CN114384491A - Point cloud processing method and device for laser radar and storage medium - Google Patents
Point cloud processing method and device for laser radar and storage medium Download PDFInfo
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Abstract
The application discloses a point cloud processing method and device for a laser radar and a storage medium, wherein the method comprises the following steps: dividing the point cloud of the laser radar into different grids according to a preset angle range and resolution; traversing all points in each grid, acquiring the closest point and the farthest point in the grid, and performing adhesion point treatment; the blocking point treatment comprises: keeping points in a certain distance range of the closest point and the farthest point in the grid, and determining the rest points as adhesion points; and deleting the adhesion points. The point cloud data processed by the method is more reasonable, the obstacle can be avoided accurately, the path planning in automatic driving is greatly facilitated, and the driving safety is guaranteed.
Description
Technical Field
The embodiment of the application relates to a laser radar point cloud data processing technology, in particular to a point cloud processing method and device for a laser radar and a storage medium.
Background
With the development of industrial intelligence, the demands on 3D perception technology, especially laser radar technology, are increasing increasingly in the fields of automatic driving, robot obstacle avoidance, vehicle-road cooperation of smart cities, surveying and mapping and the like. In the case of environmental perception with lidar, there is often a case where: because the emitted light of the laser radar has a divergence angle, the formed light spot covers a certain area, and when a certain light spot simultaneously irradiates the boundary of two objects which are close to each other and are arranged in front of and behind, the generated echoes can be superposed together. As shown in fig. 2, the dotted line is an echo formed by a beam of light spots simultaneously hitting the boundary between two objects at the front and the rear, and the object boundaries are close to each other, and the solid line is an echo signal after actual superposition. Because the echoes formed by the front object and the rear object cannot be distinguished, the distance calculation is carried out according to the superposed echo signals in the actual signal processing, and the distance between the calculated distance and the object has great deviation, so that floating false point clouds appear between the edges of the front object and the rear object in the same direction, namely the point cloud adhesion phenomenon appears. For an automatic driving vehicle, if two obstacles in front are adjacent, the adhesion phenomenon of point clouds is easy to occur, the automatic driving vehicle senses according to the point clouds with adhesion, algorithms such as sensing route planning of the automatic driving vehicle are influenced, and vehicle speed adjustment is influenced.
Disclosure of Invention
In view of this, embodiments of the present application provide a point cloud processing method and apparatus for a laser radar, and a storage medium.
According to a first aspect of embodiments of the present application, there is provided a point cloud processing method for a lidar, including:
dividing the point cloud of the laser radar into different grids according to a preset angle range and resolution;
traversing all points in each grid, acquiring the closest point and the farthest point in the grid, and performing adhesion point treatment;
the blocking point treatment comprises: keeping points in a certain distance range of the closest point and the farthest point in the grid, and determining the rest points as adhesion points;
and deleting the adhesion points.
In some exemplary embodiments, the keeping of the points within a certain distance range of the closest point and the farthest point, and determining the remaining points as adhesion points, comprises:
and reserving the points in the first set distance range of the nearest point and the points in the second set distance range of the farthest point, and determining the rest points in the grid as adhesion points.
In some exemplary embodiments, the method further comprises:
and calculating the difference between the distance of the closest point and the distance of the farthest point, and performing adhesion point treatment when the difference is larger than a first set threshold value.
In some exemplary embodiments, the partitioning the point cloud of the lidar into different grids includes:
converting the point cloud of the laser radar into a representation by a spherical coordinate system;
dividing the point cloud represented by the spherical coordinate system into different grids; wherein the number of points in each grid is greater than or equal to a set value.
In some exemplary embodiments, the method further comprises:
and dividing the point cloud of the laser radar into different grids according to the coordinate information of the point cloud of the laser radar.
In some exemplary embodiments, the method further comprises:
determining a Region with a distance smaller than a second set threshold value in the point cloud of the laser radar as a Region of Interest (ROI);
correspondingly, the dividing the point cloud of the laser radar into different grids includes:
the point clouds contained by the ROIs are divided into different grids.
