CN115685224A - Laser radar point cloud clustering method and device, laser radar and vehicle - Google Patents
Laser radar point cloud clustering method and device, laser radar and vehicle Download PDFInfo
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Abstract
The application belongs to the technical field of perception and relates to a radar imaging technology in the field. The method comprises the following steps: acquiring a first distance between a first point on an obstacle and a laser radar, a second distance between a second point on the obstacle and the laser radar, and included angle information between a laser beam corresponding to the first point on the obstacle and a laser beam corresponding to the second point on the obstacle; acquiring reference data from a pre-constructed clustering parameter table according to the first distance, the included angle information between the laser beam corresponding to the first point on the barrier and the laser beam corresponding to the second point on the barrier; the clustering parameter table is constructed according to internal parameters of the laser radar; clustering a second point on the obstacle and a first point on the obstacle according to the reference data and the second distance. Based on the technical scheme, a great amount of calculation force can be saved when the original point cloud data collected by the laser radar are clustered.
Description
Technical Field
The application relates to the technical field of radar imaging in perception, in particular to a laser radar point cloud clustering method and device, a laser radar and a vehicle.
Background
With the development of the unmanned technology, higher requirements are put forward on the performances of the vehicle-mounted radar such as detection distance, angular resolution and the like. The existing vehicle-mounted radar (for example, a laser radar) forms point cloud data when imaging a target, and in order to distinguish the targets detected at the same time, the point cloud data acquired by the vehicle-mounted radar needs to be clustered through a clustering algorithm.
The traditional clustering algorithm has high requirements on chip computing power, and therefore, the traditional clustering algorithm is not suitable for a scene with high computing resources like an on-board computing center.
Disclosure of Invention
In view of the above problems in the prior art, the present application provides a laser radar point cloud clustering method, device, laser radar and vehicle, the clustering process is simple, and the effect of saving calculation power is achieved.
In order to achieve the above object, a first aspect of the present application provides a laser radar point cloud clustering method, including:
acquiring a first distance between a first point on an obstacle and a laser radar, a second distance between a second point on the obstacle and the laser radar, and included angle information between a laser beam corresponding to the first point on the obstacle and a laser beam corresponding to the second point on the obstacle; acquiring reference data from a pre-constructed clustering parameter table according to the first distance, the included angle information between the laser beam corresponding to the first point on the barrier and the laser beam corresponding to the second point on the barrier; wherein, the clustering parameter table is constructed according to the internal parameters of the laser radar; clustering a second point on the obstacle and a first point on the obstacle according to the reference data and the second distance.
According to the laser radar point cloud clustering method provided by the first aspect of the application, the clustering parameter table is established through laser radar internal reference, the physical characteristics of the laser radar are considered, and clustering can be more accurate. Secondly, based on a pre-constructed clustering parameter table, the calculation amount in actual clustering can be simplified, and the method is very suitable for scenes with shortage of calculation resources.
As a possible implementation manner of the first aspect, the first point and the second point are adjacent points.
As a possible implementation manner of the first aspect, the internal reference of the lidar includes: information of an included angle between adjacent laser beams of the laser radar and the number of the laser radar wire harnesses.
As a possible implementation manner of the first aspect, the clustering parameter table is constructed according to internal parameters of the lidar, and specifically includes: acquiring included angle information between adjacent laser beams of the laser radar and detection multiples of the laser radar; wherein, the detection multiple of the laser radar is determined according to the detection distance of the laser beam and the detection distance resolution of the laser radar; establishing index information of a clustering parameter table by taking included angle information between adjacent laser beams as a first dimension and a detection multiple as a second dimension; and calculating according to the included angle information between the adjacent laser beams and the detection multiple to obtain reference data in the clustering parameter table.
By the above, the position of the point cloud collected by the laser radar can be accurately described by using the included angle information between the adjacent laser beams and the detection multiple of the laser radar as the index information of the clustering parameter table, so that the point cloud identification is unique. In addition, the included angle information between the adjacent laser beams and the detection multiple of the laser radar can be directly obtained by the factory information of the laser radar without additional processing, so that the clustering parameter table has the advantage of simple manufacture.
As a possible implementation manner of the first aspect, the included angle information includes a size of the longitudinal included angle and a size of the horizontal included angle.
As a possible implementation manner of the first aspect, the number of elements of the first dimension is determined according to the number of line beams of the laser radar.
As a possible implementation manner of the first aspect, the number of elements of the second dimension is determined according to a detection multiple of the laser radar.
As a possible implementation manner of the first aspect, the determining, by the detection multiple of the laser radar according to the detection distance of the laser beam and the detection distance resolution of the laser radar, includes: and dividing the detection distance of the laser beam by the detection distance resolution of the laser radar to obtain the detection multiple of the laser radar.
As a possible implementation manner of the first aspect, the reference data includes a first threshold and a second threshold, where the first threshold is greater than the second threshold.
As a possible implementation manner of the first aspect, obtaining reference data in the clustering parameter table by calculating according to information of an included angle between adjacent laser beams and a detection multiple includes: determining a third distance according to the detection multiple and the detection distance resolution of the laser radar; determining a first threshold value based on the third distance and a preset distance threshold value; and determining a second threshold value based on the third distance, the included angle information between the adjacent laser beams and a preset angle threshold value.
In view of the above, a method for determining a first threshold and a second threshold in reference data is provided. The method considers the detection distance of the laser radar, the included angle information between adjacent laser beams and the like, so that the calculation process is scientific and reasonable.
