WO2021253429A1

WO2021253429A1 - Data processing method and apparatus, and laser radar and storage medium

Info

Publication number: WO2021253429A1
Application number: PCT/CN2020/097196
Authority: WO
Inventors: 朱晏辰; 刘政; 李延召
Original assignee: 深圳市大疆创新科技有限公司
Priority date: 2020-06-19
Filing date: 2020-06-19
Publication date: 2021-12-23
Also published as: CN114080545A

Abstract

A data processing method and apparatus, and a storage medium. The method comprises: acquiring point cloud data of the current frame (S110); and acquiring feature points from point cloud points included in the point cloud data, wherein the feature points comprise plane points and edge points, and the edge points comprise plane-plane intersecting edge points and/or jumping edge points (S120). The plane-plane intersecting edge points correspond to points on the line of intersection of intersecting planes in a three-dimensional space, and the jumping edge points correspond to points on an edge of an isolated plane in the three-dimensional space. By means of extracting edge points in point cloud data, the edge points are classified in a more refined manner, such that the feature extraction of the point cloud data can be applicable to various irregular point cloud patterns, thereby improving the accuracy of feature extraction of point cloud data.

Description

Data processing method, device, lidar and storage medium

manual

Technical field

This application generally relates to the field of laser detection technology, and more specifically relates to a data processing method, device, and storage medium.

Background technique

The traditional mechanical rotary scanning lidar has regular point cloud patterns, and simpler methods can be used to better complete point cloud feature extraction. With the evolution of lidar technology, semi-solid and solid-state lidars appear, and irregular point cloud patterns appear in lidar data, and many feature extraction methods are no longer applicable or the effect has deteriorated.

Summary of the invention

In order to solve the above-mentioned problems, the embodiments of the present application provide a data processing solution. The data processing solution proposed by the present application will be briefly described below, and more details will be described in the specific implementation manner in conjunction with the accompanying drawings.

According to one aspect of the present application, there is provided a data processing method, the method comprising: acquiring point cloud data of the current frame; acquiring feature points from the point cloud points included in the point cloud data, the feature points including plane points and An edge point, the edge point includes a face-to-face intersection edge point and/or a jump edge point, the face-to-face intersection edge point corresponds to a point on the boundary line of the intersecting faces in the three-dimensional space, and the jump edge point corresponds to the three-dimensional A point on the edge of an isolated surface in space.

According to another aspect of the present application, a data processing device is provided. The device includes a memory and a processor, and a computer program run by the processor is stored in the memory. The above data processing method is executed at runtime.

According to still another aspect of the present application, a laser radar is provided, the laser radar includes: a transmitting end device for transmitting an optical pulse signal; a receiving end device for receiving an echo signal corresponding to the optical pulse signal; and The processor is configured to obtain point cloud data based on the echo signal, and execute the above-mentioned data processing method on the point cloud data.

According to another aspect of the present application, a storage medium is provided, and a computer program is stored on the storage medium, and the computer program executes the above-mentioned data processing method when running.

When extracting edge points in point cloud data according to the data processing method, device, lidar, and storage medium of the embodiments of the present application, the edge points are classified in a more detailed manner, so that the feature extraction of point cloud data can be It is suitable for various irregular point cloud patterns and improves the accuracy of point cloud data feature extraction.

Description of the drawings

Fig. 1 shows a schematic flowchart of a data processing method according to an embodiment of the present application.

Fig. 2 shows an exemplary schematic diagram of an edge point where a surface intersects obtained in a data processing method according to an embodiment of the present application.

Fig. 3 shows an exemplary schematic diagram of jumping edge points obtained in the data processing method according to an embodiment of the present application.

Fig. 4 shows a schematic flow chart of obtaining a plane point in a data processing method according to an embodiment of the present application.

FIG. 5 shows a schematic flow chart of obtaining the edge points of the intersection of surfaces in the data processing method according to an embodiment of the present application.

Fig. 6 shows a schematic flow chart of obtaining a jumping edge point in a data processing method according to an embodiment of the present application.

Fig. 7 shows a schematic flow chart of acquiring edge points of small objects in a data processing method according to an embodiment of the present application.

Fig. 8 shows a schematic block diagram of a data processing device according to an embodiment of the present application.

Fig. 9 shows a schematic block diagram of a lidar according to an embodiment of the present application.

FIG. 10 shows a schematic structural diagram of a distance measuring device that can be used to collect point cloud data in a data processing method according to an embodiment of the present application.

FIG. 11 shows a schematic diagram of an embodiment in which a distance measuring device that can be used to collect point cloud data in a data processing method according to an embodiment of the present application adopts a coaxial optical path.

FIG. 12 shows a schematic diagram of a scanning pattern of the distance measuring device shown in FIG. 11.

detailed description

In order to make the objectives, technical solutions, and advantages of the present application more obvious, the exemplary embodiments according to the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited by the example embodiments described herein. Based on the embodiments of this application described in this application, all other embodiments obtained by those skilled in the art without creative work should fall within the protection scope of this application.

In the following description, a lot of specific details are given in order to provide a more thorough understanding of this application. However, it is obvious to those skilled in the art that this application can be implemented without one or more of these details. In other examples, in order to avoid confusion with this application, some technical features known in the art are not described.

It should be understood that this application can be implemented in different forms and should not be interpreted as being limited to the embodiments presented here. On the contrary, the provision of these embodiments will make the disclosure thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

The purpose of the terms used here is only to describe specific embodiments and not as a limitation of the present application. When used herein, the singular forms of "a", "an" and "the/the" are also intended to include plural forms, unless the context clearly indicates otherwise. It should also be understood that the terms "composition" and/or "including", when used in this specification, determine the existence of the described features, integers, steps, operations, elements and/or components, but do not exclude one or more other The existence or addition of features, integers, steps, operations, elements, components, and/or groups. As used herein, the term "and/or" includes any and all combinations of related listed items.

In order to thoroughly understand this application, detailed steps and detailed structures will be proposed in the following description to explain the technical solution proposed by this application. The preferred embodiments of the present application are described in detail as follows. However, in addition to these detailed descriptions, the present application may also have other implementation modes.

First, a data processing method according to an embodiment of the present application will be described with reference to FIG. 1. Fig. 1 shows a schematic flowchart of a data processing method 100 according to an embodiment of the present application. As shown in FIG. 1, the data processing method 100 according to the embodiment of the present application may include the following steps:

In step S110, the point cloud data of the current frame is acquired.

In step S120, a feature point is obtained from the point cloud points included in the point cloud data, the feature point includes a plane point and an edge point, and the edge point includes a surface intersection edge point and/or a jump edge point. Wherein, the surface-surface intersection edge point corresponds to a point on the boundary of the intersecting surfaces in the three-dimensional space, and the jumping edge point corresponds to a point on the edge of an isolated surface in the three-dimensional space.

