CN112166457A - Point cloud segmentation method and system and movable platform - Google Patents
Point cloud segmentation method and system and movable platform Download PDFInfo
- Publication number
- CN112166457A CN112166457A CN201980034128.0A CN201980034128A CN112166457A CN 112166457 A CN112166457 A CN 112166457A CN 201980034128 A CN201980034128 A CN 201980034128A CN 112166457 A CN112166457 A CN 112166457A
- Authority
- CN
- China
- Prior art keywords
- point cloud
- cloud data
- dimensional
- index value
- preset
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 65
- 230000011218 segmentation Effects 0.000 title claims abstract description 62
- 230000001174 ascending effect Effects 0.000 claims description 6
- 238000004590 computer program Methods 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 8
- 238000004364 calculation method Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000001514 detection method Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 239000011435 rock Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/12—Edge-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
- G06T2207/10012—Stereo images
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
- G06T2207/10044—Radar image
Landscapes
- Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Optical Radar Systems And Details Thereof (AREA)
- Traffic Control Systems (AREA)
Abstract
The application provides a point cloud segmentation method, a point cloud segmentation system and a movable platform, wherein the point cloud segmentation method comprises the following steps: acquiring point cloud data to be processed in a target area (S201); acquiring a one-dimensional index value of each point cloud data according to the boundary information and the grid resolution of the three-dimensional map of the target area (S202); acquiring a density value of each point cloud data according to the one-dimensional index value of each point cloud data (S203); and segmenting the point cloud data to be processed according to the density value of each point cloud data to obtain a plurality of point cloud clusters (S204). The point cloud data are segmented based on the density of the point cloud data, and accuracy of point cloud segmentation is improved.
Description
Technical Field
The application relates to the technical field of movable platforms, in particular to a point cloud segmentation method, a point cloud segmentation system and a movable platform.
Background
The laser radar is a scanning type sensor, and the working principle is as follows: the target is detected by emitting laser beams, point cloud data is formed by collecting the reflected beams, and the point cloud data can be generated into an accurate three-dimensional image after being subjected to photoelectric processing. Through laser radar, can be accurate acquire high accuracy physical space environmental information, the range finding precision can reach centimetre level. Therefore, the laser radar becomes the most central sensor device in the fields of automobile automatic driving, unmanned driving, positioning navigation, space mapping, security and protection and the like.
Among them, the segmentation of point cloud data is one of the important problems of data processing. The main role of point cloud segmentation is to segment point cloud data into multiple independent entities, e.g., people, cars, bicycles, facades, columns, etc. At present, a method based on distance threshold judgment is generally used for clustering and segmenting point cloud data. However, the density difference of the real-time point clouds generated by the laser radar is large and changes with the distance, the point cloud density is inconsistent, so that under-segmentation or over-segmentation is caused, and the accuracy of point cloud segmentation is low.
Disclosure of Invention
The application provides a point cloud segmentation method, a point cloud segmentation system and a movable platform, and improves the accuracy of point cloud segmentation.
In a first aspect, the present application provides a point cloud segmentation method, including:
acquiring point cloud data to be processed in a target area;
acquiring a one-dimensional index value of each point cloud data according to the boundary information and the grid resolution of the three-dimensional map of the target area;
acquiring a density value of each point cloud data according to the one-dimensional index value of each point cloud data, wherein the density value of the point cloud data is used for indicating the point cloud density in a preset three-dimensional space, and the preset three-dimensional space comprises the point cloud data;
and segmenting the point cloud data to be processed according to the density value of each point cloud data to obtain a plurality of point cloud clusters.
In a second aspect, the present application provides a point cloud segmentation system, comprising: a memory, a processor, and a point cloud sensor;
the point cloud sensor is used for acquiring point cloud data to be processed in a target area;
the memory for storing program code;
the processor, invoking the program code, when executed, is configured to:
acquiring a one-dimensional index value of each point cloud data according to the boundary information and the grid resolution of the three-dimensional map of the target area;
acquiring a density value of each point cloud data according to the one-dimensional index value of each point cloud data, wherein the density value of the point cloud data is used for indicating the point cloud density in a preset three-dimensional space, and the preset three-dimensional space comprises the point cloud data;
and segmenting the point cloud data to be processed according to the density value of each point cloud data to obtain a plurality of point cloud clusters.
In a third aspect, the present application provides a movable platform comprising: the point cloud segmentation system provided by any embodiment of the second aspect of the application.
In a fourth aspect, the present application provides a computer storage medium having a computer program stored therein, which when executed, implements the method as provided in the first aspect.
The application provides a point cloud segmentation method, a point cloud segmentation system and a movable platform. Because the point cloud data in the target area are segmented according to the density value of each point cloud data to obtain a plurality of point cloud clusters, the accuracy of point cloud segmentation is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.
FIG. 1 is a schematic diagram of an application scenario to which the present application relates;
fig. 2 is a flowchart of a point cloud segmentation method according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a three-dimensional map of a target area and a grid resolution provided by an embodiment of the present application;
fig. 4 is a flowchart of another point cloud segmentation method provided in the embodiment of the present application;
fig. 5A-5B are schematic diagrams illustrating the effect of point cloud data segmentation provided by the embodiment of the present application;
fig. 6 is a schematic structural diagram of a point cloud segmentation system according to an embodiment of the present disclosure.
Detailed Description
The method and the device can be applied to scenes for processing point cloud data acquired by the laser radar. For example, the method can be applied to the intelligent driving fields such as automatic driving, auxiliary driving and safe driving, and can detect obstacles such as vehicles and pedestrians in a road scene by processing point cloud data. For another example, the method can be applied to the field of unmanned aerial vehicles, and can detect obstacles in the flight scene of the unmanned aerial vehicle. For another example, the method can be applied to the field of security protection and used for detecting objects entering a specified area.
Fig. 1 is a schematic diagram of an application scenario related to the present application. As shown in fig. 1, the smart driving vehicle may include a lidar (not shown). The number and type of lidar materials is not limited in this application. For example, the lidar may be a rotary scanning multiline lidar having a multiple-transmit-multiple-receive sensor, or the like. During the driving process of the intelligent driving vehicle, the laser radar can acquire point cloud data of objects (such as falling rocks, lost objects, withered branches, pedestrians, vehicles and the like) in a lane ahead. By segmenting the point cloud data, subsequently, the object can be identified and detected according to the point cloud data, detection information such as the three-dimensional position, the posture orientation and the three-dimensional size of the object is obtained, and the intelligent driving state such as lane changing, deceleration or parking is planned according to the detection information.