According to a second aspect of embodiments of the present application, there is provided a point cloud processing apparatus for a lidar, including:
the dividing unit is used for dividing the point cloud of the laser radar into different grids according to a preset angle range and resolution;
the acquisition unit is used for traversing all point clouds in each grid to acquire the closest point and the farthest point in the grid;
the adhesion point processing unit is used for processing adhesion points; the blocking point treatment comprises: keeping point clouds in a certain distance range of the closest point and the farthest point, and determining the rest point clouds as adhesion points;
and the deleting unit is used for deleting the adhesion points.
In some exemplary embodiments, the first determining unit is further configured to:
and reserving the points in the first set distance range of the nearest point and the points in the second set distance range of the farthest point, and determining the rest points in the grid as adhesion points.
In some exemplary embodiments, the apparatus further comprises:
and the calculation unit is used for calculating the difference between the distance of the closest point and the distance of the farthest point, and triggering the adhesion point processing unit to perform adhesion point processing under the condition that the difference is greater than a first set threshold value.
In some exemplary embodiments, the dividing unit is further configured to:
converting the point cloud of the laser radar into a representation by a spherical coordinate system;
performing rasterization processing on the point cloud represented by the spherical coordinate system; and the number of the point clouds in each grid is greater than or equal to a set value.
In some exemplary embodiments, the dividing unit is further configured to:
and dividing the point cloud of the laser radar into different grids according to the coordinate information of the point cloud of the laser radar.
In some exemplary embodiments, the apparatus further comprises:
a second determining unit, configured to determine a region with a distance smaller than a second set threshold as an ROI of the point cloud;
correspondingly, the dividing unit is further configured to: the point clouds contained by the ROIs are divided into different grids.
According to a third aspect of embodiments of the present application, there is provided a computer-readable storage medium having stored therein a computer program, which when executed by a processor, implements the steps of the point cloud processing method for lidar.
In the embodiment of the application, according to a preset angle range and a preset resolution ratio, the point cloud of the laser radar is divided into different grids, all points in each grid are traversed, the closest point and the farthest point in each grid are obtained, the points in a certain distance range of the closest point and the farthest point are reserved, the rest points are determined to be adhesion points, and the adhesion points are deleted. Because the points including the adhesion phenomenon are removed, the point cloud processed based on the embodiment of the application is more accurate in sensing the size of the obstacle, is beneficial to determining the corresponding route planning of the real obstacle size of an automatic driving vehicle and the like, can accurately avoid the obstacle and the like, greatly facilitates the route planning in automatic driving, and ensures the driving safety; the method and the device further support the determination of the areas which are likely to be adhered, and only the determination and deletion of the adhering points are performed on the areas which are likely to be adhered, so that the point cloud processing efficiency is improved.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below. It is obvious that the drawings in the following description are some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
FIG. 1 is a schematic diagram illustrating a scene in which point cloud data are stuck;
FIG. 2 is a diagram showing an echo signal of a laser signal in which sticking has occurred;
FIG. 3 shows a schematic diagram of a point cloud in which sticking occurs in a point cloud diagram;
fig. 4 is a schematic flowchart of a point cloud processing method for a laser radar according to an embodiment of the present disclosure;
FIG. 5 is a schematic diagram illustrating an example of a point cloud processing method for a lidar according to an embodiment of the present disclosure;
FIG. 6 is a schematic distribution diagram of a point cloud after being rasterized according to an embodiment of the present disclosure;
FIG. 7 shows a schematic point cloud with adhesion points removed from the point cloud;
fig. 8 is a schematic structural diagram of a point cloud processing apparatus for a lidar according to an embodiment of the present disclosure.
Detailed Description
The essence of the technical solution of the embodiments of the present application is explained in detail below with reference to the accompanying drawings.
Fig. 1 shows a scene schematic diagram of point cloud data adhesion, as shown in fig. 1, because laser spots emitted by a laser radar have a certain size, when one laser spot irradiates on a boundary of two objects (the distance is related to the width of a light emitting pulse) which are close to each other, the generated echoes are superposed together, and in the actual signal processing, distance calculation is performed according to the superposed echo signals, so that the distance between the calculated result and the object has a great deviation, and the size of an obstacle obtained by processing of a perception algorithm deviates from a true value.