In the embodiment of the present application, the feature extraction operation can be performed on the acquired frame of point cloud data, that is, feature points are extracted from the point cloud points included in the frame of point cloud data. In the embodiment of the present application, the extracted feature points include plane points and edge points. The plane points can be points located on a plane in the real scene, and the edge points can be planes, objects, thin rods, etc. in the real scene. The point on the edge. In an embodiment of the present application, the obtained edge point may include a surface intersection edge point and/or a jump edge point. Wherein, the surface-surface intersection edge point may be a point on the boundary line corresponding to the intersecting surfaces in the three-dimensional space, such as the point cloud point (scan point) A on the boundary line of the two surfaces S1 and S2 shown in FIG. 2 . The jumping edge point may be a point on the edge corresponding to the isolated surface in the three-dimensional space, such as the point cloud point (scan point) B shown in FIG. 3. Figure 3 is a top view angle view, where S3 is a surface in three-dimensional space, S4 is another surface in three-dimensional space, S3 does not intersect with S4, it is an isolated surface, when the laser light emitted by the lidar passes through the edge point of S3 When B shoots back on S4, the point cloud formed on S4 is not an edge point, so point B on the edge of S3 is called a jumping edge point.

In the embodiment of the present application, when extracting the edge points in the point cloud data, the edge points are classified in a more refined manner (including the edge points where the faces meet and/or the jumping edge points), so that the point cloud The feature extraction of data can be applied to various irregular point cloud patterns to improve the accuracy of point cloud data feature extraction.

The following describes an exemplary process of acquiring various feature points in the data processing method according to the embodiment of the present application in conjunction with FIG. 4 to FIG. 7.

FIG. 4 shows a schematic flowchart of a process 400 of obtaining a plane point in a data processing method according to an embodiment of the present application. As shown in FIG. 4, the process 400 may include the following steps:

In step S410, a group of point cloud points are acquired in a time series from the point cloud data, and it is determined whether the acquired group of point cloud points meets a first preset condition.

In step S420, when the acquired set of point cloud points meets the first preset condition, the set of point cloud points are determined as planar point candidate points, and the next set of point cloud points are acquired to perform the It is determined that the next group of point cloud points includes at least one point cloud point in the previous group of point cloud points.

In step S430, a final plane point extraction result of the current frame point cloud data is obtained based on the determined plane point candidate points.

In the embodiment of the present application, when the plane points are obtained from the point cloud data, they can be judged group by group in a grouping manner to improve efficiency. For example, a group of point cloud points can be acquired at a time based on a sliding window, and the number of point cloud points in each group depends on the size of the sliding window. In the embodiment of the present application, it can be determined whether the acquired set of point cloud points meets the first preset condition, and if so, the set of point cloud points can be determined as planar point candidate points, and the next set of point cloud points can be obtained. The point cloud point performs the same judgment; after traversing the entire point cloud pattern to obtain all the plane point candidate points, the final plane point extraction result is obtained based on the plane point candidate points.

In an embodiment of the present application, the aforementioned first preset condition may include: the spatial distribution of the acquired set of point cloud points is approximately a straight line, and the set of point cloud points is approximately center-symmetric when the center point is the center. Here, it should be understood that the expression “the spatial distribution of the acquired set of point cloud points is approximately a straight line” means that the spatial distribution of the acquired set of point cloud points does not necessarily completely coincide with the trajectory of a straight line, but satisfies Approximate coincidence is sufficient, and a certain calculation can be used to determine whether a certain condition is satisfied, and it can be considered as approximate coincidence. Of course, if the spatial distribution of the acquired set of point cloud points completely coincides with a straight line, it is also a situation that satisfies the first preset condition. Similarly, the expression "the group of point cloud points is approximately centrally symmetrical when centered on the intermediate point" means that when the group of point cloud points are centered on the intermediate point, it does not necessarily appear to be completely centrally symmetrical, but just satisfies a certain degree , It can be judged whether the symmetry of this degree is satisfied through certain calculation, and it can be regarded as approximately centrosymmetric. Of course, if the acquired set of point cloud points is completely centrally symmetric with the intermediate point as the center, it also belongs to the situation that satisfies the first preset condition. Through the limitation of the first preset condition, a more accurate plane point acquisition result can be obtained.

In the embodiment of the present application, the method of Principal Components Analysis (PCA for short) can be used to determine whether the spatial distribution of the acquired set of point cloud points is approximately a straight line. In the embodiment of the present application, other suitable methods can also be used to determine whether the spatial distribution of the acquired set of point cloud points is approximately a straight line.

In the embodiment of the present application, when the acquired group of point cloud points meets the first preset condition, the group of point cloud points are determined as planar point candidate points, and the next group of point cloud points is acquired to proceed. The judgment. In addition, because the above-mentioned candidate points of the plane point may be located at the edge of the surface and thus are edge points, the next group of point cloud points should be judged so that the next group of point cloud points should at least include the previous group of point clouds A point cloud point in the point.

In an embodiment of the present application, after obtaining all the plane point candidate points in the point cloud data of the frame in the above manner, the determined plane point candidate points can be directly used as the final plane point extraction of the point cloud data of the current frame result. In this embodiment, the plane point extraction result can be obtained efficiently.

In another embodiment of the present application, after all the plane point candidate points in the point cloud data of the frame are obtained in the above manner, the determined plane point candidate points may be determined according to the satisfaction of the first preset condition The level is sorted, and based on the sorting result, some plane point candidate points are selected as the final plane point extraction result of the point cloud data of the current frame. As mentioned above, the first preset condition requires that the spatial distribution of the acquired set of point cloud points is approximately a straight line, and the spatial distribution of the acquired set of point cloud points can be determined by calculation to coincide with a straight line According to the degree of coincidence and preset conditions (such as setting a threshold for the degree of coincidence), it is determined whether the approximate is a straight line; in addition, the first preset condition requires a set of point cloud points to be acquired to be the middle point The center is approximately centrally symmetrical, and the obtained set of point cloud points can be calculated to determine the degree of central symmetry. According to the degree of satisfaction and preset conditions (for example, setting a threshold for the degree of satisfaction) to determine whether it is satisfied Approximate center symmetry. Based on this, it can be understood that the degree to which the acquired set of point cloud points meets the first preset condition is available. Therefore, in this embodiment, the partial plane point candidate points with the highest degree of satisfaction can be sorted according to the degree and obtained as the current The final plane point extraction result of the frame point cloud data, so as to obtain a more accurate plane point extraction result.

In another embodiment of the present application, after all the candidate points of the plane point in the point cloud data of the frame are obtained in the above-mentioned manner, the point cloud data of the current frame may be divided into several regions, and the point cloud data in each region can be divided into several regions. The plane point candidate points are sorted according to the degree of satisfaction of the first preset condition, and a part of the plane point candidate points selected from each region based on the sorting result is used as the final plane point extraction result of the current frame point cloud data. A slight difference from the previous embodiment is that in this embodiment, instead of sorting all planar point candidate points of a frame of point cloud data, the final planar point extraction result is obtained, but a frame of point cloud data is first divided For several regions, perform such sorting in each region, and then sort the top plane point candidate points in each region as the final plane point extraction result. In this way, the clustering of feature points can be avoided, so that the obtained plane points are more evenly distributed on the point cloud pattern, which is beneficial to the subsequent processing of each region after the feature is extracted.