It should be noted that fig. 1 is a schematic view of an application scenario of the present application, and the application scenario of the present application includes, but is not limited to, that shown in fig. 1.
The technical solution of the present application will be described in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
It should be noted that, for the convenience of clearly describing the technical solutions of the present application, in the embodiments of the present application, words such as "first", "second", and the like are used to distinguish the same items or similar items having substantially the same functions and actions. Those skilled in the art will appreciate that the terms "first," "second," etc. do not denote any order or quantity, nor do the terms "first," "second," etc. denote any order or importance.
Fig. 2 is a flowchart of a point cloud segmentation method according to an embodiment of the present disclosure. In the point cloud segmentation method provided by this embodiment, the execution subject may be a point cloud segmentation system. Alternatively, the point cloud segmentation system may be a separate device, such as a point cloud sensor with data processing capabilities. Alternatively, the point cloud segmentation system may be a distributed system, such as a memory, a processor, and a point cloud sensor, which are located at different locations of the vehicle. As shown in fig. 2, the point cloud segmentation method provided in this embodiment may include:
s201, point cloud data to be processed in the target area are obtained.
Wherein, the application scenes are different, and the target areas can be different. For example, in the field of vehicle smart driving, the target area may be a road area in front of the vehicle detected by a point cloud sensor. In the field of unmanned aerial vehicles, the target area can be an area detected by a point cloud sensor in the flight scene of the unmanned aerial vehicle. The point cloud sensor may be a laser radar, a Time of Flight (TOF) sensor, or the like, and is mounted on or integrated with the movable platform.
S202, acquiring a one-dimensional index value of each point cloud data according to the boundary information and the grid resolution of the three-dimensional map of the target area.
Specifically, in the present embodiment, the range of the target area is greater than or equal to the range of the three-dimensional map of the target area. For example. In the smart driving scenario, the vehicle is prepared to pass through a bridge in front of it. The target area may then be an area on the vehicle that the lidar can detect, and the three-dimensional map of the target area may be a three-dimensional map of the bridge. Wherein the grid resolution is used to divide the three-dimensional map of the target area into grids. The grid is a basic unit in a three-dimensional coordinate system. According to the boundary information and the grid resolution of the three-dimensional map of the target area, the three-dimensional map of the target area can be divided into a plurality of grids, so that a one-dimensional index value of each point cloud data is obtained. The one-dimensional index value of each point cloud data is related to the grid where the point cloud data is located, and the one-dimensional index value of each point cloud data also reflects the relative position of the point cloud data in the target area or in the three-dimensional map of the target area. Alternatively, the one-dimensional index values of the point cloud data included in the same grid may be the same.
The boundary information of the three-dimensional map of the target area is used to define the three-dimensional position of the three-dimensional map, and the implementation manner of the boundary information is not limited in this embodiment. For example, the boundary information of the three-dimensional map of the target area may include coordinate values of boundary points of the three-dimensional map, which may uniquely determine the three-dimensional map. For another example, the boundary information of the three-dimensional map of the target area may include a minimum value and a maximum value of the three-dimensional map of the target area on three coordinate axes in the three-dimensional coordinate system. For example, the minimum value and the maximum value on the X coordinate axis are sequentially represented as XminAnd XmaxThe minimum and maximum values on the Y coordinate axis are sequentially represented as YminAnd YmaxThe minimum and maximum values on the Z coordinate axis are sequentially represented as ZminAnd Zmax. By Xmin、Xmax、Ymin、Ymax、ZminAnd ZmaxThe three-dimensional map can be uniquely determined。
Optionally, the grid resolution may include a unit resolution of three coordinate axes in the three-dimensional coordinate system, and a specific value of the unit resolution is not limited in this embodiment. This is illustrated by way of example. Fig. 3 is a schematic diagram of a three-dimensional map of a target area and a grid resolution provided in an embodiment of the present application. As shown in FIG. 3, the grid resolution may include a unit resolution X of the X-axis in a three-dimensional coordinate systemresY-axis unit resolution YresAnd unit resolution Z of Z axisres. Illustratively, the three-dimensional map of the target area may be divided into 8 grids according to the boundary information and the grid resolution of the three-dimensional map of the target area. In this embodiment, the identification information of each grid is not limited. For example, grids 1 to 8 may be respectively marked, and grids a to H may be respectively marked.
S203, obtaining the density value of each point cloud data according to the one-dimensional index value of each point cloud data.
The density value of the point cloud data is used for indicating the point cloud density in a preset three-dimensional space, and the preset three-dimensional space comprises the point cloud data.
Taking fig. 3 as an example, the density value of the point cloud data is exemplified.
In fig. 3, for the point cloud data a, the density value of the point cloud data a is used to indicate the point cloud density within the preset three-dimensional space. For example, the preset three-dimensional space may include a grid 3, a grid 1, a grid 4, and a grid 7.
In fig. 3, for the point cloud data B, the density value of the point cloud data B is used to indicate the point cloud density within the preset three-dimensional space. For example, the preset three-dimensional space may include grid 8, grid 6, grid 7, and grid 4.
It should be noted that, the preset three-dimensional space is not limited in this embodiment.
And S204, segmenting the point cloud data to be processed according to the density value of each point cloud data to obtain a plurality of point cloud clusters.
Generally, the density of the point cloud data acquired by the laser radar is not uniform, and the density of the point cloud data changes with the change of the distance. The closer to the lidar, the greater the density of the point cloud data is typically. The further away from the lidar, the less dense the point cloud data is typically. If the point cloud data is segmented by the distance threshold judgment-based method, the accuracy of point cloud segmentation is low. In the present embodiment, a one-dimensional index value of each point cloud data is obtained according to the boundary information and the grid resolution of the three-dimensional map of the target area. The one-dimensional index value can reflect the relative position relationship of the point cloud data in the target area. And obtaining the density value of each point cloud data according to the one-dimensional index value of each point cloud data, and thus segmenting the point cloud data in the target area according to the density value of each point cloud data to obtain a plurality of point cloud clusters. Due to the fact that the density of the point cloud data is considered, accuracy of point cloud segmentation is improved.
Optionally, in this embodiment, the point cloud data to be processed in the target area may be point cloud data other than ground point cloud data in the point cloud data acquired by the laser radar.