As a possible implementation manner of the first aspect, acquiring reference data from a pre-constructed clustering parameter table according to a first distance, and angle information between a laser beam corresponding to a first point on an obstacle and a laser beam corresponding to a second point on the obstacle, includes: acquiring a first dimension according to included angle information between a laser beam corresponding to a first point on the obstacle and a laser beam corresponding to a second point on the obstacle; determining a detection multiple corresponding to the first distance according to the first distance, and acquiring a second dimension according to the detection multiple corresponding to the first distance; and based on the first dimension and the second dimension, looking up a table from a pre-constructed clustering parameter table to obtain corresponding reference data.
According to the method, a specific process of inquiring the clustering parameter table is provided, the first dimension of the clustering parameter table is positioned through the included angle information between the laser beam corresponding to the first point and the laser beam corresponding to the second point, the second dimension of the clustering parameter table is positioned through the detection multiple corresponding to the first distance, and accordingly, the reference data corresponding to the first dimension and the second dimension can be obtained.
As a possible implementation manner of the first aspect, clustering a second point on the obstacle and a first point on the obstacle according to the reference data and the second distance includes: if the second distance is within the reference data range, the second point on the obstacle and the first point on the obstacle are in the same class; otherwise, the second point on the obstacle is not of the same type as the first point on the obstacle.
The second aspect of the present application provides a laser radar point cloud clustering device, including: the device comprises a first acquisition module, a second acquisition module and a clustering module. The first acquisition module is used for acquiring a first distance between a first point on the obstacle and the laser radar, a second distance between a second point on the obstacle and the laser radar, and included angle information between a laser beam corresponding to the first point on the obstacle and a laser beam corresponding to the second point on the obstacle; the second acquisition module is used for acquiring reference data from a pre-constructed clustering parameter table according to the first distance, and the included angle information between the laser beam corresponding to the first point on the obstacle and the laser beam corresponding to the second point on the obstacle; wherein, the clustering parameter table is constructed according to the internal parameters of the laser radar; and the clustering module is used for clustering the second point on the obstacle and the first point on the obstacle according to the reference data and the second distance.
As a possible implementation manner of the second aspect, the internal reference of the lidar in the second acquisition module includes: information of an included angle between adjacent laser beams of the laser radar and the number of the laser radar wire harnesses.
As a possible implementation manner of the second aspect, the clustering parameter table in the second obtaining module is constructed according to internal parameters of the laser radar, and specifically includes: the acquisition submodule is used for acquiring included angle information between adjacent laser beams of the laser radar and detection multiples of the laser radar; wherein, the detection multiple of the laser radar is determined according to the detection distance of the laser beam and the detection distance resolution of the laser radar; the construction submodule is used for constructing index information of a clustering parameter table by taking included angle information between adjacent laser beams as a first dimension and taking a detection multiple as a second dimension; and the calculating submodule is used for calculating and obtaining reference data in the clustering parameter table according to the included angle information between the adjacent laser beams and the detection multiple.
As a possible implementation manner of the second aspect, the included angle information includes a size of the longitudinal included angle and a size of the horizontal included angle.
As a possible implementation manner of the second aspect, the number of elements of the first dimension in the building submodule is determined according to the number of the line beams of the laser radar.
As a possible implementation manner of the second aspect, the number of elements of the second dimension in the building submodule is determined according to the detection multiple of the lidar.
As a possible implementation manner of the second aspect, the determining the detection multiple of the laser radar according to the detection distance of the laser beam and the detection distance resolution of the laser radar includes: and dividing the detection distance of the laser beam by the detection distance resolution of the laser radar to obtain the detection multiple of the laser radar.
As a possible implementation manner of the second aspect, the reference data in the calculation submodule includes a first threshold and a second threshold, where the first threshold is greater than the second threshold.
As a possible implementation manner of the second aspect, the computation submodule is specifically configured to: determining a third distance according to the detection multiple and the detection distance resolution of the laser radar; determining a first threshold value based on the third distance and a preset distance threshold value; and determining a second threshold value based on the third distance, the included angle information between the adjacent laser beams and a preset angle threshold value.
As a possible implementation manner of the second aspect, the second obtaining module is specifically configured to: acquiring a first dimension according to included angle information between a laser beam corresponding to a first point on the obstacle and a laser beam corresponding to a second point on the obstacle; determining a detection multiple corresponding to the first distance according to the first distance, and acquiring a second dimension according to the detection multiple corresponding to the first distance; and based on the first dimension and the second dimension, looking up a table from a pre-constructed clustering parameter table to obtain corresponding reference data.
As a possible implementation manner of the second aspect, the clustering module is specifically configured to: if the second distance is within the reference data range, the second point on the obstacle and the first point on the obstacle are of the same type.
A third aspect of the present application provides a lidar comprising: a communication interface; at least one processor coupled with the communication interface; and at least one memory coupled to the processor and storing program instructions that, when executed by the at least one processor, cause the at least one processor to perform a lidar point cloud clustering method of any of the first aspects.
A fourth aspect of the present application provides a vehicle comprising: the laser radar is used for acquiring point cloud data; and the processor is used for clustering the point cloud data acquired by the laser radar by using the laser radar point cloud clustering method in any one of the first aspect.
A fifth aspect of the present application provides a computing device, a computer readable storage medium having program instructions stored thereon, wherein the program instructions, when executed by a computer, cause the computer to perform a lidar point cloud clustering method according to any one of the first aspects.
A sixth aspect of the present application provides a computer program product, which, when run on a computing device, causes the computing device to execute a lidar point cloud clustering method according to any of the first aspects above.