In an example, the regions can be divided according to the exit angle of the laser radar. For example, the laser radar scans in 360 degrees, and every 30 degrees can be used as an area to obtain a total of 12 areas and so on. In another example, the entire point cloud pattern can be divided into grids, and each grid serves as a region. In other examples, the area can also be divided in any other suitable manner, as long as the finally extracted plane points can be distributed throughout the point cloud pattern instead of being concentrated in one place.

In a further embodiment of the present application, when the acquired set of point cloud points does not meet the first preset condition, one point cloud point can be backed forward according to the time sequence and a point cloud point can be obtained starting from the point cloud point to which it is backed down. The group of point cloud points performs the judgment. For example, assuming that each group includes 5 point cloud points, when judging a group of point cloud points including the 5th point cloud point to the 9th point cloud point in accordance with the time sequence of all the point cloud points, if the group of point cloud points If the point does not satisfy the aforementioned first preset condition, one point cloud point can be backed forward, that is, a group of point cloud points including the 4th point cloud point to the 8th point cloud point is obtained for judgment, and so on.

In a further embodiment of the present application, before acquiring a set of point cloud points in time series from the point cloud data, the point cloud data of the current frame can be sorted according to the depth value, and the median value is selected as the scene scale threshold, and based on The scene scale threshold determines the size of the sliding window. In this embodiment, the size of the sliding window is selected according to the scene scale, a large sliding window is used for a large scene, and a small sliding window is used for a small scene, so that the plane point acquisition process can obtain accurate results while improving efficiency.

The above shows the process of obtaining the plane point in the data processing method according to the embodiment of the present application. It should be understood that the process is only exemplary, and any other suitable method may be used to obtain the plane point.

FIG. 5 shows a schematic flow chart of the process 500 of the intersecting edge points of the surface in the data processing method according to an embodiment of the present application. As shown in FIG. 5, the process 500 may include operations of performing the following steps for the point cloud points in the point cloud data:

In step S510, it is determined whether the two groups of point cloud points before and after the current point cloud point are located meet the first preset condition.

In step S520, when the two groups of point cloud points before and after the current point cloud point meet the first preset condition, it is determined whether the two groups of point cloud points meet the second preset condition.

In step S530, when the two groups of point cloud points meet the second preset condition, the current point cloud point is determined as the edge point where the surface intersects.

In the embodiment of the present application, it can be judged point by point whether a point cloud point is an edge point where a surface intersects. When judging whether a point cloud point is an edge point where a surface is intersected, it is first necessary to judge whether the two groups of point cloud points before and after the point satisfies the aforementioned first preset condition, that is, judge the two groups of point cloud points before and after the point. Whether it is a plane point candidate. For example, assuming that each group includes 5 point cloud points, when judging whether the fifth point cloud point of all the point cloud points according to the time sequence is the edge point where the surface intersects, the previous group of point cloud points (ie Whether the first point cloud point to the fifth point cloud point) and the latter group of point cloud points (that is, the fifth point cloud point to the ninth point cloud point) all satisfy the aforementioned first preset condition. Based on this, the foregoing plane point acquisition process and the surface intersection edge point acquisition process may be performed simultaneously or sequentially.

After determining that the two groups of point cloud points before and after the current point cloud point satisfies the first preset condition, it is possible to continue to determine whether the two groups of point cloud points before and after the current point cloud point satisfies the second preset condition. In the embodiment of the present application, the second preset condition may include: the maximum value of the distance between any two points in each group of point cloud points in the two groups of point cloud points before and after the current point cloud point satisfies the first threshold range , The angle of the direction vector formed by the two groups of point cloud points at the current point cloud point satisfies the second threshold range, and the direction vector formed by the two groups of point cloud points at the current point cloud point is the same as the current point The angle between the exit directions of the cloud points meets the third threshold range.

In the embodiment of the present application, the first threshold range may be [a1, +∞), that is to say, if a point cloud point is an edge point where a surface intersects, the two groups of point cloud points before and after the point cloud point are located The maximum value of the distance between any two points in each group of point cloud points should be greater than or equal to the threshold a1. In the embodiment of the present application, the second threshold range may be [b1, b2], that is to say, if a point cloud point is an edge point where a surface intersects, the two groups of point cloud points before and after the point cloud point are located respectively The angle of the formed direction vector should be greater than or equal to b1 and less than or equal to b2. In an example, b1 may be 45 degrees; b2 may be 135 degrees. In the embodiment of the present application, the third threshold range may include a threshold range, that is, if a point cloud point is an edge point where a surface intersects, the direction vectors formed by the two groups of point cloud points before and after the point cloud point are respectively The angle with the exit direction of the current point cloud point should all satisfy the threshold range; or, the third threshold range may include two threshold ranges, that is, if a point cloud point is an edge point where a surface intersects, the point cloud The angle between the direction vector formed by the previous group of point cloud points where the point is located and the exit direction of the current point cloud point should meet one of the two threshold ranges, and the latter group of point cloud points where the point cloud point is located The angle between the formed direction vector and the exit direction of the current point cloud point should satisfy the other of the two threshold ranges.

In the embodiment of the present application, for a point cloud point, when the two groups of point cloud points before and after the point cloud point meet the aforementioned first preset condition and the aforementioned second preset condition, then the The point cloud point is determined as the edge point where the surface intersects.

The above shows the process of obtaining the surface-to-surface intersection edge point in the data processing method according to the embodiment of the present application. It should be understood that the process is only exemplary, and any other suitable method may be used to obtain the surface-to-surface intersection edge point.

FIG. 6 shows a schematic flowchart of a process 600 of jumping edge points in a data processing method according to an embodiment of the present application. As shown in FIG. 6, the process 600 may include operations of performing the following steps for the point cloud points in the point cloud data:

In step S610, it is determined whether the difference between the distance between the current point cloud point and the two points before and after it is greater than a predetermined threshold, wherein a point closer to the current point cloud point among the two points before and after is defined as a near side point.

In step S620, when the difference between the distance between the current point cloud point and the two points before and after it is greater than the predetermined threshold, the current point cloud point is determined as a jumping candidate point.

In step S630, the point cloud point in the group where the near side point is located in the two groups of point cloud points where the jumping candidate point is located is defined as the near side group point cloud point, and it is determined whether the near side group point cloud point satisfies the third Pre-conditions.

In step S640, when the near-side group of point cloud points meets the third preset condition, the jumping candidate point is determined as a jumping edge point.