Specifically, in an application scenario of the smart vehicle, the point cloud data acquired by the laser radar includes point cloud data corresponding to a ground portion, and the point cloud data of the ground portion is useless for detection and identification of subsequent objects. Therefore, by eliminating the ground point cloud data, the number of the point cloud data is reduced, the calculation amount is reduced, and the accuracy of point cloud data segmentation is improved.
Optionally, before obtaining the density value of each point cloud data according to the one-dimensional index value of each point cloud data in S203, the point cloud segmentation method provided in this embodiment may further include:
and deleting invalid point cloud data in the point cloud data to be processed. And the invalid point cloud data is point cloud data which is not in the three-dimensional map.
Specifically, in this embodiment, the point cloud data to be processed in the target area is not necessarily valid point cloud data, and only the point cloud data located in the three-dimensional map of the target area is valid point cloud data. By eliminating invalid point cloud data, the number of the point cloud data is reduced, the calculation amount is reduced, and the accuracy of effective point cloud data segmentation is improved.
Optionally, the point cloud segmentation method provided in this embodiment may further include:
and acquiring the number of point cloud data in each point cloud cluster.
And if the number of the point cloud data in the point cloud cluster is less than a preset value, deleting the point cloud cluster.
Specifically, if the number of point cloud data included in a point cloud cluster is small, the probability that the point cloud cluster is an invalid point cloud cluster is high. By deleting the point cloud clusters with less point cloud data, the accuracy and the effectiveness of the obtained point cloud clusters are further improved.
Optionally, in S203, obtaining the density value of each point cloud data according to the one-dimensional index value of each point cloud data may include:
and if the adjacent point cloud data are searched in the preset three-dimensional space according to the one-dimensional index value of each point cloud data, acquiring the number of the adjacent point cloud data. The preset three-dimensional space is a first three-dimensional space.
And acquiring the density value of the point cloud data according to the number of the adjacent point cloud data and the first three-dimensional space.
Specifically, the one-dimensional index value of the point cloud data may reflect a relative position relationship of the point cloud data in the target area. For each point cloud data, searching other point cloud data adjacent to the point cloud data in a preset three-dimensional space corresponding to the point cloud data according to the one-dimensional index values of all the point cloud data in the target area, and acquiring the number of the other adjacent point cloud data. For convenience of description, the preset three-dimensional space may be referred to as a first three-dimensional space. And then, acquiring the density value of the point cloud data according to the number of the adjacent point cloud data and the first three-dimensional space.
This is illustrated in connection with fig. 3.
It is assumed that the number of point cloud data to be processed in the target area is 1000. Each point cloud data corresponds to a one-dimensional index value. The first three-dimensional space corresponding to the point cloud data a may be referred to as a space H. And aiming at the point cloud data A, searching the number of other point cloud data adjacent to the point cloud data A in the space H according to the one-dimensional index values of 1000 point cloud data in the target area, wherein the number is assumed to be 200. Then, the density value of the point cloud data a may be obtained according to the number 200 of adjacent point cloud data and the space H.
In this embodiment, the shape and size of the first three-dimensional space are not limited.
Optionally, the first three-dimensional space may be a spherical space with the point cloud data as a center of a circle and the first preset distance as a radius. Correspondingly, the density value of the point cloud data is obtained according to the number of the adjacent points and the first three-dimensional space, and the density value comprises the following steps:
and acquiring the density value of the point cloud data according to the number of the adjacent point cloud data and the volume of the spherical space.
In this embodiment, the specific value of the first preset distance is not limited.
Optionally, the point cloud segmentation method provided in this embodiment may further include:
and performing ascending arrangement or descending arrangement on the one-dimensional index value of each point cloud data to obtain an index value sequence.
Correspondingly, searching adjacent point cloud data in a preset three-dimensional space according to the one-dimensional index value of each point cloud data may include:
and determining grids included in the preset three-dimensional space according to the index value sequence and the preset three-dimensional space.
And searching adjacent point cloud data in a grid included in a preset three-dimensional space.
Specifically, when the density value of the point cloud data is obtained, the preset three-dimensional space is the first preset three-dimensional space. The one-dimensional index values of the point cloud data can reflect the relative position relationship of the point cloud data in the target area, and the obtained index value sequence can further reflect the proximity relationship between the point cloud data in the target area by performing ascending or descending arrangement on the one-dimensional index values of the point cloud data. And determining grids included in the first preset three-dimensional space according to the index value sequence and the first preset three-dimensional space. Thereby, adjacent point cloud data is searched in a grid included in the first preset three-dimensional space.
The grids included in the preset three-dimensional space are determined through the sorted index value sequence, and then other point cloud data adjacent to certain point cloud data are determined according to the point cloud data included in the grids, so that all point cloud data in a target area are prevented from being processed, the calculation amount for searching the adjacent point cloud data is greatly reduced, and the processing efficiency and accuracy are improved.
The embodiment provides a point cloud segmentation method, which comprises the following steps: the method comprises the steps of obtaining point cloud data to be processed in a target area, obtaining a one-dimensional index value of each point cloud data according to boundary information and grid resolution of a three-dimensional map of the target area, obtaining a density value of each point cloud data according to the one-dimensional index value of each point cloud data, and dividing the point cloud data to be processed according to the density value of each point cloud data to obtain a plurality of point cloud clusters. According to the point cloud segmentation method provided by the embodiment, the point cloud data is segmented based on the density of the point cloud data, and the accuracy of point cloud segmentation is improved.
Optionally, on the basis of the point cloud segmentation method provided in the foregoing method embodiment, in another embodiment, an implementation manner of obtaining a one-dimensional index value of the point cloud data is provided. In this embodiment, in S202, acquiring a one-dimensional index value of each point cloud data according to the boundary information and the grid resolution of the three-dimensional map of the target area may include:
and if the point cloud data is in the three-dimensional map, acquiring a one-dimensional index value of each point cloud data according to the position information of the point cloud data, the boundary information of the three-dimensional map and the grid resolution.
And if the point cloud data is not in the three-dimensional map, determining that the one-dimensional index value of the point cloud data is a preset invalid value.
Specifically, for each point cloud data in the target area, it is determined whether the point cloud data is located in the three-dimensional map of the target area. If the point cloud data is located in the three-dimensional map, the point cloud data can be determined to be effective point cloud data, and a one-dimensional index value of each point cloud data is further obtained according to the position information of the point cloud data, the boundary information of the three-dimensional map and the grid resolution. And if the point cloud data is not located in the three-dimensional map, determining that the point cloud data is invalid point cloud data, and directly determining that the one-dimensional index value of the invalid point cloud data is a preset invalid value.
