threshold Q before actually performing the downsampling operation. For example, the total number J of spatial points within the region range falling into the target segmentation unit can be statistically judged by setting the point filtering threshold Q. If J ≥ Q, then S308 is executed. Otherwise, that is, J < Q, it is considered that there are no valid points in the target segmentation unit, and all spatial points within the region range falling into this target segmentation unit are discarded without executing S308.

[0114] Thus, a batch of spatial points are first screened out according to the total number J of spatial points falling into the target segmentation unit. Only the target segmentation units with a sufficiently large total number J of falling points will sample and retain one spatial point, which can reduce the number of executions of the downsampling operation and reduce the data processing volume of the downsampling operation. On the other hand, there will inevitably be noise points in the original point cloud data set. If the total number J of spatial points originally falling into the target segmentation unit is already very small (for example, only 1), then these points can be considered as noise points and need to be removed. If this only spatial point is still retained after the target segmentation unit executes S308, it will cause the weight of this spatial point to be unreasonably increased, which will instead lead to the distortion of the distribution of the point cloud data in the detection space after downsampling.

[0115] In a specific implementation, the downsampled point cloud data set aggregated in S308 may include multiple sampling points. Correspondingly, this implementation scheme may further include the steps of: determining target sampling points with the same horizontal angle value and vertical angle value based on the polar coordinate position information of the multiple sampling points; for the target sampling points with the same horizontal angle value and vertical angle value, retaining the target sampling point with the smallest radius value and removing other target sampling points from the downsampled point cloud data set.

[0116] In one example, the original point cloud data may also be the ideal point cloud data of the detected target object. The ideal point cloud data of the target object is the point cloud data of the target object under an ideal state. Compared with the measured point cloud data, which may only reflect part of the position information of the target object, the ideal point cloud data can reflect the overall position information of the target object. The ideal point cloud data can be obtained through the three-dimensional ideal model corresponding to the target object, or through the point cloud acquisition device to obtain the target object by pre-scanning the whole. The ideal point cloud data of the target object is composed of a plurality of discretely distributed spatial points, and the ideal model corresponding to the ideal point cloud data has the same shape and size as the target object. Therefore, the ideal point cloud data theoretically includes the point cloud data of each discrete position of the target object. When determining the position and posture information of the target object, the ideal point cloud data and the measured point cloud data can be first registered and the conversion relationship between the two can be determined. Then, based on the ideal point cloud data and the conversion relationship, the position of each point of the target object can be more accurately determined.

[0117] In order to further improve the computational efficiency or the registration accuracy, it is also necessary to downsample the ideal point cloud data. Specifically, based on the relative position information between the target object and the point cloud acquisition device, the representation of the ideal point cloud data in the coordinate system of the point cloud acquisition device can be determined, for example, the polar coordinate system representation with the point cloud acquisition device as the polar center, and then the steps S302 to S308 are executed repeatedly to obtain the downsampled point cloud data set. Among them, the relative position information between the target object and the point cloud acquisition device can be pre-set or obtained based on the measured point cloud data.

[0118] Furthermore, the downsampled point cloud data set obtained after downsampling the ideal point cloud data also reflects the overall spatial information of the target object, while the measured point cloud data may lack spatial information of many unmeasured blind spots and loopholes. If the two are directly aligned, the spatial points in the blind spots of the ideal point cloud data may be matched with the spatial points in the non-blind spots of the measured point cloud data, resulting in inaccurate alignment results. In order to avoid this situation and improve the accuracy of point cloud data alignment, in the embodiment of the present disclosure, according to the visibility of the point cloud acquisition device, the spatial points in the downsampled point cloud data set that are blocked by other spatial points are deleted, that is, the actual measurement scene is simulated for projection filtering.