In the embodiment of the present application, it can be judged point by point whether a point cloud point is a jumping edge point. When judging whether a point cloud point is a jumping edge point, it is mainly based on the two point cloud points before and after the point cloud point (according to the time sequence) and the two groups of point cloud points before and after the current point cloud point. For ease of description, assuming that the current point cloud point is the fifth point cloud point in time sequence, the point cloud point before it is the fourth point cloud point, and the point cloud point behind it is the sixth point cloud point. Assume that the distance between the fourth point cloud point and the fifth point cloud point of the two point cloud points is d1, the distance between the sixth point cloud point and the fifth point cloud point is d2, and d1 Less than d2, the 4th point cloud point is called the near side point of the 5th point cloud point, the 6th point cloud point is called the far side point of the 5th point cloud point, and the 5th point cloud point is located Two groups of point cloud points before and after (taking each group of point cloud points including 5 point cloud points as an example, that is, the first group of point cloud points is the first to the fifth point cloud point, the latter group of point cloud points From the 5th point cloud point to the 9th point cloud point), the group where the near side point (4th point cloud point) is located is called the near side group point cloud point (that is, the previous group of point cloud points-the first Point cloud point to the fifth point cloud point), the group where the far side point (the sixth point cloud point) is located in the two groups of point cloud points before and after the fifth point cloud point is called the far side group point cloud point (ie The latter group of point cloud points-the 5th point cloud point to the 9th point cloud point). Further, if the difference between d2 and d1 is greater than a predetermined threshold, the fifth point cloud point may be determined as a jumping candidate point. Further, if the fifth point cloud point has been determined to be a jumping candidate point, it can be determined whether the aforementioned near-side group point cloud points (the first point cloud point to the fifth point cloud point) meet the third preset If the conditions are met, the fifth point cloud point can be determined as the jumping edge point.

In the embodiment of the present application, the third preset condition may include: the near-side group of point cloud points satisfy the first preset condition, the direction vector formed by the near-side group of point cloud points and the jump candidate The angle of the direction vector formed by a group of point cloud points on the other side of the point (that is, the far-side group of point cloud points) meets the fourth threshold range, the distance between any two points in the near-side group of point cloud points The maximum value of satisfies the fifth threshold range, and the angle between the direction vector formed by the near-side group of point cloud points and the exit direction of the jump candidate point satisfies the sixth threshold range. Following the previous example, assuming that a group of point cloud points includes 5 point cloud points, the current point cloud point is the 5th point cloud point according to the time sequence, if the near side group point cloud point of the current point cloud point (the first point The cloud point to the fifth point cloud point) satisfies the third preset condition, which means: the near-side group of point cloud points should meet the aforementioned first preset condition, that is, the near-side group of point cloud points should first be flat Point candidate points; in addition, the angle between the near side group point cloud points and the far side group point cloud points (the 5th point cloud point to the 9th point cloud point) should meet the fourth threshold range; in addition, the near side The maximum value of the distance between any two points in the group of point cloud points should meet the fifth threshold range; in addition, the direction vector formed by the near-side group of point cloud points and the jump candidate point (the fifth point cloud point) The angle of the exit direction satisfies the sixth threshold range.

In the embodiment of the present application, the fourth threshold range may be [b3, b4], that is to say, if a point cloud point is a jumping edge point, the two groups of point cloud points before and after the point cloud point are formed by each The angle of the direction vector should be greater than or equal to b3 and less than or equal to b4. In the embodiment of the present application, the fifth threshold range may be (-∞, a2), that is, if a point cloud point is a jumping edge point, then any of the near-side group point cloud points where the point cloud point is located The maximum value of the distance between the two points should be less than or equal to the threshold a2.

In the embodiment of the present application, the aforementioned third preset condition may further include: one of the two points before and after the current jump candidate point that is farther from the jump candidate point (that is, the far side point described above) is Non-zero point or non-blind zone zero point. In this embodiment, when a point cloud point is determined as a jump candidate point, in addition to determining the above conditions, a zero point is also performed on the far side point of the jump candidate point (zero point is the point with coordinates (0,0,0) ) Determine that if the far side point of the jump candidate point is a non-zero point (that is, not a zero point), the jump candidate point can be determined as a jump candidate point. Conversely, if the far side point of the jumping candidate point is a zero point, it is necessary to further determine whether the zero point is a point in the blind zone of a point cloud detection device (such as a lidar) or a point at infinity. If the zero point is a point in the blind zone of a point cloud detection device (such as a lidar), the jump candidate point is not determined as a jump candidate point; if the zero point is a point at infinity, the jump candidate point is determined as a jump candidate point. This embodiment can avoid erroneously dividing the jumping edge points due to the blind zone of the point cloud detection device (such as lidar).

In the embodiment of the present application, before acquiring feature points from the point cloud points included in the point cloud data, the current frame point cloud data may be traversed to obtain and mark the zero point in the current frame point cloud data for the aforementioned implementation example.

The above shows the process of obtaining the jumping edge point in the data processing method according to the embodiment of the present application. It should be understood that the process is only exemplary, and any other suitable method may be used to obtain the jumping edge point.

In a further embodiment of the present application, in addition to the above-mentioned intersecting edge points and jumping edge points, the obtained edge points may also include edge points of small objects. Among them, the edge point of a small object may correspond to a point on the edge of a small object in a three-dimensional space, such as a point on the edge of a small object such as a thin rod, a tree trunk, and so on. In this embodiment, the obtained edge points are classified in a more detailed manner, which can further improve the accuracy of point cloud data feature extraction.

FIG. 7 shows a schematic flowchart of a process 700 of a small object edge point in a data processing method according to an embodiment of the present application. As shown in FIG. 7, the process 700 may include the following steps:

In step S710, a predetermined number of consecutive point cloud points in the point cloud data of the current frame are determined as candidate edge points of the small object. Wherein, the maximum value of the distance between any two points in the predetermined number of point cloud points meets the seventh threshold range, and the predetermined number is less than the number of the aforementioned group of point cloud points.

In step S720, based on the edge point extraction result of the previous frame point cloud data of the current frame point cloud data, it is determined whether the edge point candidate points of the small object in the current frame point cloud data and the edge points in the previous frame point cloud data together constitute a slender edge.

In step S730, when the small object edge point candidate points in the current frame point cloud data and the edge points in the previous frame point cloud data form a long edge together, the small object edge point candidate points in the current frame point cloud data are determined It is the edge point of a small object.

In the embodiment of the present application, a number of consecutive point cloud points that are less than the aforementioned set of point cloud points may be determined as candidate points for the edge points of the small object. For example, following the previous example, assuming that a group of point cloud points includes 5 point cloud points, such as 4 or 3 or 2 consecutive point cloud points (isolated point cloud clusters) can be determined as edge points of small objects Candidate points. Further, the feature point extraction results (especially the edge point extraction results) of the previous frame point cloud data before the current frame point cloud data can be combined to determine whether the aforementioned small object edge point candidate points are really small object edge points. In the embodiment of the present application, when the edge point candidate points of the small object in the point cloud data of the current frame and the edge points in the point cloud data of the previous frame form a slender edge, the small object in the point cloud data of the current frame The candidate point of the edge point is determined as the edge point of the small object.

The above shows the process of obtaining the edge points of the small object in the data processing method according to the embodiment of the present application. It should be understood that the process is only exemplary, and any other suitable method may be used to obtain the edge points of the small object.