Whether the point cloud data is effective or not is determined firstly, and then the one-dimensional index value of the point cloud data is obtained, so that the calculation amount is reduced, the processing speed and efficiency are improved, and the accuracy and the effectiveness of the one-dimensional index value of the point cloud data are improved.
In this embodiment, a specific value of the preset invalid value is not limited. For example, it may be any negative integer, such as-1.
Optionally, obtaining a one-dimensional index value of each point cloud data according to the position information of the point cloud data, the boundary information of the three-dimensional map, and the grid resolution may include:
and acquiring a three-dimensional index value of the point cloud data according to the position information of the point cloud data, the boundary information of the three-dimensional map and the grid resolution. The three-dimensional index value comprises index values of grids where the point cloud data are located on three-dimensional coordinate axes corresponding to the three-dimensional map respectively.
And acquiring a one-dimensional index value of the point cloud data according to the three-dimensional index value and the number of grids respectively included by the three-dimensional map on the three-dimensional coordinate axis.
The following description is given by way of example.
It is assumed that the position information of the point cloud data in the three-dimensional map of the target area is represented as (Px, Py, Pz). The grid resolution may include a unit resolution X of an X-axis in a three-dimensional coordinate systemresY-axis unit resolution YresAnd unit resolution Z of Z axisres. The number of the three-dimensional map on the X axis, the Y axis and the Z axis in the three-dimensional coordinate system is respectively expressed as (X)num，Ynum，Znum). The three-dimensional index value of the point cloud data is represented by (Px)i，Pyi，Pzi). The minimum value and the maximum value of the three-dimensional map of the target area on the X axis, the Y axis and the Z axis in the three-dimensional coordinate system are sequentially expressed as Xmin、Xmax、Ymin、Ymax、ZminAnd Zmax。
Optionally, in an implementation manner, the three-dimensional index value of the point cloud data may be obtained through the following formula:
it should be noted that the three-dimensional index value of the point cloud data may also be obtained in other manners, which is not limited in this embodiment. For example, each grid in the three-dimensional map has a preset index value. According to the position information of the point cloud data, a grid where the point cloud data is located can be determined, and further, a preset index value of the grid can be determined as a three-dimensional index value of the point cloud data.
Optionally, in an implementation, the one-dimensional index value Hash of the point cloud datapCan be determined by the following formula:
Hashp＝Pxi*Ynum*Znum+Pyi*Znum+Pzi
optionally, in another implementation manner, the one-dimensional index value Hash of the point cloud datapCan be determined by the following formula:
Hashp＝Pyi*Xnum*Znum+Pxi*Znum+Pzi
it should be noted that the one-dimensional index value of the point cloud data may also be obtained by other methods, which is not limited in this embodiment.
Optionally, on the basis of the point cloud segmentation method provided in the above method embodiment, in yet another embodiment, an implementation manner of obtaining a point cloud cluster is provided. Fig. 4 is a flowchart of another point cloud segmentation method according to an embodiment of the present disclosure. As shown in fig. 4, in S204, segmenting the point cloud data to be processed according to the density value of each point cloud data to obtain a plurality of point cloud clusters, which may include:
s401, traversing point cloud data to be processed, and acquiring a proximity relation tree.
S402, obtaining a plurality of point cloud clusters according to the adjacent relation tree.
In S401, traversing the point cloud data to be processed may include:
and if the adjacent point cloud data is searched in the preset three-dimensional space according to the one-dimensional index value of each point cloud data, generating a connection relation between the point cloud data and the adjacent point cloud data. And the preset three-dimensional space is a second three-dimensional space.
And if the adjacent point cloud data is searched in the preset three-dimensional space according to the one-dimensional index value of each point cloud data, and the density value of the adjacent point cloud data is greater than that of the point cloud data, generating a connection relation between the point cloud data and the adjacent point cloud data. The preset three-dimensional space is a third three-dimensional space, and the third three-dimensional space is larger than the second three-dimensional space.
In the present embodiment, for convenience of explanation, the second three-dimensional space and the third three-dimensional space are referred to. In this embodiment, the shapes and sizes of the second three-dimensional space and the third three-dimensional space are not limited. Optionally, the second three-dimensional space may be a spherical space with the point cloud data as a center of a circle and the second preset distance as a radius. The third three-dimensional space may be a spherical space with the point cloud data as a center of a circle and a third preset distance as a radius. In this embodiment, specific values of the second preset distance and the third preset distance are not limited. Optionally, the second preset distance may have the same value as the first preset distance.
The following description is given by way of example.
It is assumed that the second preset distance is denoted as r2 and the third preset distance is denoted as r 3.
In one processing process, according to one-dimensional index values of all point cloud data in the target area, searching adjacent point cloud data in a spherical space with the point cloud data P as a sphere center and r2 as a radius to generate a connection relation between the point cloud data P and the adjacent point cloud data. In the processing procedure, whether the point cloud data are adjacent or not is determined based on the distance between the point cloud data, and then a connection relation is generated. In another processing procedure, according to the one-dimensional index values of all the point cloud data in the target area, the adjacent point cloud data is searched in a spherical space with the point cloud data P as the center of sphere and r3 as the radius. And if the density value of the searched adjacent point cloud data is greater than that of the point cloud data P, generating a connection relation between the point cloud data P and the adjacent point cloud data. In the processing process, whether the point cloud data are adjacent or not is determined based on the distance between the point cloud data and the density of the point cloud data, and then a connection relation is generated.
Therefore, for each point cloud data in the target area, the point cloud density is relatively high in a space with a relatively small distance from the point cloud data, other point cloud data adjacent to the point cloud data can be searched according to the one-dimensional index value of the point cloud data in the target area, the connection relation between the point cloud data is obtained based on distance judgment, and the accuracy of establishing the proximity relation is improved. In a space with a relatively large distance from the point cloud data, the point cloud density is relatively low, other point cloud data adjacent to the point cloud data can be searched according to the one-dimensional index value and the density value of the point cloud data in the target area, the connection relation between the point cloud data is obtained based on distance judgment and the point cloud density, and the accuracy of establishing the adjacent relation is improved. Therefore, the method provided by the embodiment improves the accuracy of establishing the proximity relation, and further improves the accuracy of point cloud segmentation in subsequent processing.