[0119] Specifically, for the target sampling points with the same horizontal angle value and the same vertical angle value in the downsampled point cloud dataset, it means that the line connecting these target sampling points passes through the point cloud acquisition device, that is, these target sampling points are located in the same radial direction. At this time, the target sampling point with a non-minimum radius value among these target sampling points is actually blocked by the target sampling point with the smallest radius value, so the target sampling point with a non-minimum radius value needs to be removed from the downsampled point cloud dataset.

[0120] For example, the downsampled point cloud dataset obtained by executing S308 may be For the same m and n point sets, , only keep ,in Then for the same m and n point sets, the downsampled point cloud data set excluding other target sampling points can be recorded as .

[0121] Therefore, considering that in actual applications the light of the point cloud acquisition device cannot see the obstructed object during the transmission process, this embodiment simulates the projection principle of the point cloud acquisition device to further screen the downsampled point cloud data set, discarding the downsampled point cloud data located behind the obstruction, and further improving the authenticity of the ideal point cloud data after the downsampling processing.

[0122] In a specific implementation, after S302 and before S304, the present embodiment may further include the step of filtering the data of the spatial points in the original point cloud data set. For example, a clustering algorithm may be used to remove outliers in the original point cloud data set, and then S304 to S308 are executed to perform downsampling processing. In this way, the interference of individual outliers on the downsampling effect can be reduced.

[0123] From the above, this embodiment adopts the target segmentation unit designed according to the scattering characteristics of the light of the point cloud acquisition device in space, and the set of target segmentation units constitutes at least a part of the spherical space that conforms to the fan-shaped scattering characteristics of the laser, and the spherical space takes the point cloud acquisition device as the center. Therefore, the downsampling operation is performed in units of target segmentation units, which can ensure that the distribution of the downsampled point cloud data in space is close to or even consistent with the distribution of the original point cloud data in space, thereby alleviating the distortion of the point cloud data after downsampling and improving the detection accuracy of the point cloud acquisition device.

[0124] Figure 4 4 is a schematic diagram of a downsampling device 4 for point cloud data according to an embodiment of the present disclosure. A person skilled in the art will appreciate that the downsampling device 4 for point cloud data according to this embodiment can be used to implement the above Figures 1 to 3 The method and technical solution described in the embodiments.

[0125] Specifically, refer to Figure 4The point cloud data downsampling device 4 of this embodiment may include: an acquisition module 41, used to acquire an original point cloud data set, the original point cloud data set is related to a point cloud acquisition device and includes data of at least one spatial point; a first determination module 42, used to determine the area range of a target segmentation unit, a set of target segmentation units is used to divide the detection space of the point cloud acquisition device and form at least a part of a spherical space; a second determination module 43, used to determine the spatial points in the original point cloud data set that fall into the area range of the target segmentation unit based on the area range of the target segmentation unit; a processing module 44, used to perform downsampling processing on the spatial points that fall into the area range of the target segmentation unit respectively, and summarize the downsampling results of one or more target segmentation units to obtain a downsampled point cloud data set.

[0126] For more information about the working principle and working method of the point cloud data downsampling device 4, please refer to the above Figures 1 to 3 The relevant description in will not be repeated here.

[0127] The embodiment of the present disclosure also provides a computer-readable storage medium, which is a non-volatile storage medium or a non-transitory storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the downsampling method of point cloud data provided in any of the above embodiments are executed. Preferably, the storage medium may include a computer-readable storage medium such as a non-volatile memory or a non-transitory memory. The storage medium may include a ROM, a RAM, a magnetic disk, or an optical disk, etc.

[0128] The present disclosure also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and the processor executes the above-mentioned Figures 1 to 3 The steps of the downsampling method of point cloud data provided in the corresponding embodiment. The electronic device can be a device with data processing capability such as a computer.

[0129] Figure 5 It is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present disclosure.

[0130] The electronic device may include a processor 501 and a memory 502 storing computer program instructions.

[0131] Specifically, the processor 501 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present disclosure.

[0132] The memory 502 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 502 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 502 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 502 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 502 is a non-volatile solid-state memory.