In a further embodiment of the present application, after the edge points are obtained from the point cloud points included in the point cloud data, corner points may be obtained from the point cloud points included in the point cloud data based on the obtained edge points. In the embodiments of the present application, the corner points can also be regarded as a kind of feature points. Obtaining the corner points based on the edge points can further expand the application range of the method according to the embodiments of the present application for irregular point cloud patterns, and at the same time, it is a feature. Subsequent processing after point extraction provides richer information, which is conducive to subsequent processing.

In the embodiment of the present application, acquiring corner points from the point cloud points included in the point cloud data based on the edge points may include performing the following operations for each edge point: analyzing the neighborhood information of the edge points to determine that the same line is formed The neighborhood points of; search for other lines that intersect the same line based on the neighborhood points, and determine the intersection of the same line and the other lines as corner points. In this embodiment, the coordinates of at least two points in the neighborhood of the same line can be recorded; then, the line feature that intersects with the line (for example, perpendicular) is searched, and if there is, the intersection of the two lines is calculated, Mark it as a characteristic corner point. Further, the coordinates of the corner point and the respective direction vectors and sizes of the same line and the other lines can be recorded as a descriptor of the corner point.

In a further embodiment of the present application, before obtaining the aforementioned feature points from the point cloud points included in the point cloud data, the point cloud data of the current frame may be traversed to obtain and mark the noise points in the point cloud data of the current frame. In this way, feature points can be obtained from point cloud points other than noise in the point cloud data, which not only reduces the amount of calculation, but also avoids false extraction results due to noise interference, thereby further improving the accuracy of feature point extraction.

In a further embodiment of the present application, subsequent processing may be performed based on the feature points obtained from the point cloud points included in the point cloud data, such as at least one of mapping, object positioning, and object recognition. Based on the previously acquired feature points with high accuracy, the accuracy and reliability of subsequent processing are also improved.

Based on the above description, the data processing method according to the embodiment of the present application enables the feature extraction of point cloud data to be applicable to various irregular point cloud patterns, and improves the accuracy of point cloud data feature extraction.

The data processing device and lidar provided according to another aspect of the present application will be described below in conjunction with FIG. 8 and FIG. 9.

FIG. 8 shows a schematic block diagram of a point cloud data data processing device 800 according to an embodiment of the present application. As shown in FIG. 8, the data processing device 800 includes a memory 810 and a processor 820. Wherein, the memory 810 stores a program for implementing corresponding steps in the data processing method according to the embodiment of the present application. The processor 820 is configured to run a program stored in the memory 810 to execute corresponding steps of the data processing method according to the embodiment of the present application. Those skilled in the art can understand the operations performed by the processor 820 in combination with the foregoing description. For the sake of brevity, details are not described herein again.

FIG. 9 shows a schematic block diagram of a lidar 900 according to an embodiment of the present application. As shown in FIG. 9, the lidar 900 includes a transmitting end device 910, a receiving end device 920 and a processor 930. Among them, the transmitting end device 910 is used to transmit an optical pulse signal. The receiving end device 920 is configured to receive the echo signal corresponding to the optical pulse signal. The processor 930 is configured to obtain point cloud data based on the echo signal, and execute the data processing method described above according to the embodiment of the present application on the point cloud data. Those skilled in the art can understand the operations performed by the processor 930 in combination with the foregoing description. For the sake of brevity, details are not described herein again.

In addition, according to an embodiment of the present application, a storage medium is also provided, and program instructions are stored on the storage medium, and the program instructions are used to execute the data processing method of the embodiment of the present application when the program instructions are executed by a computer or a processor. The corresponding steps. The storage medium may include, for example, a memory card of a smart phone, a storage component of a tablet computer, a hard disk of a personal computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disk read-only memory (CD-ROM), USB memory, or any combination of the above storage media. The computer-readable storage medium may be any combination of one or more computer-readable storage media.

Based on the above description, when extracting the edge points in the point cloud data according to the data processing method, device, lidar, and storage medium of the embodiments of the present application, the edge points are classified in a more detailed manner, so that the point cloud The feature extraction of data can be applied to various irregular point cloud patterns to improve the accuracy of point cloud data feature extraction.

In some examples, the above point cloud data may be point cloud data obtained by the distance measuring device. Wherein, the distance measuring device may be electronic equipment such as laser radar and laser distance measuring equipment. In one embodiment, the distance measuring device is used to sense external environmental information, for example, distance information, orientation information, reflection intensity information, speed information, etc. of environmental targets. One point cloud point in the point cloud data may include at least one of the external environment information measured by the distance measuring device.

In one implementation manner, the distance measuring device can detect the distance from the probe to the distance measuring device by measuring the time of light propagation between the distance measuring device and the probe, that is, the time-of-flight (TOF). Alternatively, the ranging device can also use other technologies to detect the distance from the detected object to the ranging device, such as a ranging method based on phase shift measurement or a ranging method based on frequency shift measurement. There is no restriction.

For ease of understanding, the distance measuring device that generates the point cloud data mentioned herein will be described as an example in conjunction with the distance measuring device 1000 shown in FIG. 10.

As shown in FIG. 10, the distance measuring device 1000 may include a transmitting circuit 1010, a receiving circuit 1020, a sampling circuit 1030, and an arithmetic circuit 1040.

The transmitting circuit 1010 may emit a light pulse sequence (for example, a laser pulse sequence). The receiving circuit 1020 can receive the light pulse sequence reflected by the object to be detected, and perform photoelectric conversion on the light pulse sequence to obtain an electrical signal. After processing the electrical signal, the electrical signal can be output to the sampling circuit 1030. The sampling circuit 1030 may sample the electrical signal to obtain the sampling result. The arithmetic circuit 1040 may determine the distance between the distance measuring device 1000 and the detected object based on the sampling result of the sampling circuit 1030.

Optionally, the distance measuring device 1000 may further include a control circuit 1050, which can control other circuits, for example, can control the working time of each circuit and/or set parameters for each circuit.

It should be understood that although the distance measuring device shown in FIG. 10 includes a transmitting circuit, a receiving circuit, a sampling circuit, and an arithmetic circuit for emitting a beam for detection, the embodiment of the present application is not limited to this, the transmitting circuit The number of any one of the receiving circuit, the sampling circuit, and the arithmetic circuit can also be at least two, which are used to emit at least two light beams in the same direction or in different directions; wherein, the at least two light paths can be simultaneous Shooting can also be shooting at different times. In an example, the light-emitting chips in the at least two transmitting circuits are packaged in the same module. For example, each emitting circuit includes a laser emitting chip, and the dies in the laser emitting chips in the at least two emitting circuits are packaged together and housed in the same packaging space.

In some implementation manners, in addition to the circuit shown in FIG. 10, the distance measuring device 1000 may further include a scanning module for changing the propagation direction of at least one laser pulse sequence emitted by the transmitting circuit.