Optionally, in S402, obtaining a plurality of point cloud clusters according to the proximity relation tree may include:
and segmenting a plurality of point cloud data with the same root node in the adjacent relation tree into the same point cloud cluster.
Specifically, through the above two processing procedures, the proximity relationship between the point cloud data is established, so that a plurality of point cloud data having the same root node in the proximity relationship tree can be segmented into the same point cloud cluster, thereby completing the segmentation of the point cloud data.
Optionally, in this embodiment, the method may further include:
and performing ascending arrangement or descending arrangement on the one-dimensional index value of each point cloud data to obtain an index value sequence.
Correspondingly, searching adjacent point cloud data in a preset three-dimensional space according to the one-dimensional index value of each point cloud data may include:
and determining grids included in the preset three-dimensional space according to the index value sequence and the preset three-dimensional space.
And searching adjacent point cloud data in a grid included in a preset three-dimensional space.
Specifically, reference may be made to the related description in the embodiment shown in fig. 3, and the technical principle and the technical effect are similar, which are not described herein again. The difference is that in the embodiment shown in fig. 3, the method is applied to obtain the density value of the point cloud data, where the preset three-dimensional space may be referred to as a first three-dimensional space. In the embodiment, the method is applied to acquiring the point cloud cluster. The predetermined three-dimensional space involved therein may be referred to as a second three-dimensional space and a third three-dimensional space.
In the following, the processing result of the point cloud segmentation method provided by the present application is exemplarily described by examples. Fig. 5A-5B are schematic diagrams illustrating an effect of point cloud data segmentation provided in the embodiment of the present application. As shown in fig. 5A, in an application scenario of an intelligent vehicle, a laser radar may be set on the vehicle to obtain point cloud data in a road scenario. Wherein the point cloud data does not include ground point cloud data. Fig. 5B shows a point cloud cluster obtained according to the point cloud segmentation method provided in the present application.
Fig. 6 is a schematic structural diagram of a point cloud segmentation system according to an embodiment of the present disclosure. The point cloud segmentation system provided by this embodiment is used to execute the point cloud segmentation method provided by any one of the implementation manners of fig. 2 to fig. 4. As shown in fig. 6, the point cloud segmentation system provided in this embodiment may include: memory 62, processor 61, and point cloud sensor 63. In some embodiments, the point cloud segmentation system may be a single sensor device, such as a point cloud sensor with data processing capabilities; in other embodiments, the point cloud segmentation system may also be a distributed system, such as a memory, processor, and point cloud sensors located at different locations of the vehicle.
The point cloud sensor 63 is used for acquiring point cloud data to be processed in a target area;
the memory 62 for storing program code;
the processor 61, invoking the program code, when executed, is configured to:
acquiring a one-dimensional index value of each point cloud data according to the boundary information and the grid resolution of the three-dimensional map of the target area;
acquiring a density value of each point cloud data according to the one-dimensional index value of each point cloud data, wherein the density value of the point cloud data is used for indicating the point cloud density in a preset three-dimensional space, and the preset three-dimensional space comprises the point cloud data;
and segmenting the point cloud data to be processed according to the density value of each point cloud data to obtain a plurality of point cloud clusters.
Optionally, the processor 61 is specifically configured to:
and for each point cloud data in the point cloud data to be processed, if the point cloud data is in the three-dimensional map, acquiring a one-dimensional index value of each point cloud data according to the position information of the point cloud data, the boundary information of the three-dimensional map and the grid resolution.
Optionally, the processor 61 is specifically configured to:
acquiring a three-dimensional index value of the point cloud data according to the position information of the point cloud data, the boundary information of the three-dimensional map and the grid resolution; the three-dimensional index value comprises index values of grids where the point cloud data are located on three-dimensional coordinate axes corresponding to the three-dimensional map respectively;
and acquiring a one-dimensional index value of the point cloud data according to the three-dimensional index value and the number of grids respectively included by the three-dimensional map on the three-dimensional coordinate axis.
Optionally, the processor 61 is further configured to:
and if the point cloud data is not in the three-dimensional map, determining that the one-dimensional index value of the point cloud data is a preset invalid value.
Optionally, the processor 61 is specifically configured to:
if adjacent point cloud data are searched in the preset three-dimensional space according to the one-dimensional index value of each point cloud data, acquiring the number of the adjacent point cloud data; the preset three-dimensional space is a first three-dimensional space;
and acquiring the density value of the point cloud data according to the number of the adjacent point cloud data and the first three-dimensional space.
Optionally, the first three-dimensional space is a spherical space with the point cloud data as a circle center and a first preset distance as a radius; the processor 61 is specifically configured to:
and acquiring the density value of the point cloud data according to the number of the adjacent point cloud data and the volume of the spherical space.
Optionally, the processor 61 is specifically configured to:
traversing the point cloud data to be processed to obtain a proximity relation tree;
acquiring the plurality of point cloud clusters according to the adjacent relation tree;
wherein traversing the point cloud data to be processed comprises:
if adjacent point cloud data are searched in a preset three-dimensional space according to the one-dimensional index value of each point cloud data, generating a connection relation between the point cloud data and the adjacent point cloud data; wherein the preset three-dimensional space is a second three-dimensional space;
if adjacent point cloud data are searched in a preset three-dimensional space according to the one-dimensional index value of each point cloud data, and the density value of the adjacent point cloud data is greater than that of the point cloud data, generating a connection relation between the point cloud data and the adjacent point cloud data; the preset three-dimensional space is a third three-dimensional space, and the third three-dimensional space is larger than the second three-dimensional space.
Optionally, the processor 61 is specifically configured to:
and segmenting a plurality of point cloud data with the same root node in the adjacent relation tree into the same point cloud cluster.
Optionally, the processor 61 is further configured to:
performing ascending arrangement or descending arrangement on the one-dimensional index value of each point cloud data to obtain an index value sequence;
searching adjacent point cloud data in a preset three-dimensional space according to the one-dimensional index value of each point cloud data, and the method comprises the following steps:
determining grids included in the preset three-dimensional space according to the index value sequence and the preset three-dimensional space;
and searching adjacent point cloud data in a grid included in the preset three-dimensional space.
Optionally, the processor 61 is further configured to
Deleting invalid point cloud data in the point cloud data to be processed before acquiring the density value of each point cloud data according to the one-dimensional index value of each point cloud data; the invalid point cloud data is point cloud data which is not in the three-dimensional map.
Optionally, the point cloud data to be processed is point cloud data except ground point cloud data in the point cloud data acquired by the laser radar.