These and other aspects of the present application will be more readily apparent in the following description of the embodiment(s).
Drawings
The various features and the connections between the various features of the present application are further described below with reference to the drawings. The figures are exemplary, some features are not shown to scale and some of the figures may omit features customary in the art to which this application relates and which are not essential to the application or show additional features which are not essential to the application, the combination of features shown in the figures is not intended to limit the application. In addition, the same reference numerals are used throughout the specification to designate the same components. The specific drawings are illustrated as follows:
FIG. 1 (a) is a front view of a three-dimensional coordinate system constructed with a laser radar as an origin;
FIG. 1 (b) is a side view of a three-dimensional coordinate system constructed with a lidar as an origin;
FIG. 1 (c) is a top view of a three-dimensional coordinate system constructed with a laser radar as an origin;
fig. 2 is a schematic view of a scene applied by the laser radar point cloud clustering method according to the embodiment of the present application;
fig. 3 is a schematic view of another scenario in which the laser radar point cloud clustering method provided in the embodiment of the present application is applied;
fig. 4 is a flowchart of a laser radar point cloud clustering method according to an embodiment of the present application;
fig. 5 is a flow chart of a construction of a clustering parameter table according to an embodiment of the present application;
FIG. 6 is a table of clustering parameters provided in an embodiment of the present application;
FIG. 7 is a schematic diagram of a point cloud data grid according to an embodiment of the present disclosure;
fig. 8 is a schematic structural diagram of a laser radar point cloud clustering device according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of a computing device according to an embodiment of the present application.
Detailed Description
The terms "first, second, third and the like" or "module a, module B, module C and the like in the description and in the claims, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order, it being understood that specific orders or sequences may be interchanged where permissible to effect embodiments of the present application in other than those illustrated or described herein.
In the following description, reference numerals indicating steps such as S110, S120 \ 8230; \8230, etc. do not necessarily indicate that the steps are performed in this order, and the order of the preceding and subsequent steps may be interchanged or performed simultaneously, where permitted.
The term "comprising" as used in the specification and claims should not be construed as being limited to the contents listed thereafter; it does not exclude other elements or steps. It is thus to be interpreted as specifying the presence of the stated features, integers, steps or components as referred to, but does not preclude the presence or addition of one or more other features, integers, steps or components, or groups thereof. Thus, the expression "an apparatus comprising the devices a and B" should not be limited to an apparatus consisting of only the components a and B.
Reference in the 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 application. Thus, 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, but may. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments, as would be apparent to one of ordinary skill in the art from this disclosure.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. In the case of inconsistency, the meaning described in the present specification or the meaning derived from the content described in the present specification shall control. In addition, the terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the present application.
To accurately describe the technical content in the present application and to accurately understand the present application, the terms used in the present specification are explained or defined as follows before describing the specific embodiments:
1) Lidar (Lidar): a radar system detects a target-related characteristic quantity by emitting a laser beam. The working principle is to transmit a detection signal (laser beam) to the target, then compare the received signal reflected from the target with the transmitted detection signal, and after appropriate processing, obtain the relevant information of the target, such as the distance, direction, height, etc. of the target.
2) Longitudinal angle, i.e. pitch angle (pitch): as shown in fig. 1, fig. 1 is a three-dimensional coordinate system constructed with the laser radar as an origin, in which fig. 1 (a) is a front view, fig. 1 (b) is a side view, and fig. 1 (c) is a top view. The p point is a point detected by the laser radar, and when the p point is projected to the X-Y plane, the included angle between the projection line and the Y axis is a longitudinal included angle, namely alpha in figure 1.
3) Horizontal angle, i.e. horizontal rotation angle (azimuth) or yaw (yaw): as shown in FIG. 1, when the p-point is projected on the X-Y plane, the angle between the projection line and the laser beam from the radar origin to the p-point is ω in FIG. 1.
4) Number of wire harnesses: i.e. the number of laser lines that the lidar can emit. For example: the number of corresponding beams is 16 for a 16-line lidar, 64 for a 64-line lidar, 8 for an 8-line lidar, and so on.
5) Point cloud clustering: and (3) merging the point cloud data points into a plurality of classes or clusters according to the characteristics, similarity or distance of the point cloud data points.
Firstly, a method for clustering point cloud data based on a laser radar provided by the related art is analyzed:
the point cloud clustering method of the scheme comprises the following steps: firstly, point cloud data acquired by a laser radar are converted to a corresponding logic plane, wherein the logic plane is used for describing the relative position relation between the laser radar origin and each point to be clustered; then, carrying out breadth-first search on each point on the corresponding logic plane to find out the same type point of each point; and finally, outputting the clustering result.
In the scheme, all links are obtained based on real-time online calculation, the calculation amount is high, the calculation power consumption of the precious vehicle-mounted calculation center is large, and therefore the method is not suitable for vehicle-mounted calculation.
The following describes embodiments of the present application in detail with reference to the accompanying drawings, and first introduces a scenario in which the laser radar point cloud clustering method provided in the embodiments of the present application is applied.
The laser radar point cloud clustering method provided by the embodiment of the application can be applied to Automatic Vehicles (AV) or intelligent driving vehicles. The application scenario may be that point cloud data acquired by a vehicle-mounted laser radar is clustered, and then the clustered data is used for subsequent calculation, for example: the subsequent calculation may be a target recognition calculation, etc.
Exemplarily, as shown in fig. 2, the method is a scenario applied to a laser radar point cloud clustering method provided in the embodiment of the present application. In this example, the lidar point cloud clustering method may be built into the lidar 10 entity. After the laser radar 10 collects the point cloud data, the point cloud data are clustered in the laser radar 10 body, and then the clustered data are transmitted to the host 20 through protocols such as a network for subsequent processing.