Among them, a module including a transmitting circuit 1010, a receiving circuit 1020, a sampling circuit 1030, and a calculation circuit 1040, or a module including a transmitting circuit 1010, a receiving circuit 1020, a sampling circuit 1030, a calculation circuit 1040, and a control circuit 1050 may be referred to as a measurement circuit. Distance module, the distance measurement module can be independent of other modules, for example, the scanning module.

A coaxial optical path can be used in the distance measuring device, that is, the light beam emitted by the distance measuring device and the reflected light beam share at least part of the optical path in the distance measuring device. For example, after at least one laser pulse sequence emitted by the transmitting circuit changes its propagation direction and exits through the scanning module, the laser pulse sequence reflected by the probe passes through the scanning module and then enters the receiving circuit. Alternatively, the distance measuring device may also adopt an off-axis optical path, that is, the light beam emitted by the distance measuring device and the reflected light beam are respectively transmitted along different optical paths in the distance measuring device. FIG. 11 shows a schematic diagram of an embodiment in which the distance measuring device of the present application adopts a coaxial optical path.

The ranging device 1100 includes a ranging module 1101. The ranging module 1101 includes a transmitter 1103 (which may include the above-mentioned transmitting circuit), a collimating element 1104, a detector 1105 (which may include the above-mentioned receiving circuit, sampling circuit, and arithmetic circuit) and Light path changing element 1106. The ranging module 1101 is used to emit a light beam, receive the return light, and convert the return light into an electrical signal. Among them, the transmitter 1103 can be used to transmit a sequence of light pulses. In one embodiment, the transmitter 1103 can emit a sequence of laser pulses. Optionally, the laser beam emitted by the transmitter 1103 is a narrow-bandwidth beam with a wavelength outside the visible light range. The collimating element 1104 is arranged on the exit light path of the emitter 1103, and is used to collimate the light beam emitted from the emitter 1103, and collimate the light beam emitted from the emitter 1103 into parallel light and output to the scanning module. The collimating element 1104 is also used to condense at least a part of the return light reflected by the probe. The collimating element 1104 may be a collimating lens or other elements capable of collimating light beams.

In the embodiment shown in FIG. 11, the light path changing element 1106 is used to combine the transmitting light path and the receiving light path in the distance measuring device before the collimating element 1104, so that the transmitting light path and the receiving light path can share the same collimating element, so that the light path More compact. In some other implementation manners, the transmitter 1103 and the detector 1105 may use their respective collimating elements, and the optical path changing element 1106 is arranged on the optical path behind the collimating element.

In the embodiment shown in FIG. 11, since the beam aperture of the beam emitted by the transmitter 1103 is small, and the beam aperture of the return light received by the distance measuring device is relatively large, the optical path changing element can use a small-area reflector to combine The transmitting light path and the receiving light path are combined. In some other implementation manners, the light path changing element may also use a reflector with a through hole, where the through hole is used to transmit the emitted light of the transmitter 1103, and the reflector is used to reflect the returned light to the detector 1105. In this way, the shielding of the back light from the support of the small reflector in the case of using the small reflector can be reduced.

In the embodiment shown in FIG. 11, the optical path changing element is deviated from the optical axis of the collimating element 1104. In some other implementation manners, the optical path changing element may also be located on the optical axis of the collimating element 1104.

The distance measuring device 1100 further includes a scanning module 1102. The scanning module 1102 is placed on the exit light path of the distance measuring module 1101. The scanning module 1102 is used to change the transmission direction of the collimated beam 1119 emitted by the collimating element 1104 and project it to the external environment, and project the returned light to the collimating element 1104 . The returned light is converged on the detector 1105 via the collimating element 1104.

In an embodiment, the scanning module 1102 may include at least one optical element for changing the propagation path of the light beam, wherein the optical element may change the propagation path of the light beam by reflecting, refraction, diffracting the light beam, and the like. For example, the scanning module 1102 includes a lens, a mirror, a prism, a galvanometer, a grating, a liquid crystal, an optical phased array (Optical Phased Array), or any combination of the foregoing optical elements. In an example, at least part of the optical element is moving, for example, the at least part of the optical element is driven to move by a driving module, and the moving optical element can reflect, refract, or diffract the light beam to different directions at different times. In some embodiments, the multiple optical elements of the scanning module 1102 can rotate or vibrate around a common axis 1109, and each rotating or vibrating optical element is used to continuously change the propagation direction of the incident light beam. In an embodiment, the multiple optical elements of the scanning module 1102 may rotate at different speeds or vibrate at different speeds. In another embodiment, at least part of the optical elements of the scanning module 1102 may rotate at substantially the same rotation speed. In some embodiments, the multiple optical elements of the scanning module may also rotate around different axes. In some embodiments, the multiple optical elements of the scanning module may also rotate in the same direction or in different directions; or vibrate in the same direction, or vibrate in different directions, which is not limited herein.

In one embodiment, the scanning module 1102 includes a first optical element 1114 and a driver 1116 connected to the first optical element 1114. The driver 1116 is used to drive the first optical element 1114 to rotate around the rotation axis 1109 to change the first optical element 1114. The direction of the collimated beam 1119. The first optical element 1114 projects the collimated beam 1119 to different directions. In one embodiment, the angle between the direction of the collimated light beam 1119 changed by the first optical element and the rotation axis 1109 changes as the first optical element 1114 rotates. In one embodiment, the first optical element 1114 includes a pair of opposite non-parallel surfaces through which the collimated light beam 1119 passes. In one embodiment, the first optical element 1114 includes a prism whose thickness varies in at least one radial direction. In an embodiment, the first optical element 1114 includes a wedge angle prism to collimate the beam 1119 for refracting.

In an embodiment, the scanning module 1102 further includes a second optical element 1115, the second optical element 1115 rotates around the rotation axis 1109, and the rotation speed of the second optical element 1115 is different from the rotation speed of the first optical element 1114. The second optical element 1115 is used to change the direction of the light beam projected by the first optical element 1114. In one embodiment, the second optical element 1115 is connected to another driver 1117, and the driver 1117 drives the second optical element 1115 to rotate. The first optical element 1114 and the second optical element 1115 can be driven by the same or different drivers, so that the rotation speed and/or rotation of the first optical element 1114 and the second optical element 1115 are different, so that the collimated light beam 1119 is projected to the outside space Different directions can scan a larger space. In one embodiment, the controller 1118 controls the

drivers

1116 and 1117 to drive the first optical element 1114 and the second optical element 1115, respectively. The rotational speeds of the first optical element 1114 and the second optical element 1115 can be determined according to the expected scanning area and pattern in actual applications. The

drivers

1116 and 1117 may include motors or other drivers.

In one embodiment, the second optical element 1115 includes a pair of opposite non-parallel surfaces through which the light beam passes. In one embodiment, the second optical element 1115 includes a prism whose thickness varies along at least one radial direction. In one embodiment, the second optical element 1115 includes a wedge prism.