Optionally, the processor 61 is further configured to:
acquiring the number of point cloud data in each point cloud cluster;
and if the number of the point cloud data in the point cloud cluster is less than a preset value, deleting the point cloud cluster.
Optionally, the point cloud segmentation system is a laser radar.
The point cloud segmentation system provided in this embodiment is configured to execute the point cloud segmentation method provided in any one of the implementation manners of fig. 2 to fig. 4, and the technical scheme and the technical effect are similar, which are not repeated here.
It is noted that the memory 62, the processor 61 and the point cloud sensor 63 may be integrated in one device, for example, in a lidar. The lidar may segment point cloud data. The memory 62, the processor 61 and the point cloud sensor 63 may also be integrated in different devices, for example, the memory 62 and the processor 61 may be integrated in an electronic device having data processing capability, and the point cloud sensor 63 may be provided in the lidar. After the laser radar acquires the point cloud data, the point cloud data can be sent to the electronic equipment.
The application also provides a movable platform which can comprise the point cloud segmentation system provided by the embodiment shown in fig. 6. It should be noted that, in this embodiment, the type of the movable platform is not limited, and may be any device that can partition point cloud data. For example, it may be a drone, a vehicle, or other vehicle.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the embodiments of the present application, and are not limited thereto; although the embodiments of the present application have been described in detail with reference to the foregoing embodiments, those skilled in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.
Claims (28)
1. A point cloud segmentation method, comprising:
acquiring point cloud data to be processed in a target area;
acquiring a one-dimensional index value of each point cloud data according to the boundary information and the grid resolution of the three-dimensional map of the target area;
acquiring a density value of each point cloud data according to the one-dimensional index value of each point cloud data, wherein the density value of the point cloud data is used for indicating the point cloud density in a preset three-dimensional space, and the preset three-dimensional space comprises the point cloud data;
and segmenting the point cloud data to be processed according to the density value of each point cloud data to obtain a plurality of point cloud clusters.
2. The method of claim 1, wherein the obtaining a one-dimensional index value of each point cloud data according to the boundary information and the grid resolution of the three-dimensional map of the target area comprises:
and for each point cloud data in the point cloud data to be processed, if the point cloud data is in the three-dimensional map, acquiring a one-dimensional index value of each point cloud data according to the position information of the point cloud data, the boundary information of the three-dimensional map and the grid resolution.
3. The method of claim 2, wherein obtaining a one-dimensional index value for each of the point cloud data according to the location information of the point cloud data, the boundary information of the three-dimensional map, and the grid resolution comprises:
acquiring a three-dimensional index value of the point cloud data according to the position information of the point cloud data, the boundary information of the three-dimensional map and the grid resolution; the three-dimensional index value comprises index values of grids where the point cloud data are located on three-dimensional coordinate axes corresponding to the three-dimensional map respectively;
and acquiring a one-dimensional index value of the point cloud data according to the three-dimensional index value and the number of grids respectively included by the three-dimensional map on the three-dimensional coordinate axis.
4. The method according to any one of claims 2-3, further comprising:
and if the point cloud data is not in the three-dimensional map, determining that the one-dimensional index value of the point cloud data is a preset invalid value.
5. The method of claim 1, wherein obtaining the density value of each point cloud data according to the one-dimensional index value of each point cloud data comprises:
if adjacent point cloud data are searched in the preset three-dimensional space according to the one-dimensional index value of each point cloud data, acquiring the number of the adjacent point cloud data; the preset three-dimensional space is a first three-dimensional space;
and acquiring the density value of the point cloud data according to the number of the adjacent point cloud data and the first three-dimensional space.
6. The method of claim 5, wherein the first three-dimensional space is a spherical space with the point cloud data as a center and a first preset distance as a radius; the obtaining of the density value of the point cloud data according to the number of the adjacent points and the first three-dimensional space includes:
and acquiring the density value of the point cloud data according to the number of the adjacent point cloud data and the volume of the spherical space.
7. The method of claim 1, wherein the segmenting the point cloud data to be processed according to the density value of each point cloud data to obtain a plurality of point cloud clusters comprises:
traversing the point cloud data to be processed to obtain a proximity relation tree;
acquiring the plurality of point cloud clusters according to the adjacent relation tree;
wherein traversing the point cloud data to be processed comprises:
if adjacent point cloud data are searched in a preset three-dimensional space according to the one-dimensional index value of each point cloud data, generating a connection relation between the point cloud data and the adjacent point cloud data; wherein the preset three-dimensional space is a second three-dimensional space;
if adjacent point cloud data are searched in a preset three-dimensional space according to the one-dimensional index value of each point cloud data, and the density value of the adjacent point cloud data is greater than that of the point cloud data, generating a connection relation between the point cloud data and the adjacent point cloud data; the preset three-dimensional space is a third three-dimensional space, and the third three-dimensional space is larger than the second three-dimensional space.
8. The method of claim 7, wherein the obtaining the plurality of point cloud clusters from the neighborhood relationship tree comprises:
and segmenting a plurality of point cloud data with the same root node in the adjacent relation tree into the same point cloud cluster.
9. The method of claim 5 or 7, further comprising:
performing ascending arrangement or descending arrangement on the one-dimensional index value of each point cloud data to obtain an index value sequence;
searching adjacent point cloud data in a preset three-dimensional space according to the one-dimensional index value of each point cloud data, and the method comprises the following steps:
determining grids included in the preset three-dimensional space according to the index value sequence and the preset three-dimensional space;
and searching adjacent point cloud data in a grid included in the preset three-dimensional space.
10. The method of any of claims 1-9, further comprising, prior to obtaining the density value of each point cloud data from the one-dimensional index value of each point cloud data:
deleting invalid point cloud data from the point cloud data to be processed; the invalid point cloud data is point cloud data which is not in the three-dimensional map.
11. The method according to any one of claims 1 to 9, wherein the point cloud data to be processed is point cloud data other than ground point cloud data among point cloud data acquired by a laser radar.
12. The method according to any one of claims 1-9, further comprising:
acquiring the number of point cloud data in each point cloud cluster;
and if the number of the point cloud data in the point cloud cluster is less than a preset value, deleting the point cloud cluster.