Exemplarily, as shown in fig. 3, another scenario to which the laser radar point cloud clustering method provided in the embodiment of the present application is applied is shown. In this example, the lidar point cloud clustering method may be located at the host end 20. The laser radar 10 transmits the acquired original point cloud data to the host 20, and the host 20 clusters the original point cloud data by using the laser radar point cloud clustering method provided by the embodiment of the application, so as to obtain clustered data for subsequent calculation.
It should be understood that fig. 2 and fig. 3 only show an exemplary scenario in which the laser radar point cloud clustering method is applied, and the application does not limit in which component the laser radar point cloud clustering method is stored, and in other application scenarios, the location where the laser radar point cloud clustering method is stored may be arbitrarily changed according to actual needs.
The laser radar point cloud clustering method provided by the embodiment of the present application is described in detail below with reference to the drawings. In the embodiments of the present application, the following description will be made by taking a laser radar as an example.
As shown in fig. 4, a flowchart of a laser radar point cloud clustering method provided in the embodiment of the present application is shown. The process mainly includes steps S110 to S130, and the following steps are described in sequence:
s110: the method comprises the steps of obtaining a first distance between a first point on an obstacle and a laser radar, a second distance between a second point on the obstacle and the laser radar, and included angle information between a laser beam corresponding to the first point on the obstacle and a laser beam corresponding to the second point on the obstacle.
As an alternative implementation, the first distance may be determined by a time difference between a transmission light emitted by the lidar to the first point and a reflected light reflected back from the first point to the lidar; similarly, the second distance may also be determined by a time difference between the transmitted light emitted by the lidar to the second point and the reflected light reflected back from the second point to the lidar. The information of the included angle between the laser beam corresponding to the first point and the laser beam corresponding to the second point can be obtained through a factory manual of the laser radar.
It should be understood that the angle information includes longitudinal angle information and horizontal angle information. For example, for a velodyne16 line mechanical laser radar, 16 laser beams can be emitted, and each two laser beams can form a pitch angle (longitudinal included angle), but the angle of each pitch angle is different in size; for the velodyne16 line mechanical lidar, it also includes a fixed aimuth angle (horizontal included angle).
Generally, the probability that neighboring points are of the same class is high, and therefore, the first point and the second point in this embodiment may be selected as neighboring points. And taking one point as a reference point to perform clustering operation on the other point.
S120: and acquiring reference data from a pre-constructed clustering parameter table according to the first distance obtained in the step S110, and the included angle information between the laser beam corresponding to the first point on the obstacle and the laser beam corresponding to the second point on the obstacle.
In the step, firstly, based on the included angle information between the laser beam corresponding to the first point on the barrier and the laser beam corresponding to the second point on the barrier, one dimension of the clustering parameter table can be determined in a table look-up mode; then according to the laser radar detection multiple corresponding to the first distance, determining the other dimension of the clustering parameter table in a table look-up mode; based on the two determined dimensions, corresponding clustering reference data in the clustering parameter table can be determined when the second point and the first point are clustered.
In this embodiment, the clustering parameter table is constructed according to the internal parameters of the lidar, wherein the internal parameters of the lidar include: the included angle information between adjacent laser beams of the laser radar and the number of the wire harnesses of the laser radar. In this embodiment, the included angle information includes the size of the longitudinal included angle and the size of the horizontal included angle; the number of line beams of the laser radar is the number of laser beams that can be emitted by the laser radar.
Specifically, the process of constructing the clustering parameter table may include steps S121 to S123, and as shown in fig. 5, the following describes the steps of constructing the clustering parameter table:
s121: and acquiring included angle information between adjacent laser beams of the laser radar and the detection multiple of the laser radar.
In this step, the information of the included angle between adjacent laser beams of the laser radar may include two pieces of information: the first aspect is the position relation of the included angle between the adjacent laser beams, namely whether the adjacent laser beams are a longitudinal included angle or a horizontal included angle; the second aspect is the size of the included angle. Generally, the information of the included angle between adjacent laser beams is set when the laser radar leaves a factory, and only the information needs to be determined according to the model of the laser radar.
In this step, the detection multiple of the laser radar refers to how many times of the laser radar distance resolution is experienced from the laser radar origin to each laser point. Generally, the detectable multiple of the laser radar is set when the laser radar leaves a factory, and only the detectable multiple needs to be determined according to the model of the laser radar. To facilitate understanding of the detection multiple concept of the lidar, it can be said that the detection multiple of the lidar is determined according to the detection distance of the laser beam and the detection distance resolution of the lidar, specifically: and multiplying the detection range resolution of the laser radar by the detection multiple of the laser radar to obtain the detection range under the detection multiple. For example: the resolution of the laser radar is 1m, and the detection multiple to a certain laser point is 5, so that the detection distance corresponding to the laser point is 1 × 5=5m.
S122: and establishing index information of the clustering parameter table by taking the included angle information between the adjacent laser beams as a first dimension and the detection multiple as a second dimension. The index information is used for indexing reference data in a clustering parameter table.
As an alternative implementation, the first dimension may be used as a row index dimension, and the second dimension may be used as a column index dimension. As another alternative implementation, the first dimension and the second dimension may be interchanged, that is: and taking the first dimension as a column index dimension and the second dimension as a row index dimension. This application is not intended to be limiting.