Fig. 2 shows a schematic diagram of echo signals of laser signals with adhesion, as shown in fig. 2, a dotted line shows echo signals respectively formed at the boundary between two objects that are close to each other and are simultaneously irradiated by a beam of light spot, and a solid line shows echo signals after actual superposition. When the laser radar calculates the distance of the object according to the echo signal after superposition shown by the solid line in fig. 2, a large deviation exists between the actual distance and the distance. In practical application, if an adjacent and close obstacle exists in the traveling direction of the automatic driving vehicle, the route planning of the automatic driving vehicle is influenced, and the automatic driving vehicle cannot normally pass.
Fig. 3 shows a schematic diagram of point clouds in which adhesion occurs in the point cloud map, and as shown in fig. 3, on the complete point cloud map, the point cloud adhesion appears as floating point clouds, such as the point clouds shown in the circles in fig. 3, between the edges of the front and rear objects in the same direction.
Fig. 4 is a schematic flow diagram of a point cloud processing method for a lidar according to an embodiment of the present disclosure, and as shown in fig. 4, the point cloud processing method for a lidar according to the embodiment of the present disclosure includes the following steps:
In the embodiment of the application, in order to avoid the adhesion points in the point cloud of the laser radar, the adhesion points are searched for and deleted for the point cloud of the whole laser radar.
Specifically, the point cloud is rasterized according to a spherical coordinate system according to a preset angle range and a preset resolution. Converting the point cloud of the laser radar into a representation by a spherical coordinate system; and rasterizing the point cloud represented by the spherical coordinate system according to a preset angle range and a preset resolution, wherein the number of points in each grid is greater than or equal to a set value. In the embodiment of the application, the point cloud is divided into grids, and the adhesion points are searched according to the grids according to the distribution characteristics of the point cloud. Because the grid comprises a certain number of points, the points irradiated on the front object, the adhesion points and the points irradiated on the rear object can be contained in the grid with higher probability, and therefore, the grid can be divided into the grids, so that the searching accuracy and the searching efficiency of the adhesion points can be improved. The setting value here may be set to 4, i.e. to ensure that the number of points in one sub-grid is maintained around 4. Specifically, the set value may be 5, 6, or the like. The embodiments of the present application are not limited. When the grids are divided, in order to ensure the number of points in each grid, the size of the grid can be adjusted according to a preset angle range and the resolution of the point cloud data, so that the number of points in each grid is maintained at about a set value.
In the embodiment of the application, the point cloud of the laser radar can be divided into different grids according to the coordinate information of the point cloud of the laser radar. Namely, determining which grid the point cloud falls into according to the coordinate value of the point cloud, and dividing the point cloud into the corresponding grids. When the number of the point clouds in the grids does not meet the set number, the distribution angle range of the grids can be adjusted according to the resolution ratio of the point clouds, and finally the number of the point clouds in each grid is kept about the set number.
And step 402, traversing all points in each grid, acquiring the closest point and the farthest point in the grid, and performing adhesion point treatment.
Here, the blocking point treatment includes: and reserving points within a certain distance range of the nearest point and the farthest point in the grid, and determining the rest points as adhesion points.
In the embodiment of the present application, for each divided grid, the ranging value of the point in each grid is obtained, and the farthest point and the closest point in the grid are determined. And finding out the point cloud which is located in a first set distance range from the closest point and the point which is located in a second set distance range from the farthest point, and determining the points which are not the points in a certain distance range of the found closest point and the found farthest point as the adhesion points.
In the embodiment of the present application, the first set distance range may be the same as the second set distance range. For example, the first set distance range may be set to a range not exceeding 0.06m, and when a point in the range of 0.06m in the vicinity of the closest point in the grid is regarded as a normal point, the remaining points are regarded as sticking points. As an example, the second set distance range may be set to a range not exceeding 0.06m, and when a point in the range of 0.06m near the farthest point in the grid is regarded as a normal point, the remaining points are regarded as sticking points. It should be understood by those skilled in the art that the first set distance range and the second set distance range are only exemplary and not limiting.