13. A point cloud segmentation system, comprising: a memory, a processor, and a point cloud sensor;
the point cloud sensor is used for acquiring point cloud data to be processed in a target area;
the memory for storing program code;
the processor, invoking the program code, when executed, is configured to:
acquiring a one-dimensional index value of each point cloud data according to the boundary information and the grid resolution of the three-dimensional map of the target area;
acquiring a density value of each point cloud data according to the one-dimensional index value of each point cloud data, wherein the density value of the point cloud data is used for indicating the point cloud density in a preset three-dimensional space, and the preset three-dimensional space comprises the point cloud data;
and segmenting the point cloud data to be processed according to the density value of each point cloud data to obtain a plurality of point cloud clusters.
14. The system of claim 13, wherein the processor is specifically configured to:
and for each point cloud data in the point cloud data to be processed, if the point cloud data is in the three-dimensional map, acquiring a one-dimensional index value of each point cloud data according to the position information of the point cloud data, the boundary information of the three-dimensional map and the grid resolution.
15. The system of claim 14, wherein the processor is specifically configured to:
acquiring a three-dimensional index value of the point cloud data according to the position information of the point cloud data, the boundary information of the three-dimensional map and the grid resolution; the three-dimensional index value comprises index values of grids where the point cloud data are located on three-dimensional coordinate axes corresponding to the three-dimensional map respectively;
and acquiring a one-dimensional index value of the point cloud data according to the three-dimensional index value and the number of grids respectively included by the three-dimensional map on the three-dimensional coordinate axis.
16. The system of any one of claims 14-15, wherein the processor is further configured to:
and if the point cloud data is not in the three-dimensional map, determining that the one-dimensional index value of the point cloud data is a preset invalid value.
17. The system of claim 13, wherein the processor is specifically configured to:
if adjacent point cloud data are searched in the preset three-dimensional space according to the one-dimensional index value of each point cloud data, acquiring the number of the adjacent point cloud data; the preset three-dimensional space is a first three-dimensional space;
and acquiring the density value of the point cloud data according to the number of the adjacent point cloud data and the first three-dimensional space.
18. The system of claim 17, wherein the first three-dimensional space is a spherical space with the point cloud data as a center and a first preset distance as a radius; the processor is specifically configured to:
and acquiring the density value of the point cloud data according to the number of the adjacent point cloud data and the volume of the spherical space.
19. The system of claim 13, wherein the processor is specifically configured to:
traversing the point cloud data to be processed to obtain a proximity relation tree;
acquiring the plurality of point cloud clusters according to the adjacent relation tree;
wherein traversing the point cloud data to be processed comprises:
if adjacent point cloud data are searched in a preset three-dimensional space according to the one-dimensional index value of each point cloud data, generating a connection relation between the point cloud data and the adjacent point cloud data; wherein the preset three-dimensional space is a second three-dimensional space;
if adjacent point cloud data are searched in a preset three-dimensional space according to the one-dimensional index value of each point cloud data, and the density value of the adjacent point cloud data is greater than that of the point cloud data, generating a connection relation between the point cloud data and the adjacent point cloud data; the preset three-dimensional space is a third three-dimensional space, and the third three-dimensional space is larger than the second three-dimensional space.
20. The system of claim 19, wherein the processor is specifically configured to:
and segmenting a plurality of point cloud data with the same root node in the adjacent relation tree into the same point cloud cluster.
21. The system of claim 17 or 19, wherein the processor is further configured to:
performing ascending arrangement or descending arrangement on the one-dimensional index value of each point cloud data to obtain an index value sequence;
searching adjacent point cloud data in a preset three-dimensional space according to the one-dimensional index value of each point cloud data, and the method comprises the following steps:
determining grids included in the preset three-dimensional space according to the index value sequence and the preset three-dimensional space;
and searching adjacent point cloud data in a grid included in the preset three-dimensional space.
22. The system of any of claims 13-21, the processor further to
Deleting invalid point cloud data in the point cloud data to be processed before acquiring the density value of each point cloud data according to the one-dimensional index value of each point cloud data; the invalid point cloud data is point cloud data which is not in the three-dimensional map.
23. The system according to any one of claims 13 to 21, wherein the point cloud data to be processed is point cloud data other than ground point cloud data among point cloud data acquired by a laser radar.
24. The system of any one of claims 13-21, wherein the processor is further configured to:
acquiring the number of point cloud data in each point cloud cluster;
and if the number of the point cloud data in the point cloud cluster is less than a preset value, deleting the point cloud cluster.
25. The system of any one of claims 13-21, wherein the point cloud segmentation system is a lidar.
26. A movable platform, comprising: the point cloud segmentation system of any of claims 13-25.
27. The movable platform of claim 26, wherein the movable platform is a vehicle or a drone.
28. A computer storage medium, characterized in that the storage medium has stored therein a computer program which, when executed, implements the method of any one of claims 1-12.