[0133] The memory may include read-only memory (ROM), random access memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical or other physical / tangible memory storage devices. Thus, typically, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0134] The processor 501 implements any one of the point cloud data downsampling methods in the above embodiments by reading and executing computer program instructions stored in the memory 502 .

[0135] In one example, the electronic device may further include a communication interface 503 and a bus 510. Figure 5 As shown, the processor 501, the memory 502, and the communication interface 503 are connected via a bus 510 and communicate with each other.

[0136] The communication interface 503 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present disclosure.

[0137] Bus 510 includes hardware, software or both, and couples the components of online data traffic billing equipment to each other. For example, but not limitation, the bus may include an accelerated graphics port (AGP) or other graphics bus, an enhanced industrial standard architecture (EISA) bus, a front-side bus (FSB), a hypertransport (HT) interconnect, an industrial standard architecture (ISA) bus, an infinite bandwidth interconnect, a low pin count (LPC) bus, a memory bus, a microchannel architecture (MCA) bus, a peripheral component interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a serial advanced technology attachment (SATA) bus, a video electronics standard association local (VLB) bus or other suitable bus or a combination of two or more of these. Where appropriate, bus 510 may include one or more buses. Although the present disclosure describes and illustrates a specific bus, the present disclosure considers any suitable bus or interconnect.

[0138] The present disclosure also provides a computer program product. When a computer program / instruction in the computer program product is executed by a processor of an electronic device, the above-mentioned Figures 1 to 3 The steps of the point cloud data downsampling method provided in the corresponding embodiment.

[0139] It should be clear that the present disclosure is not limited to the specific configurations and processes described above and shown in the figures. For the sake of simplicity, a detailed description of the known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present disclosure is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present disclosure.

[0140] The functional blocks shown in the structural block diagram described above can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application-specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present disclosure are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link through a data signal carried in a carrier. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memories, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, and the like. The code segments can be downloaded via computer networks such as the Internet, intranets, and the like. It should also be noted that the exemplary embodiments mentioned in the present disclosure describe some methods or systems based on a series of steps or devices. However, the present disclosure is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or they can be different from the order in the embodiment, or several steps can be performed simultaneously.

[0141] Aspects of the present disclosure are described above with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the functions / actions specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0142] Although the disclosure is disclosed as above, the disclosure is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the disclosure, so the protection scope of the disclosure should be based on the scope defined by the claims.

Claims

1. A downsampling method for point cloud data, characterized in that: include: Acquire an original point cloud data set, wherein the original point cloud data set is related to a point cloud acquisition device and includes data of at least one spatial point; Determine the area range of the target segmentation unit, the set of target segmentation units is used to divide the detection space of the point cloud acquisition device and form at least a part of the spherical space; Based on the area range of the target segmentation unit, determining the spatial points in the original point cloud data set that fall within the area range of the target segmentation unit; Downsampling is performed on the spatial points falling within the area range of the target segmentation unit, and the downsampling results of one or more target segmentation units are aggregated to obtain a downsampled point cloud data set.

2. The method according to claim 1, characterized in that The farther away from the point cloud acquisition device, the larger the volume of the target segmentation unit.

3. The method according to claim 1, characterized in that The area of ​​each target segmentation unit includes an area located between a first spherical surface and a second spherical surface with the point cloud acquisition device as the center, within a range defined by a first horizontal angle and a second horizontal angle, and within a range defined by a first vertical angle and a second vertical angle, wherein the first spherical surface and the second spherical surface are defined by a first radius and a second radius, respectively, the first horizontal angle, the second horizontal angle, the first vertical angle, and the second vertical angle are all center angles, and for different target segmentation units, at least one of the first radius, the second radius, the first horizontal angle, the second horizontal angle, the first vertical angle, and the second vertical angle is different.