In this embodiment, the number of elements in the first dimension may be determined according to the number of the line beams of the laser radar. For example: to velodyne16 line machinery laser radar, its pencil quantity is 16, and 16 laser beams can be launched to this laser radar promptly, forms a vertical contained angle between per two laser beams, then this laser radar corresponds 15 vertical contained angles, and velodyne16 line machinery laser radar possesses a fixed horizontal contained angle, consequently, is 16 to the element number of its first dimension of velodyne16 line machinery laser radar.
In this embodiment, the number of elements in the second dimension is determined according to the detection multiple of the lidar. For example: for a lidar with a detection resolution of 2cm and a maximum detection multiple of 50, the radar has a maximum detectable distance of 2 x 50=100cm. For the radar, the number of the second dimension elements is determined by the maximum detection multiple of the laser radar, and then the number of the second dimension elements is 50. It should be understood that the radar resolution and the maximum detection multiple are only virtual settings for helping understanding the number of elements in the second dimension, and do not represent limitations on the lidar parameters.
S123: and calculating according to the included angle information between the adjacent laser beams and the detection multiple to obtain the reference data in the clustering parameter table.
In this step, the reference data includes a first threshold and a second threshold, wherein the first threshold is greater than the second threshold. For ease of understanding, the first threshold value may be replaced by upper limit data and the second threshold value by lower limit data hereinafter. It is to be understood that range data is constructed from the upper limit data and the lower limit data to cluster the relevant points.
In this step, as an optional implementation manner, first, a multiplication operation is performed on the detection multiple and the resolution of the laser radar to obtain a third distance. Then determining the upper limit data based on the third distance and a preset distance threshold; and determining lower limit data based on the third distance, the information of the included angle between the adjacent laser beams, and a preset angle threshold.
As an alternative implementation, the upper limit data upper may be determined according to the following formula:
upper＝ε+d 1
as an alternative implementation, the lower limit data floor may be determined according to the following formula:
wherein, d 1 ＝n*f，d 1 The method comprises the steps of calculating a detection distance corresponding to a point in a laser radar point cloud, namely a third distance, wherein n is a detection multiple of the laser radar, f is a detection distance resolution of the laser radar, epsilon is a preset distance threshold, beta is an included angle between adjacent laser beams, and theta is a preset angle threshold.
S130: and acquiring reference data from a pre-constructed clustering parameter table according to the first distance, and the information of the included angle between the laser beam corresponding to the first point on the obstacle and the laser beam corresponding to the second point on the obstacle.
In this step, when the second distance is within the upper and lower limits of the reference data, the second point on the obstacle and the first point on the obstacle are in the same class; correspondingly, when the second distance is not within the upper and lower limits of the reference data, the second point on the obstacle is not of the same type as the first point on the obstacle.
Specific implementation manners of the laser radar point cloud clustering method provided by the embodiment of the present application are described in detail below with reference to fig. 6 to 7.
The laser radar point cloud clustering method provided by the embodiment of the application is based on clustering performed by a clustering parameter table, so that the construction process of the clustering parameter table is introduced firstly. It should be understood that the clustering parameter table may be pre-constructed.
As an alternative implementation manner, the clustering parameter table may include two dimensions, the first dimension may be included angle information between adjacent laser beams, and specifically, the included angle information may include a size of a horizontal included angle and a size of a longitudinal included angle. The second dimension may be a detection multiple of the lidar, wherein the detection multiple of the lidar is determined based on a detection range of the laser beam and a detection range resolution of the lidar. For example, the maximum detectable range of the laser radar is 120m, the detection range resolution of the laser radar is 1m, and the detection multiple corresponding to the laser radar is any integer value from 0 to 120. In addition, the included angle information and the detection multiple of the laser radar can be obtained by a laser radar factory manual. For example: the first dimension of the velodyne16 line mechanical radar comprises 15 pitch angles and a fixed aimuth angle, i.e. 16 data. The second dimension of the clustering parameter table comprises 32500 data, so that the clustering parameter table of the velodyne 16-line mechanical radar shares 16 × 32500 data (elements).
Specifically, an element a (reference data) in the clustering parameter table is composed of an upper limit data upper and a lower limit data floor, i.e., a = (floor, upper). The calculation processes of floor and upper can be referred to the above embodiments, and the description thereof is omitted in this embodiment.
As shown in fig. 6, the method is a schematic diagram of a clustering parameter table constructed based on the above method for constructing a clustering parameter table. In this figure: the column index dimension is information of an included angle between adjacent laser beams, the row index dimension is a detection multiple of the laser radar, wherein 0-m is the detection multiple of the laser radar and can also be understood as a detection range resolution of the laser radar of m times, and the upper limit of the detection range resolution is a quotient of the maximum detectable range and the range resolution of the laser radar; pitch 1 -pitch m At a corresponding longitudinal angle between adjacent laser beams, aimuth 1 -aimuth w Is a corresponding horizontal included angle between adjacent laser beams; upper limit of the elements in the clustering parameter table, floor of the elements in the clustering parameter table. It should be understood that in this figureThe positions of the horizontal angle and the vertical angle in the clustering parameter table are only an example and are not limiting. In addition, the row index and the column index can also be interchanged, namely the column index is the detection multiple of the laser radar, and the row index is the information of the included angle between the adjacent laser beams.
Then, a process of clustering the laser radar point cloud based on the clustering parameter table shown in fig. 6 is described.