In an embodiment, the scanning module 1102 further includes a third optical element (not shown) and a driver for driving the third optical element to move. Optionally, the third optical element includes a pair of opposite non-parallel surfaces, and the light beam passes through the pair of surfaces. In one embodiment, the third optical element includes a prism whose thickness varies in at least one radial direction. In one embodiment, the third optical element includes a wedge prism. At least two of the first, second, and third optical elements rotate at different rotation speeds and/or rotation directions.

The rotation of each optical element in the scanning module 1102 can project light to different directions, such as the direction of the light 1111 and the direction of the light 1113, so that the space around the distance measuring device 1100 is scanned. As shown in FIG. 12, FIG. 12 is a schematic diagram of a scanning pattern of the distance measuring device 1100. It is understandable that when the speed of the optical element in the scanning module changes, the scanning pattern will also change accordingly.

When the light 1111 projected by the scanning module 1102 hits the detection object 1110, a part of the light is reflected by the detection object 1110 to the distance measuring device 1100 in a direction opposite to the projected light 1111. The return light 1112 reflected by the detection object 1110 is incident on the collimating element 1104 after passing through the scanning module 1102.

The detector 1105 and the transmitter 1103 are placed on the same side of the collimating element 1104, and the detector 1105 is used to convert at least part of the return light passing through the collimating element 1104 into electrical signals.

In one embodiment, an anti-reflection coating is plated on each optical element. Optionally, the thickness of the antireflection coating is equal to or close to the wavelength of the light beam emitted by the transmitter 1103, which can increase the intensity of the transmitted light beam.

In an embodiment, a filter layer is plated on the surface of an element located on the beam propagation path in the distance measuring device, or a filter is provided on the beam propagation path for transmitting at least the wavelength band of the beam emitted by the transmitter 1103 , Reflect other wavebands to reduce the noise caused by ambient light to the receiver.

In some embodiments, the transmitter 1103 may include a laser diode through which nanosecond laser pulses are emitted. Further, the laser pulse receiving time can be determined, for example, the laser pulse receiving time can be determined by detecting the rising edge time and/or the falling edge time of the electrical signal pulse. In this way, the distance measuring device 1100 can calculate the TOF 1107 by using the pulse receiving time information and the pulse sending time information, so as to determine the distance between the probe 1110 and the distance measuring device 1100.

The distance and orientation detected by the distance measuring device 1100 can be used for remote sensing, obstacle avoidance, surveying and mapping, modeling, navigation, and so on. In one embodiment, the distance measuring device of the embodiment of the present application can be applied to a mobile platform, and the distance measuring device can be installed on the platform body of the mobile platform. A mobile platform with a distance measuring device can measure the external environment, for example, measuring the distance between the mobile platform and obstacles for obstacle avoidance and other purposes, and for two-dimensional or three-dimensional surveying and mapping of the external environment. In some embodiments, the mobile platform includes at least one of an unmanned aerial vehicle, a car, a remote control car, a robot, and a camera. When the ranging device is applied to an unmanned aerial vehicle, the platform body is the fuselage of the unmanned aerial vehicle. When the distance measuring device is applied to a car, the platform body is the body of the car. The car can be a self-driving car or a semi-self-driving car, and there is no restriction here. When the distance measuring device is applied to a remote control car, the platform body is the body of the remote control car. When the distance measuring device is applied to a robot, the platform body is a robot. When the distance measuring device is applied to a camera, the platform body is the camera itself.

Although the exemplary embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above-described exemplary embodiments are merely exemplary, and are not intended to limit the scope of the present application thereto. Those of ordinary skill in the art can make various changes and modifications therein without departing from the scope and spirit of the present application. All these changes and modifications are intended to be included in the scope of the present application as required by the appended claims.

A person of ordinary skill in the art may be aware that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software depends on the specific application and design constraint conditions of the technical solution. Professionals and technicians can use different methods for specific applications to implement the described functions, but such implementation should not be considered beyond the scope of this application.

In the several embodiments provided in this application, it should be understood that the disclosed device and method can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other divisions in actual implementation, for example, multiple units or components can be combined or It can be integrated into another device, or some features can be ignored or not implemented.

In the instructions provided here, a lot of specific details are explained. However, it can be understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures, and technologies are not shown in detail, so as not to obscure the understanding of this specification.

Similarly, it should be understood that, in order to simplify this application and help understand one or more of the various aspects of the invention, in the description of the exemplary embodiments of this application, the various features of this application are sometimes grouped together into a single embodiment or figure. , Or in its description. However, the method of this application should not be interpreted as reflecting the intention that the claimed application requires more features than those clearly stated in the claims. To be more precise, as reflected in the corresponding claims, the point of the invention is that the corresponding technical problems can be solved with features that are less than all the features of a single disclosed embodiment. Therefore, the claims following the specific embodiment are thus explicitly incorporated into the specific embodiment, wherein the claims themselves are all regarded as separate embodiments of the present application.

Those skilled in the art can understand that, in addition to mutual exclusion between the features, any combination of all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and any method or device disclosed in this manner can be used. Processes or units are combined. Unless expressly stated otherwise, the features disclosed in this specification (including the accompanying claims, abstract and drawings) may be replaced by alternative features that provide the same, equivalent or similar purpose.

In addition, those skilled in the art can understand that although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments means that they are within the scope of the present application. Within and form different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

The various component embodiments of the present application may be implemented by hardware, or by software modules running on one or more processors, or by a combination of them. Those skilled in the art should understand that a microprocessor or a digital signal processor (DSP) may be used in practice to implement some or all of the functions of some modules according to the embodiments of the present application. This application can also be implemented as a device program (for example, a computer program and a computer program product) for executing part or all of the methods described herein. Such a program for implementing the present application may be stored on a computer-readable storage medium, or may have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

It should be noted that the above-mentioned embodiments illustrate rather than limit the application, and those skilled in the art can design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be constructed as a limitation to the claims. The application can be realized by means of hardware including several different elements and by means of a suitably programmed computer. In the unit claims enumerating several devices, several of these devices may be embodied in the same hardware item. The use of the words first, second, and third, etc. do not indicate any order. These words can be interpreted as names.

The above are only specific implementations of this application or descriptions of specific implementations. The scope of protection of this application is not limited to this. Anyone skilled in the art can easily fall within the technical scope disclosed in this application. Any change or replacement should be covered in the scope of protection of this application. The protection scope of this application shall be subject to the protection scope of the claims.