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/CN2019/103314 WO2021035618A1 (en) | 2019-08-29 | 2019-08-29 | Point cloud segmentation method and system, and movable platform |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112166457A true CN112166457A (en) | 2021-01-01 |
Family
ID=73860466
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201980034128.0A Pending CN112166457A (en) | 2019-08-29 | 2019-08-29 | Point cloud segmentation method and system and movable platform |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN112166457A (en) |
| WO (1) | WO2021035618A1 (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112415490A (en) * | 2021-01-25 | 2021-02-26 | 天津卡雷尔机器人技术有限公司 | 3D point cloud scanning device based on 2D laser radar and registration algorithm |
| CN113269897A (en) * | 2021-07-19 | 2021-08-17 | 深圳市信润富联数字科技有限公司 | Method, device and equipment for acquiring surface point cloud and storage medium |
| CN113885567A (en) * | 2021-10-22 | 2022-01-04 | 北京理工大学 | Multi-unmanned aerial vehicle collaborative path planning method based on conflict search |
| CN115205717A (en) * | 2022-09-14 | 2022-10-18 | 广东汇天航空航天科技有限公司 | Obstacle point cloud data processing method and flight equipment |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112907747B (en) * | 2021-03-26 | 2024-10-29 | 上海商汤临港智能科技有限公司 | Point cloud data processing method and device, electronic equipment and storage medium |
| CN116797704B (en) * | 2023-08-24 | 2024-01-23 | 山东云海国创云计算装备产业创新中心有限公司 | Point cloud data processing method, system, device, electronic equipment and storage medium |
| CN116862985B (en) * | 2023-09-01 | 2023-11-10 | 江苏中安建设集团有限公司 | Land high-precision positioning electronic informatization monitoring method |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105354829A (en) * | 2015-10-08 | 2016-02-24 | 西北农林科技大学 | Self-adaptive point cloud data segmenting method |
| US20170023473A1 (en) * | 2015-07-21 | 2017-01-26 | Airbus Ds Electronics And Border Security Gmbh | Method for segmenting the data of a 3d sensor produced in the presence of aerosol clouds for increasing the situational awareness and the location detection of obstacles |
| CN106600622A (en) * | 2016-12-06 | 2017-04-26 | 西安电子科技大学 | Point cloud data partitioning method based on hyper voxels |
| CN107292276A (en) * | 2017-06-28 | 2017-10-24 | 武汉大学 | A kind of vehicle-mounted cloud clustering method and system |
| CN109816664A (en) * | 2018-12-25 | 2019-05-28 | 西安中科天塔科技股份有限公司 | A kind of three-dimensional point cloud dividing method and device |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107239794B (en) * | 2017-05-18 | 2020-04-28 | 深圳市速腾聚创科技有限公司 | Point cloud data segmentation method and terminal |
| CN108596860A (en) * | 2018-05-10 | 2018-09-28 | 芜湖航飞科技股份有限公司 | A kind of ground point cloud dividing method based on three-dimensional laser radar |
-
2019
- 2019-08-29 CN CN201980034128.0A patent/CN112166457A/en active Pending
- 2019-08-29 WO PCT/CN2019/103314 patent/WO2021035618A1/en active Application Filing
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170023473A1 (en) * | 2015-07-21 | 2017-01-26 | Airbus Ds Electronics And Border Security Gmbh | Method for segmenting the data of a 3d sensor produced in the presence of aerosol clouds for increasing the situational awareness and the location detection of obstacles |
| CN105354829A (en) * | 2015-10-08 | 2016-02-24 | 西北农林科技大学 | Self-adaptive point cloud data segmenting method |
| CN106600622A (en) * | 2016-12-06 | 2017-04-26 | 西安电子科技大学 | Point cloud data partitioning method based on hyper voxels |
| CN107292276A (en) * | 2017-06-28 | 2017-10-24 | 武汉大学 | A kind of vehicle-mounted cloud clustering method and system |
| CN109816664A (en) * | 2018-12-25 | 2019-05-28 | 西安中科天塔科技股份有限公司 | A kind of three-dimensional point cloud dividing method and device |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112415490A (en) * | 2021-01-25 | 2021-02-26 | 天津卡雷尔机器人技术有限公司 | 3D point cloud scanning device based on 2D laser radar and registration algorithm |
| CN113269897A (en) * | 2021-07-19 | 2021-08-17 | 深圳市信润富联数字科技有限公司 | Method, device and equipment for acquiring surface point cloud and storage medium |
| CN113269897B (en) * | 2021-07-19 | 2021-11-09 | 深圳市信润富联数字科技有限公司 | Method, device and equipment for acquiring surface point cloud and storage medium |
| CN113885567A (en) * | 2021-10-22 | 2022-01-04 | 北京理工大学 | Multi-unmanned aerial vehicle collaborative path planning method based on conflict search |
| CN113885567B (en) * | 2021-10-22 | 2023-08-04 | 北京理工大学 | Collaborative path planning method for multiple unmanned aerial vehicles based on conflict search |
| CN115205717A (en) * | 2022-09-14 | 2022-10-18 | 广东汇天航空航天科技有限公司 | Obstacle point cloud data processing method and flight equipment |
| CN115205717B (en) * | 2022-09-14 | 2022-12-20 | 广东汇天航空航天科技有限公司 | Obstacle point cloud data processing method and flight equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2021035618A1 (en) | 2021-03-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112166457A (en) | Point cloud segmentation method and system and movable platform | |
| CN109509210B (en) | Obstacle tracking method and device | |
| CN111192295B (en) | Target detection and tracking method, apparatus, and computer-readable storage medium | |
| CN109143207B (en) | Laser radar internal reference precision verification method, device, equipment and medium | |
| CN110674705B (en) | Small-sized obstacle detection method and device based on multi-line laser radar | |
| CN111612728B (en) | 3D point cloud densification method and device based on binocular RGB image | |
| CN110632617B (en) | Laser radar point cloud data processing method and device | |
| WO2024012211A1 (en) | Autonomous-driving environmental perception method, medium and vehicle | |
| CN111308500B (en) | Obstacle sensing method and device based on single-line laser radar and computer terminal | |
| CN113111513B (en) | Sensor configuration scheme determining method and device, computer equipment and storage medium | |
| WO2024012212A1 (en) | Environmental perception method, domain controller, storage medium, and vehicle | |
| CN111142514B (en) | Robot and obstacle avoidance method and device thereof | |
| CN112166458A (en) | Target detection and tracking method, system, equipment and storage medium | |
| US20230251097A1 (en) | Efficient map matching method for autonomous driving and apparatus thereof | |
| CN115523935A (en) | Point cloud ground detection method and device, vehicle and storage medium | |
| CN113281782A (en) | Laser radar snow point filtering method based on unmanned vehicle | |
| CN111611900A (en) | Target point cloud identification method and device, electronic equipment and storage medium | |
| CN113345072A (en) | Multi-view remote sensing topographic image point cloud reconstruction method and system | |
| CN111736167B (en) | Method and device for obtaining laser point cloud density | |
| CN112861595A (en) | Method and device for identifying data points and computer-readable storage medium | |
| CN111742242A (en) | Point cloud processing method, system, device and storage medium | |
| CN113256574B (en) | Three-dimensional target detection method | |
| CN104898106B (en) | Towards the ground point extracting method of complicated landform airborne laser radar data | |
| CN115235482A (en) | Map updating method, map updating device, computer equipment and medium | |
| KR102624644B1 (en) | Method of estimating the location of a moving object using vector map |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20240522 Address after: Building 3, Xunmei Science and Technology Plaza, No. 8 Keyuan Road, Science and Technology Park Community, Yuehai Street, Nanshan District, Shenzhen City, Guangdong Province, 518057, 1634 Applicant after: Shenzhen Zhuoyu Technology Co.,Ltd. Country or region after: China Address before: 518057 Shenzhen Nanshan High-tech Zone, Shenzhen, Guangdong Province, 6/F, Shenzhen Industry, Education and Research Building, Hong Kong University of Science and Technology, No. 9 Yuexingdao, South District, Nanshan District, Shenzhen City, Guangdong Province Applicant before: SZ DJI TECHNOLOGY Co.,Ltd. Country or region before: China |