4. The method according to claim 3, characterized in that Each of the target segmentation units has the same radius increment, or the radius increment of the target segmentation unit is positively correlated with the distance from the target segmentation unit to the point cloud acquisition device, wherein the radius increment is the radius increment between the first radius and the second radius; and / or A horizontal angle increment between the first horizontal angle and the second horizontal angle of each target segmentation unit is associated with a horizontal scanning angle of the point cloud acquisition device; and / or A vertical angle increment between the first vertical angle and the second vertical angle of each of the object segmentation units is associated with a vertical scanning angle of the point cloud acquisition device.

5. The method according to claim 4, characterized in that In the case where the radius increment of the target segmentation unit is positively correlated with the distance from the target segmentation unit to the point cloud acquisition device, the radius increment is correlated with the product of the first radius and the horizontal angle increment.

6. The method according to claim 1, characterized in that Determining the area range of the target segmentation unit includes: Acquire polar coordinate position information of a target space point, wherein the target space point is selected from the original point cloud data set; Based on the radius value, the horizontal angle value and the vertical angle value of the polar coordinate position information of the target space point, the area range of the target segmentation unit corresponding to the target space point is determined.

7. The method according to claim 1, characterized in that Determining the area range of the target segmentation unit includes: Based on information of one or more preset candidate segmentation units, the region range of the target segmentation unit is determined.

8. The method according to claim 6 or 7, characterized in that: After downsampling the spatial points falling within the area range of the target segmentation unit, the method further includes: The data of the spatial points falling within the area range of the target segmentation unit are removed from the original point cloud data set.

9. The method according to claim 1, characterized in that: The data of the spatial point includes polar coordinate position information or Cartesian coordinate position information of the spatial point. In the case where the data of the spatial point includes Cartesian coordinate position information of the spatial point, the method further includes: For each spatial point in the original point cloud data set, the Cartesian coordinate position information of the spatial point is converted into polar coordinate position information.

10. The method according to claim 1, characterized in that Downsampling is performed on the spatial points falling within the area range of the target segmentation unit, including: The centroid or the shape centroid of the space points falling within the area range of the target segmentation unit is determined, and the data of the centroid or the shape centroid is used as the down-sampled point cloud data corresponding to the target segmentation unit.

11. The method according to claim 1, characterized in that The method further comprises: Determine the total number of spatial points that fall within the region of the target segmentation unit; When the total number meets a preset condition, performing the downsampling process on the spatial points falling within the area range of the target segmentation unit; When the total number does not satisfy a preset condition, the downsampled point cloud data set does not include data of spatial points falling within the area range of the target segmentation unit.

12. The method according to any one of claims 1 to 7 and 9 to 11, characterized in that The downsampled point cloud data set includes a plurality of sampling points, and the method further includes: Based on the polar coordinate position information of the plurality of sampling points, determining target sampling points having the same horizontal angle value and vertical angle value; For target sampling points with the same horizontal angle value and vertical angle value, the target sampling point with the smallest radius value is retained, and other target sampling points are removed from the downsampled point cloud data set.

13. A downsampling device for point cloud data, characterized in that: include: An acquisition module, used to acquire an original point cloud data set, wherein the original point cloud data set is related to a point cloud acquisition device and includes data of at least one spatial point; A first determination module is used to determine the area range of the target segmentation unit, and the set of target segmentation units is used to divide the detection space of the point cloud acquisition device and form at least a part of the spherical space; A second determination module is used to determine the spatial points in the original point cloud data set that fall within the area range of the target segmentation unit based on the area range of the target segmentation unit; The processing module is used to perform downsampling processing on the spatial points falling into the area range of the target segmentation unit respectively, and summarize the downsampling results of one or more target segmentation units to obtain a downsampled point cloud data set.

14. A computer-readable storage medium, wherein the computer-readable storage medium is a non-volatile storage medium or a non-transient storage medium, and a computer program is stored thereon, wherein: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 12 are performed.

15. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor runs the computer program, the steps of the method according to any one of claims 1 to 12 are performed.

16. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 12 are implemented.

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