In the embodiment of the present application, the first set distance range and the second set distance range may be different. For example, the first set distance range may be set to a range not exceeding 0.06m, and when a point in the range of 0.06m in the vicinity of the closest point in the grid is regarded as a normal point, the remaining points are regarded as sticking points. As an example, the second set distance range may be set to a range of not more than 0.08m, and when the point cloud in the range of 0.08m near the farthest point in the grid is regarded as a normal point cloud, the remaining points are sticky points. It should be understood by those skilled in the art that the first set distance range and the second set distance range are only exemplary and not limiting.
In the embodiment of the application, because the point cloud is divided into the grids under normal conditions, the points in the normal grids which do not contain the adhesion points are usually within a certain distance range, in order to avoid the mistaken deletion of the normal points, in the process of determining the adhesion points, the difference value between the distance of the closest point and the distance of the farthest point is calculated, and in the case that the difference value is greater than the first set threshold value, the points outside the certain distance range of the closest point and the farthest point in the grids are deleted. That is, the distance difference between the closest point and the farthest point in the grid may be determined, and only when the distance difference exceeds a first set threshold, it is determined that a stuck point exists in the grid region, and then a point within a first set distance range of the closest point and a point within a second set distance range of the farthest point are determined, and the found closest point and the found point outside the set distance range of the farthest point are both deleted as stuck points. If the difference between the distance of the farthest point cloud and the distance of the nearest point cloud in the grid is greater than 0.4m as the first set threshold may be set to 0.4m, it is considered that the stuck point cloud exists in the grid.
And step 403, deleting the adhesion points.
In the embodiment of the application, in order to improve the point cloud processing efficiency, the area where point cloud adhesion is likely to occur can be pre-judged, the area where point cloud adhesion is likely to occur is determined as far as possible, and adhesion points in different areas are deleted.
In the embodiment of the application, the relevant area where the laser radar may generate the adhesion point is determined first, so that the judgment of the adhesion point is directly performed in the relevant area, the computing resource of point cloud identification is saved, and the processing efficiency of the adhesion point identification is improved. When the distance between the obstacle and the laser radar is short, such as less than 1.6m, the pulse width of the echo signal of the laser radar is wide, and the probability that the laser echo signals of the two obstacles which are short is superposed together is increased, so that the adhesion phenomenon is generated. The pulse width Of the echo far away is narrow, so that the possibility that the echoes Of two obstacles close to each other are superposed together is reduced, and therefore, in the embodiment Of the present application, a Region with a distance smaller than a second set threshold is determined as a Region Of Interest (ROI) Of the point cloud, where the ROI is a Region where the adhesion points easily occur. In the embodiment of the application, the ROI area which possibly generates the adhesion points is determined firstly, so that the adhesion points are searched and deleted only in the ROI area, all point clouds of the whole laser radar are not required to be searched, and the point cloud processing efficiency is greatly improved. Therefore, the ROI area of the point cloud can be selected according to the distance or other areas which are likely to generate the adhesion points, and the original point cloud of the laser radar can be divided into the ROI point cloud and the non-ROI point cloud by determining the ROI area of the point cloud. Here, the second set threshold may be 1.6m, and those skilled in the art should understand that the second set threshold may also be other values such as 1.7m, 2.1m, etc., which are only exemplary.
In the embodiment of the application, the adhesion phenomenon possibly caused by the obstacle is determined according to the distance of the point cloud, for example, when the distance of the point determined according to the echo signal is less than the range of 1.6m, the adhesion phenomenon of the point cloud is easily generated, and the region where the point with the distance of less than 1.6m is located in the collected point cloud can be determined as the ROI region. In the embodiment of the present application, the edge region of the obstacle may be divided into an ROI region or the like according to the approximate shape of the obstacle.