As shown in fig. 7, a schematic diagram of a point cloud data grid acquired by a lidar (only a part of the data is shown). In the figure, two adjacent points A1 and A2 are around a, and if a is regarded as a first point on the obstacle, i.e. a reference point, the process of respectively determining whether A1 and A2 are the same as a is as follows:
firstly, the included angle information between the reference point A and the points A1 and A2 to be clustered is obtained. Namely, a horizontal included angle is obtained between the reference point A and the point A1 to be clustered, the included angle is x, a vertical included angle is obtained between the reference point A and the point A2 to be clustered, and the included angle is y. Based on the information of the included angle between the reference point a and the points A1 and A2 to be clustered, the corresponding column information can be determined from the clustering parameter table shown in fig. 6.
Then, the distance from the reference point a to the origin of the laser radar is converted into the detection multiple of the laser radar, and the row information corresponding to the reference point a can be determined from the clustering parameter table shown in fig. 6.
And finally, determining whether the point A1 to be clustered, the point A2 to be clustered and the reference point A are in the same class by judging whether the distances from the point A1 to be clustered and the point A2 to be clustered to the original point of the laser radar are within the upper and lower limit ranges of the corresponding elements respectively. If the distance is within the range of the upper limit and the lower limit of the corresponding element, the point and the reference point are in the same class, otherwise, the point and the reference point are not in the same class.
Another embodiment of the present application provides a radar point cloud clustering apparatus, which may be implemented by a software system, a hardware device, or a combination of a software system and a hardware device.
It should be understood that fig. 8 is only a structural schematic diagram illustrating a lidar point cloud clustering device by way of example, and the present application does not limit the division of the functional modules in the head pose measurement device. As shown in fig. 8, the lidar point cloud clustering apparatus may be logically divided into a plurality of modules, each of which may have a different function, the functions of each module being implemented by instructions in a memory that may be read by a processor in a computing device and executed. Illustratively, the radar point cloud clustering device comprises a first obtaining module 710, a second obtaining module 720 and a clustering module 730. In an alternative implementation manner, the lidar point cloud clustering device is configured to perform the operations described in steps S110 to S130 shown in fig. 4. Specifically, the following may be mentioned: the first obtaining module 710 is configured to obtain a first distance between a first point on an obstacle and a lidar, a second distance between a second point on the obstacle and the lidar, and angle information between a laser beam corresponding to the first point on the obstacle and a laser beam corresponding to the second point on the obstacle. A second obtaining module 720, configured to obtain reference data from a pre-constructed clustering parameter table according to the first distance, and information of an included angle between the laser beam corresponding to the first point on the obstacle and the laser beam corresponding to the second point on the obstacle; wherein the clustering parameter table is constructed according to the internal parameters of the laser radar. A clustering module 730, configured to cluster the second point on the obstacle and the first point on the obstacle according to the reference data and the second distance.
Specifically, the internal reference of the lidar in the second obtaining module 720 includes: information of included angles between adjacent laser beams of the laser radar and the number of the line bundles of the laser radar.
Specifically, the clustering parameter table in the second obtaining module 720 is constructed according to the internal parameters of the lidar, and specifically includes: an acquisition sub-module 721, a construction sub-module 722, and a calculation sub-module 723. The obtaining submodule 721 is configured to obtain information of an included angle between adjacent laser beams of the laser radar and a detection multiple of the laser radar; wherein the detection multiple of the laser radar is determined according to the detection distance of the laser beam and the detection distance resolution of the laser radar. The constructing sub-module 722 is configured to construct index information of the clustering parameter table by using the included angle information between the adjacent laser beams as a first dimension and the detection multiple as a second dimension. And the calculating submodule 723 is used for calculating and obtaining the reference data in the clustering parameter table according to the included angle information between the adjacent laser beams and the detection multiple.
As an optional implementation manner, the included angle information includes a size of the longitudinal included angle and a size of the horizontal included angle.
As an alternative implementation manner, the number of elements of the first dimension in the building sub-module 722 is determined according to the number of beam lines of the lidar.
As an alternative implementation manner, the number of elements in the second dimension of the building submodule 722 is determined according to a detection multiple of the lidar.
As an optional implementation manner, the determining the detection multiple of the lidar according to the detection range of the laser beam and the detection range resolution of the lidar includes:
and dividing the detection distance of the laser beam by the detection distance resolution of the laser radar to obtain the detection multiple of the laser radar.
As an optional implementation manner, the reference data in the calculation sub-module 723 includes a first threshold and a second threshold, wherein the first threshold is greater than the second threshold.
As an optional implementation manner, the calculation sub-module 723 is specifically configured to: determining a third distance according to the detection multiple and the detection distance resolution of the laser radar; determining the first threshold based on the third distance and a preset distance threshold; and determining the second threshold value based on the third distance, the included angle information between the adjacent laser beams and a preset angle threshold value. As an optional implementation manner, the second obtaining module 720 is specifically configured to: acquiring the first dimension according to included angle information between the laser beam corresponding to the first point on the obstacle and the laser beam corresponding to the second point on the obstacle; determining a detection multiple corresponding to the first distance according to the first distance, and acquiring the second dimension according to the detection multiple corresponding to the first distance; and based on the first dimension and the second dimension, obtaining corresponding reference data from the pre-constructed clustering parameter table lookup table.
As an optional implementation manner, the clustering module 730 is specifically configured to: and if the second distance is within the reference data range, the second point on the obstacle and the first point on the obstacle are in the same class.
The specific implementation manner of each functional module in this embodiment may refer to the description in the foregoing method embodiment, and details of this embodiment are not described again.