Claims

A data processing method, characterized in that the method includes:

Obtain the point cloud data of the current frame;

Obtain feature points from the point cloud points included in the point cloud data, the feature points include plane points and edge points, the edge points include face-to-face intersection edge points and/or jump edge points, and the face-to-face intersection edges The point corresponds to a point on the boundary line of the intersecting faces in the three-dimensional space, and the jumping edge point corresponds to the point on the edge of the isolated face in the three-dimensional space.
The method according to claim 1, wherein obtaining a plane point from the point cloud points included in the point cloud data comprises:

Acquire a group of point cloud points from the point cloud data in a time sequence, and determine whether the acquired group of point cloud points meets a first preset condition;

When the acquired set of point cloud points meets the first preset condition, the set of point cloud points are determined as candidate points for plane points, and the next set of point cloud points are acquired for the judgment. The next group of point cloud points includes at least one point cloud point in the previous group of point cloud points;

Acquiring a final plane point extraction result of the current frame point cloud data based on the determined plane point candidate points;

The first preset condition includes: the spatial distribution of the set of point cloud points is approximately a straight line, and the set of point cloud points is approximately center-symmetric when the center point is the center.
The method according to claim 2, wherein acquiring the final plane point extraction result of the current frame point cloud data based on the determined plane point candidate points comprises:

Use the determined candidate points of the plane point directly as the final plane point extraction result of the point cloud data of the current frame; or

Sort the determined candidate points of the plane point according to the degree of satisfaction of the first preset condition, and select a part of the candidate points of the plane point as the final plane point extraction result of the point cloud data of the current frame based on the sorting result; or

The point cloud data of the current frame is divided into several regions, the candidate points of the plane points in each region are sorted according to the degree of satisfaction of the first preset condition, and the part selected from each region based on the sorting result The plane point candidate points are used as the final plane point extraction result of the point cloud data of the current frame.
The method according to claim 2 or 3, wherein when the acquired set of point cloud points does not meet the first preset condition, one point cloud point is backed forward according to the time sequence and from the point cloud point back to The point cloud point starts to acquire a group of point cloud points to execute the judgment.
The method according to any one of claims 2-4, wherein the acquiring a group of point cloud points in a time sequence from the point cloud data is performed based on a sliding window.
The method according to claim 5, wherein the method further comprises:

Before acquiring a set of point cloud points from the point cloud data in time series, sort the current frame point cloud data according to depth values, select the median value as the scene scale threshold, and determine all points based on the scene scale threshold. State the size of the sliding window.
The method according to any one of claims 2-6, characterized in that, obtaining a surface intersection edge point from the point cloud points included in the point cloud data comprises: referring to the points in the point cloud data Cloud Point performs the following operations:

Judging whether the two groups of point cloud points before and after the current point cloud point meets the first preset condition;

When the front and back two sets of point cloud points where the current point cloud point is located meet the first preset condition, determining whether the front and back sets of point cloud points meet the second preset condition;

When the two sets of point cloud points meet the second preset condition, determining the current point cloud point as a surface intersection edge point;

The second preset condition includes: the maximum value of the distance between any two points in each group of point cloud points in the front and back two groups of point cloud points meets the first threshold range, and the two groups of point cloud points each The included angle of the formed direction vector satisfies the second threshold range, and the included angle between the respective direction vectors formed by the two groups of point cloud points and the exit direction of the current point cloud point satisfies the third threshold range.
The method according to any one of claims 2-7, wherein obtaining a jumping edge point from the point cloud points included in the point cloud data comprises: referring to the point cloud points in the point cloud data Do the following:

Judging whether the difference between the distance between the current point cloud point and the two points before and after it is greater than a predetermined threshold, wherein a point closer to the current point cloud point among the two points before and after is defined as a near side point;

When the difference between the distance between the current point cloud point and the two points before and after it is greater than the predetermined threshold, determining the current point cloud point as a jumping candidate point;

Define the point cloud point in the group where the near side point is located among the two groups of point cloud points before and after the jump candidate point is located, and determine whether the near side group point cloud point meets the third preset condition;

When the near-side group of point cloud points meet the third preset condition, the jumping candidate point is determined as a jumping edge point.
The method according to claim 8, wherein the third preset condition comprises: the near-side group of point cloud points meets the first preset condition, and the direction formed by the near-side group of point cloud points The angle between the vector and the direction vector formed by a group of point cloud points on the other side of the jump candidate point satisfies the fourth threshold range, and the maximum value of the distance between any two points in the near-side group of point cloud points The fifth threshold range is met, and the angle between the direction vector formed by the near-side group of point cloud points and the exit direction of the jump candidate point meets the sixth threshold range.
The method according to claim 9, wherein the third preset condition further comprises: a point farther from the jump candidate point among the two points before and after is a non-zero point or a non-blind zone zero point.
The method according to claim 10, wherein the method further comprises:

Before acquiring feature points from the point cloud points included in the point cloud data, the current frame point cloud data is traversed to acquire and mark the zero point in the current frame point cloud data.
The method according to any one of claims 2-11, wherein the edge point further comprises a small object edge point, and the small object edge point corresponds to a point on the edge of the small object in the three-dimensional space.
The method according to claim 12, wherein obtaining the edge points of the small object from the point cloud points included in the point cloud data comprises:

A predetermined number of consecutive point cloud points among the point cloud points are determined as candidate edge points of small objects, and the maximum value of the distance between any two points in the predetermined number of point cloud points meets the seventh threshold range, so The predetermined number is less than the number of the set of point cloud points;

Based on the edge point extraction result of the previous frame point cloud data of the current frame point cloud data, it is determined whether the edge point candidate points of the small object in the current frame point cloud data are formed together with the edge points in the previous frame point cloud data Slender edge

When the small object edge point candidate points in the current frame point cloud data and the edge points in the previous frame point cloud data form a slender edge, the small object edge point candidates in the current frame point cloud data The point is determined as the edge point of the small object.
The method according to any one of claims 1-13, wherein the method further comprises:

Before acquiring feature points from the point cloud points included in the point cloud data, traverse the current frame point cloud data to acquire and mark the noise points in the current frame point cloud data; and

The acquiring feature points from the point cloud points included in the point cloud data includes: acquiring feature points from the point cloud points of the point cloud data except for the noise points.
The method according to any one of claims 1-14, wherein the method further comprises:

After the edge points are obtained from the point cloud points included in the point cloud data, corner points are obtained from the point cloud points included in the point cloud data based on the edge points.
The method according to claim 15, wherein the step of acquiring corner points from the point cloud points included in the point cloud data based on the edge points comprises:

Analyze the neighborhood information of the edge points to determine the neighborhood points that constitute the same line;

Searching for other lines that intersect the same line based on the neighborhood points, and determining the intersection of the same line and the other lines as corner points.
The method according to claim 16, wherein the method further comprises:

The coordinates of the corner point and the respective direction vectors and sizes of the same line and the other lines are recorded as a descriptor of the corner point.
The method according to any one of claims 1-17, wherein the method further comprises:

At least one of mapping, object positioning, and object recognition is performed based on the feature points acquired from the point cloud points included in the point cloud data.
A data processing device, characterized in that the system includes a memory and a processor, the memory stores a computer program run by the processor, and the computer program executes the claims when run by the processor The data processing method described in any one of 1-18.
A lidar, characterized in that the lidar includes:

Transmitter equipment, used to transmit optical pulse signals;

The receiving end device is used to receive the echo signal corresponding to the optical pulse signal; and

The processor is configured to obtain point cloud data based on the echo signal, and execute the data processing method according to any one of claims 1-18 on the point cloud data.
A storage medium, characterized in that a computer program is stored on the storage medium, and the computer program executes the data processing method according to any one of claims 1-18 when running.