Deleting all the searched adhesion points in the ROI area, taking the point cloud of the laser radar with the adhesion points deleted as effective point cloud, namely determining the point cloud contained in the ROI without the adhesion points and the point cloud in the non-ROI as the effective point cloud, and performing obstacle distance operation and other data processing analysis and the like.
The essence of the technical solution of the embodiments of the present application is further clarified below by specific examples.
Fig. 5 is a schematic diagram illustrating an example of a point cloud processing method for a lidar according to an embodiment of the present disclosure, and as shown in fig. 5, the point cloud processing method for the lidar according to the embodiment of the present disclosure includes the following processing steps:
step 1, selecting an ROI (region of interest) region for point clouds formed by all echo signals of the laser radar, wherein the ROI region can be selected according to distance or other regions possibly generating adhesion points, and dividing the original point clouds into ROI point clouds and non-ROI point clouds.
Since the pulse width of the echo at a long distance is narrow, the probability of the echoes of two objects being superimposed together is reduced, and thus the ROI region may be a region corresponding to a point cloud within a certain distance (e.g., less than 1.6 m).
And 2, rasterizing the point cloud according to a polar coordinate according to a preset angle range and a preset resolution. For point cloud coordinates (x, y, z) in each point cloud of the ROI region, inverse calculation is performed as (element, azimuth, distance) of a spherical coordinate system, and then, according to a preset angle range and resolution, rasterization division is performed on all points in the point cloud according to the spherical coordinate system, so as to divide the points into different grids.
Rasterization refers to respectively calculating and obtaining horizontal and vertical serial numbers (hori _ pos and vert _ pos) of the grid where each point is located according to a preset angle range and an angle resolution, and obtaining the horizontal and vertical serial numbers (hori _ pos and vert _ pos) of the grid where each point is located.
An example of coordinate determination for grid cell correspondence of a point cloud is as follows:
hori_pos = floor(azimuth-angle_hori_min) / angle_hori_resolution
vert_pos = floor(element-angle_vert_min)/ angle_vert_resolution
wherein, angle _ hori _ min and angle _ vert _ min represent the minimum horizontal and vertical angles of the point to be processed; angle _ hori _ resolution, angle _ vert _ resolution representing the horizontal and vertical resolutions of rasterization, respectively; floor (arg) is a floor rounding function, returning a maximum integer value no greater than arg.
And 3, calculating the closest point and the farthest point in each grid. All points in each grid are traversed, resulting in the distance of the closest point and the distance of the farthest point.
Fig. 6 is a schematic distribution diagram of a point cloud after being subjected to rasterization processing in an embodiment of the present application, as shown in fig. 6, in the diagram, reference numeral 0 denotes a divided grid unit, reference numeral 1 denotes two front and rear objects, and reference numeral 2 denotes a point cloud of a radar in a scanning manner, and the size of the grid can be adjusted according to a radar scanning resolution (i.e., a distance between points), so as to control the number of points in the grid, for example, the number of points in the grid is at least 4, and since a certain number of points are included, a larger probability in the grid includes points irradiated on the front object, adhesion points, and points irradiated on the rear object; for example, reference numeral 3 is a grid including points irradiated to the front object, adhesion points, and points irradiated to the rear object; the grid denoted by 3 has some points (schematically 1 in the figure) just illuminating the boundary of the front and rear objects.
And 4, judging whether the difference value of the closest point and the farthest point in each grid is larger than a certain first set threshold, if the difference value of the closest point and the farthest point in each grid is larger than the first set threshold, executing the step 5, otherwise, considering that the grid does not contain the adhesion point, and not executing the adhesion point processing in the step 5.
And 5, determining a point in each grid, wherein the distance between the point and the closest point is within a first set distance range, and the distance between the point and the farthest point is within a second set distance range, and regarding the rest points in the grids as adhesion points to be deleted.
In this embodiment of the present application, the point irradiated on the front obstacle and the point irradiated on the rear obstacle in the grid including the adhesion point are retained, and the adhesion point is deleted. For a normal grid that does not contain stuck points, the points in the normal grid are usually within a certain distance range; namely, the difference value between the distance of the farthest point and the distance of the nearest point is smaller than the first set threshold, and the adhesion point deleting operation is not executed, so that normal point cloud can be kept. Fig. 7 shows a schematic diagram of the point cloud after the sticky points are deleted, as shown in fig. 7.