Another embodiment of the present application provides a lidar including:
a communication interface;
at least one processor coupled with the communication interface; and:
the at least one memory is connected with the processor and stores program instructions, and when the program instructions are executed by the at least one processor, the at least one processor executes the laser radar point cloud clustering method according to the above embodiment, which is not described again in this embodiment.
Another embodiment of the present application provides a vehicle that may include the lidar and the processor of the previous embodiment. The processor is configured to cluster the point cloud data acquired by the laser radar by using the laser radar point cloud clustering method according to the embodiment. This application will not be described in detail.
Fig. 9 is a schematic structural diagram of a computing device 900 provided in an embodiment of the present application. The computing device 900 includes: a processor 910, a memory 920, and a communication interface 930.
It is to be appreciated that the communication interface 930 in the computing device 900 illustrated in FIG. 9 may be employed to communicate with other devices.
The processor 910 may be connected to the memory 920. The memory 920 may be used to store the program codes and data. Therefore, the memory 920 may be a storage unit inside the processor 910, an external storage unit independent of the processor 910, or a component including a storage unit inside the processor 910 and an external storage unit independent of the processor 910.
Optionally, computing device 900 may also include a bus. The memory 920 and the communication interface 930 may be connected to the processor 910 through a bus. The bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc.
It should be understood that, in the embodiment of the present application, the processor 910 may employ a Central Processing Unit (CPU). The processor may also be other general purpose processors, digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. Or the processor 910 may employ one or more integrated circuits for executing related programs to implement the technical solutions provided in the embodiments of the present application.
The memory 920 may include a read-only memory and a random access memory, and provides instructions and data to the processor 910. A portion of the processor 910 may also include non-volatile random access memory. For example, the processor 910 may also store device type information.
When the computing device 900 is running, the processor 910 executes the computer-executable instructions in the memory 920 to perform the operational steps of the above-described method.
It should be understood that the computing device 900 according to the embodiment of the present application may correspond to a corresponding main body for executing the method according to the embodiments of the present application, and the above and other operations and/or functions of each module in the computing device 900 are respectively for implementing corresponding flows of each method of the embodiment, and are not described herein again for brevity.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the technical solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
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, may be located in one position, or may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or parts of the technical solution may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is used, when executed by a processor, to execute a laser radar point cloud clustering method, where the method includes at least one of the solutions described in the above embodiments.
The computer storage media of embodiments of the present application may take any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, radio, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It should be noted that the foregoing is only illustrative of the preferred embodiments of the present application and the technical principles employed. It will be understood by those skilled in the art that the present application is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the application. Therefore, although the present application has been described in more detail with reference to the above embodiments, the present application is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present application.
Claims (26)
1. A laser radar point cloud clustering method is characterized by comprising the following steps:
acquiring a first distance between a first point on an obstacle and a laser radar, a second distance between a second point on the obstacle and the laser radar, and included angle information between a laser beam corresponding to the first point on the obstacle and a laser beam corresponding to the second point on the obstacle;
acquiring reference data from a pre-constructed clustering parameter table according to the first distance, and the included angle information between the laser beam corresponding to the first point on the obstacle and the laser beam corresponding to the second point on the obstacle; wherein the clustering parameter table is constructed according to the internal parameters of the laser radar;
clustering a second point on the obstacle and a first point on the obstacle according to the reference data and the second distance.
2. The method of claim 1, wherein the internal referencing of the lidar comprises: the included angle information between adjacent laser beams of the laser radar and the number of the wire harnesses of the laser radar.
3. The method according to claim 2, wherein the clustering parameter table is constructed according to the internal parameters of the lidar, and specifically comprises:
acquiring included angle information between adjacent laser beams of the laser radar and detection multiples of the laser radar; wherein the detection multiple of the laser radar is determined according to the detection distance of the laser beam and the detection distance resolution of the laser radar;
establishing index information of the clustering parameter table by taking the included angle information between the adjacent laser beams as a first dimension and the detection multiple as a second dimension;
and calculating according to the included angle information between the adjacent laser beams and the detection multiple to obtain the reference data in the clustering parameter table.
4. A method according to any of claims 1-3, wherein the angle information comprises the size of the longitudinal angle and the size of the horizontal angle.
5. The method of claim 3, wherein the number of elements in the first dimension is determined based on the number of beams of the lidar.
6. The method of claim 3, wherein the number of elements in the second dimension is determined according to a detection multiple of the lidar.
7. The method of claim 3, wherein the determining the detection multiple of the lidar based on the detection range of the laser beam and a detection range resolution of the lidar comprises:
and dividing the detection distance of the laser beam by the detection distance resolution of the laser radar to obtain the detection multiple of the laser radar.
8. The method of claim 3, wherein the reference data comprises a first threshold and a second threshold, wherein the first threshold is greater than the second threshold.
9. The method of claim 8, wherein the obtaining the reference data in the clustering parameter table according to the angle information between the adjacent laser beams and the detection multiple calculation comprises:
determining a third distance according to the detection multiple and the detection distance resolution of the laser radar;
determining the first threshold based on the third distance and a preset distance threshold;
and determining the second threshold value based on the third distance, the included angle information between the adjacent laser beams and a preset angle threshold value.
10. The method according to claim 3, wherein said obtaining reference data from a pre-constructed clustering parameter table according to the first distance, the angle information between the laser beam corresponding to the first point on the obstacle and the laser beam corresponding to the second point on the obstacle comprises:
acquiring the first dimension according to included angle information between the laser beam corresponding to the first point on the obstacle and the laser beam corresponding to the second point on the obstacle;
determining a detection multiple corresponding to the first distance according to the first distance, and acquiring the second dimension according to the detection multiple corresponding to the first distance;
and based on the first dimension and the second dimension, obtaining corresponding reference data from the pre-constructed clustering parameter table lookup table.