And 6, deleting the adhesion points in the grid. And taking the point cloud data of the laser radar with the adhesion points deleted as effective point cloud data.
Fig. 8 is a schematic structural diagram of a point cloud processing apparatus for a laser radar according to an embodiment of the present disclosure, and as shown in fig. 8, the point cloud processing apparatus for a laser radar according to an embodiment of the present disclosure includes:
a dividing unit 80, configured to divide the point cloud of the laser radar into different grids according to a preset angle range and a preset resolution;
an obtaining unit 81, configured to traverse all point clouds in each grid, and obtain a closest point and a farthest point in the grid;
an adhesion point processing unit 82 for performing adhesion point processing; the blocking point treatment comprises: keeping point clouds in a certain distance range of the closest point and the farthest point, and determining the rest point clouds as adhesion point clouds;
and the deleting unit 83 is used for deleting the adhesion point cloud.
In some exemplary embodiments, the sticky point processing unit 82 is further configured to:
and reserving the points in the first set distance range of the nearest point and the points in the second set distance range of the farthest point, and determining the rest points in the grid as adhesion points.
On the basis of the point cloud processing apparatus for lidar shown in fig. 8, the point cloud processing apparatus for lidar according to the embodiment of the present application further includes:
and a calculation unit (not shown in fig. 8) configured to calculate a difference between the distance of the closest point and the distance of the farthest point, and trigger the adhesion point processing unit to perform adhesion point processing when the difference is greater than a first set threshold.
In some exemplary embodiments, the dividing unit 80 is further configured to:
converting the point cloud of the laser radar into a representation by a spherical coordinate system;
performing rasterization processing on the point cloud represented by the spherical coordinate system; and the number of the point clouds in each grid is greater than or equal to a set value.
In some exemplary embodiments, the dividing unit 80 is further configured to:
and dividing the point cloud of the laser radar into different grids according to the coordinate information of the point cloud of the laser radar.
On the basis of the point cloud processing apparatus for lidar shown in fig. 8, the point cloud processing apparatus for lidar according to the embodiment of the present application further includes:
a determination unit (not shown in fig. 8) for determining a region having a distance smaller than a second set threshold as the ROI of the point cloud;
correspondingly, the dividing unit 80 is further configured to: the point clouds contained by the ROIs are divided into different grids.
In an exemplary embodiment, the dividing Unit 80, the obtaining Unit 81, the sticky point Processing Unit 82, the deleting Unit 83, the calculating Unit, the determining Unit, and the like may be implemented by one or more Central Processing Units (CPUs), Graphics Processing Units (GPUs), Application Specific Integrated Circuits (ASICs), DSPs, Programmable Logic Devices (PLDs), Complex Programmable Logic Devices (CPLDs), Field Programmable Gate Arrays (FPGAs), general purpose processors, controllers, Micro Controllers (MCUs), microprocessors (microprocessors), or other electronic elements.
In the embodiment of the present application, the specific manner in which each unit in the point cloud processing apparatus for lidar shown in fig. 8 performs operations has been described in detail in the embodiment related to the method, and will not be described in detail here.
An embodiment of the present application further describes an electronic device, which includes: a processor and a memory for storing processor executable instructions, wherein the processor is configured to be capable of performing the steps of the point cloud processing method for lidar of the embodiments upon invocation of the executable instructions in the memory.
The embodiment of the present application further describes a computer-readable storage medium, in which a computer program is stored, and when being executed by a processor, the computer program implements the steps of the point cloud processing method for lidar of the embodiment.
It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that, in various embodiments of the present invention, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application. The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are only illustrative, for example, the division of the unit is only one logical function division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not present.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the present invention, and all such changes or substitutions are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (13)
1. A point cloud processing method for a lidar, the method comprising:
dividing the point cloud of the laser radar into different grids according to a preset angle range and resolution;
traversing all points in each grid, acquiring the closest point and the farthest point in the grid, and performing adhesion point treatment;
the blocking point treatment comprises: keeping points in a certain distance range of the closest point and the farthest point in the grid, and determining the rest points as adhesion points;
and deleting the adhesion points.
2. The method of claim 1, wherein the retaining points within a distance range of the closest point and the farthest point, and determining the remaining points as adhesion points comprises:
and reserving the points in the first set distance range of the nearest point and the points in the second set distance range of the farthest point, and determining the rest points in the grid as adhesion points.
3. The method according to claim 1 or 2, characterized in that the method further comprises:
and calculating the difference between the distance of the closest point and the distance of the farthest point, and performing adhesion point treatment when the difference is larger than a first set threshold value.
4. The method of claim 1 or 2, wherein the partitioning of the point cloud of the lidar into different grids comprises:
converting the point cloud of the laser radar into a representation by a spherical coordinate system;
dividing the point cloud represented by the spherical coordinate system into different grids; wherein the number of points in each grid is greater than or equal to a set value.
5. The method according to claim 1 or 2, characterized in that the method further comprises:
and dividing the point cloud of the laser radar into different grids according to the coordinate information of the point cloud of the laser radar.
6. The method according to claim 1 or 2, characterized in that the method further comprises:
determining a region with a distance smaller than a second set threshold value in the point cloud of the laser radar as a region of interest (ROI);
correspondingly, the dividing the point cloud of the laser radar into different grids includes:
the point clouds contained by the ROIs are divided into different grids.
7. A point cloud processing apparatus for a lidar, the apparatus comprising:
the dividing unit is used for dividing the point cloud of the laser radar into different grids according to a preset angle range and resolution;
the acquisition unit is used for traversing all point clouds in each grid to acquire the closest point and the farthest point in the grid;
the adhesion point processing unit is used for processing adhesion points; the blocking point treatment comprises: keeping point clouds in a certain distance range of the closest point and the farthest point, and determining the rest point clouds as adhesion points;
and the deleting unit is used for deleting the adhesion points.
8. The apparatus of claim 7, wherein the sticky point processing unit is further configured to:
and reserving the points in the first set distance range of the nearest point and the points in the second set distance range of the farthest point, and determining the rest points in the grid as adhesion points.
9. The apparatus of claim 7 or 8, further comprising:
and the calculation unit is used for calculating the difference between the distance of the closest point and the distance of the farthest point, and triggering the adhesion point processing unit to perform adhesion point processing under the condition that the difference is greater than a first set threshold value.
10. The apparatus according to claim 7 or 8, wherein the dividing unit is further configured to:
converting the point cloud of the laser radar into a representation by a spherical coordinate system;
performing rasterization processing on the point cloud represented by the spherical coordinate system; and the number of the point clouds in each grid is greater than or equal to a set value.
11. The apparatus according to claim 7 or 8, wherein the dividing unit is further configured to:
and dividing the point cloud of the laser radar into different grids according to the coordinate information of the point cloud of the laser radar.
12. The apparatus of claim 7 or 8, further comprising:
the determining unit is used for determining the area with the distance smaller than a second set threshold value as the ROI of the point cloud;
correspondingly, the dividing unit is further configured to: the point clouds contained by the ROIs are divided into different grids.
13. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which computer program, when being executed by a processor, realizes the steps of the point cloud processing method for lidar according to any one of claims 1 to 6.
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| CN202210293400.0A CN114384491B (en) | 2022-03-24 | 2022-03-24 | Point cloud processing method and device for laser radar and storage medium |
| PCT/CN2023/083413 WO2023179717A1 (en) | 2022-03-24 | 2023-03-23 | Point cloud processing method and apparatus for laser radar, device, and storage medium |
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| WO2023179717A1 (en) * | 2022-03-24 | 2023-09-28 | 北京一径科技有限公司 | Point cloud processing method and apparatus for laser radar, device, and storage medium |
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| CN117075171B (en) * | 2023-10-18 | 2024-01-16 | 新石器慧通（北京）科技有限公司 | Pose information determining method, device and equipment of laser radar and storage medium |
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