11. The method of claim 1, wherein said clustering a second point on the obstacle and a first point on the obstacle according to the reference data and the second distance comprises:
and if the second distance is within the reference data range, the second point on the obstacle and the first point on the obstacle are in the same class.
12. A radar point cloud clustering device is characterized by comprising:
the system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring a first distance between a first point on an obstacle and a laser radar, a second distance between a second point on the obstacle and the laser radar, and included angle information between a laser beam corresponding to the first point on the obstacle and a laser beam corresponding to the second point on the obstacle;
the second acquisition module is used for acquiring reference data from a pre-constructed clustering parameter table according to the first distance, and the included angle information between the laser beam corresponding to the first point on the obstacle and the laser beam corresponding to the second point on the obstacle; wherein the clustering parameter table is constructed according to the internal parameters of the laser radar;
and the clustering module is used for clustering a second point on the obstacle and a first point on the obstacle according to the reference data and the second distance.
13. The apparatus of claim 12, wherein the internal reference of the lidar in the second acquisition module comprises: the included angle information between adjacent laser beams of the laser radar and the number of the wire harnesses of the laser radar.
14. The apparatus according to claim 12, wherein the clustering parameter table in the second obtaining module is constructed according to internal parameters of the lidar, and specifically includes:
the acquisition submodule is used for acquiring included angle information between adjacent laser beams of the laser radar and detection multiples of the laser radar; wherein the detection multiple of the laser radar is determined according to the detection distance of the laser beam and the detection distance resolution of the laser radar;
the construction submodule is used for constructing the index information of the clustering parameter table by taking the included angle information between the adjacent laser beams as a first dimension and the detection multiple as a second dimension;
and the calculation submodule is used for calculating and obtaining the reference data in the clustering parameter table according to the included angle information between the adjacent laser beams and the detection multiple.
15. The apparatus according to any one of claims 12-14, wherein the angle information comprises a magnitude of a longitudinal angle and a magnitude of a horizontal angle.
16. The apparatus of claim 14, wherein the number of elements in the first dimension in the building submodule is determined based on the number of beams of the lidar.
17. The apparatus of claim 14, wherein the number of elements in the second dimension in the building submodule is determined according to a detection multiple of the lidar.
18. The apparatus of claim 14, wherein the detection multiple of the lidar is determined based on a detection range of the laser beam and a detection range resolution of the lidar, comprising:
and dividing the detection distance of the laser beam by the detection distance resolution of the laser radar to obtain the detection multiple of the laser radar.
19. The apparatus of claim 14, wherein the reference data in the computation sub-module comprises a first threshold and a second threshold, wherein the first threshold is greater than the second threshold.
20. The apparatus according to claim 19, wherein the computing submodule is configured to:
determining a third distance according to the detection multiple and the detection distance resolution of the laser radar;
determining the first threshold value based on the third distance and a preset distance threshold value;
and determining the second threshold value based on the third distance, the included angle information between the adjacent laser beams and a preset angle threshold value.
21. The apparatus of claim 14, wherein the second obtaining module is specifically configured to:
acquiring the first dimension according to included angle information between the laser beam corresponding to the first point on the obstacle and the laser beam corresponding to the second point on the obstacle;
determining a detection multiple corresponding to the first distance according to the first distance, and acquiring the second dimension according to the detection multiple corresponding to the first distance;
and based on the first dimension and the second dimension, obtaining corresponding reference data from the pre-constructed clustering parameter table lookup table.
22. The apparatus according to claim 12, wherein the clustering module is specifically configured to:
and if the second distance is within the reference data range, the second point on the obstacle and the first point on the obstacle are in the same class.
23. A lidar, comprising:
a communication interface;
at least one processor coupled with the communication interface; and
at least one memory coupled to the processor and storing program instructions that, when executed by the at least one processor, cause the at least one processor to perform a lidar point cloud clustering method of any of claims 1-11.
24. A vehicle, characterized by comprising:
the laser radar is used for acquiring point cloud data;
a processor for clustering the point cloud data collected by the lidar using the lidar point cloud clustering method of any of claims 1-11.
25. A computer readable storage medium having stored thereon program instructions, which when executed by a computer, cause the computer to perform a lidar point cloud clustering method of any of claims 1-11.
26. A computer program product, which, when run on a computing device, causes the computing device to perform a lidar point cloud clustering method of any of claims 1-11.
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| CN202110791099.1A CN115685224A (en) | 2021-07-13 | 2021-07-13 | Laser radar point cloud clustering method and device, laser radar and vehicle |
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| CN115877373B (en) * | 2023-02-20 | 2023-04-28 | 上海几何伙伴智能驾驶有限公司 | Method for realizing point cloud radar clustering parameter design by combining laser radar information |
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| CN110458055B (en) * | 2019-07-29 | 2021-10-15 | 江苏必得科技股份有限公司 | Obstacle detection method and system |
| CN111060923B (en) * | 2019-11-26 | 2022-05-13 | 武汉乐庭软件技术有限公司 | Multi-laser-radar automobile driving obstacle detection method and system |
| CN111144228B (en) * | 2019-12-05 | 2023-09-12 | 超越科技股份有限公司 | Obstacle identification method based on 3D point cloud data and computer equipment |
| CN111337941B (en) * | 2020-03-18 | 2022-03-04 | 中国科学技术大学 | Dynamic obstacle tracking method based on sparse laser radar data |
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