CN118212370A - Terrain analysis method and device based on point cloud data, electronic equipment and medium - Google Patents
Terrain analysis method and device based on point cloud data, electronic equipment and medium Download PDFInfo
- Publication number
- CN118212370A CN118212370A CN202410391358.5A CN202410391358A CN118212370A CN 118212370 A CN118212370 A CN 118212370A CN 202410391358 A CN202410391358 A CN 202410391358A CN 118212370 A CN118212370 A CN 118212370A
- Authority
- CN
- China
- Prior art keywords
- point cloud
- missing
- array
- determining
- ground
- 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
- 238000004458 analytical method Methods 0.000 title claims abstract description 31
- 238000000034 method Methods 0.000 claims abstract description 39
- 238000003491 array Methods 0.000 claims abstract description 31
- 238000012545 processing Methods 0.000 claims description 16
- 238000004590 computer program Methods 0.000 claims description 13
- 230000011218 segmentation Effects 0.000 claims description 3
- 230000008859 change Effects 0.000 description 10
- 238000004891 communication Methods 0.000 description 8
- 238000012876 topography Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- ADTDNFFHPRZSOT-PVFUSPOPSA-N ram-330 Chemical compound C([C@H]1N(CC2)C)C3=CC=C(OC)C(OC)=C3[C@]32[C@@]1(O)CC[C@@H](OC(=O)OCC)C3 ADTDNFFHPRZSOT-PVFUSPOPSA-N 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000011835 investigation Methods 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 238000012732 spatial analysis Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Landscapes
- Length Measuring Devices With Unspecified Measuring Means (AREA)
Abstract
The disclosure provides a terrain analysis method and device based on point cloud data, electronic equipment and medium, wherein the method comprises the steps of firstly acquiring point cloud arrays in two time periods, and respectively extracting ground point clouds in the point cloud arrays; then, a point cloud projection plane is determined, and two groups of ground point clouds are projected onto the point cloud projection plane respectively to obtain a projection grid; determining a missing point cloud and a missing point cloud according to the projection grid; grouping the missing point clouds with high concentration, determining corresponding collapse areas, and grouping the missing point clouds with high concentration, and determining corresponding filling areas; then, determining the volume of the collapse area according to the boundary of the collapse area, and determining the volume of the filling area according to the boundary of the filling area; and finally outputting the boundary and the volume of each region.
Description
Technical Field
The disclosure relates to the field of laser point cloud data processing, in particular to the technical field of terrain detection, and discloses a terrain analysis method and device based on point cloud data, electronic equipment and a medium.
Background
At present, the point cloud processing technology based on laser radar scanning is widely applied, and the point cloud is widely applied to the tasks of three-dimensional reconstruction, object recognition, gesture estimation and the like. The three-dimensional reconstruction is an important application direction of the point cloud technology, and a three-dimensional model of a scene can be obtained by processing the point cloud in the scene. In addition, point cloud technology is also applied to the field of robots. The robot can sense the surrounding environment through the point cloud data, so that autonomous navigation and obstacle avoidance are realized.
However, the point cloud is still in the starting stage in the traditional industry at present, and is lack of a system solution, for example, the analysis of the topography change of each industry, especially the degree of manual intervention in a multi-period data comparison scene is higher, the degree of intelligence is not high, and the following two problems exist: 1. in the current situation of conclusion according to knowledge and experience, an effective, scientific and rapid mode for analyzing the terrain change is lacking. 2. The more accurate data are needed to be manually inspected on site in a field, and are subjected to multi-period arrangement, so that the workload is very large, and the personnel safety cannot be guaranteed.
Disclosure of Invention
The disclosure provides a terrain analysis method and device based on point cloud data, electronic equipment and medium, so as to solve at least one technical problem.
According to an aspect of the present disclosure, there is provided a terrain analysis method based on point cloud data, including:
Acquiring a first point cloud array obtained by carrying out laser scanning on a target area in a first time period, and a second point cloud array obtained by carrying out laser scanning on the target area in a second time period; extracting the ground point clouds in the first point cloud array and the second point cloud array respectively to obtain a first ground point cloud array and a second ground point Yun Shuzu; wherein the first period of time is earlier than the second period of time;
Determining a point cloud main direction according to the first and second ground point cloud arrays, and determining a point cloud projection plane according to the point cloud main direction; respectively projecting the first ground point cloud array and the second ground point cloud array onto the point cloud projection plane to obtain projection grids; determining a missing point cloud of the second ground point Yun Shuzu relative to the first ground point cloud array and determining a missing point cloud of the second ground point Yun Shuzu relative to the first ground point cloud array according to the obtained projected grid;
Determining the concentration degree of missing point clouds by using a clustering grouping method, dividing the missing point clouds with the concentration degree higher than a preset concentration degree into a plurality of missing point cloud arrays, determining the concentration degree of missing point clouds by using a clustering grouping method, and dividing the missing point clouds with the concentration degree higher than the preset concentration degree into a plurality of missing point cloud arrays;
Determining the region boundaries corresponding to the missing point cloud arrays to obtain a plurality of collapse regions, and determining the region boundaries corresponding to the missing point cloud arrays to obtain a plurality of filling regions;
dividing the area into a plurality of grids aiming at each area in the collapse areas and the filling areas, filling the grids without point clouds with low points, constructing a triangular mesh by using the filled grids, and superposing the volumes of triangular columns in the triangular mesh to obtain the volume of the area;
Outputting the region boundary and the volume of each collapse region;
and outputting the region boundary and the volume of each filling region.
In one possible embodiment, the point cloud main direction includes an extension direction of a power line small-size tower to a large-size tower.
In one possible implementation manner, the determining the missing point cloud of the second ground point Yun Shuzu relative to the first ground point cloud array according to the obtained projection grid includes:
Comparing the projection grids corresponding to the first ground point cloud array and the second ground point cloud array, and taking the point cloud corresponding to the projection grid in which the first ground point cloud array exists and the projection grid in which the second ground point cloud array does not exist as a missing point cloud;
Determining a first difference value of the maximum elevation value in each projection grid of the first ground point cloud array minus the maximum elevation value in the corresponding projection grid of the second ground point cloud array, and taking the point cloud corresponding to the projection grid with the first difference value larger than a first preset threshold value as a missing point cloud.
In one possible implementation, the determining the missing point cloud of the second ground point Yun Shuzu with respect to the first ground point cloud array includes:
Comparing the projection grids of the first ground point cloud array and the second ground point cloud array, and taking the point cloud corresponding to the projection grid in which the second ground point cloud array exists and the projection grid in which the first ground point cloud array does not exist as the missing point cloud;
And determining a second difference value of the maximum elevation value in the corresponding projection grids in the first ground point cloud array subtracted from the maximum elevation value in each projection grid in the second ground point cloud array, and taking the point cloud corresponding to the projection grid with the second difference value larger than a second preset threshold value as the missing point cloud.
In one possible implementation manner, the terrain analysis method based on the point cloud data further includes:
Determining, for each of the plurality of collapse areas and the plurality of fill areas, a distance of the area from a forward direction start side;
the regions are ordered in order of distance from small to large.
In one possible implementation manner, the terrain analysis method based on the point cloud data further includes:
the orientation of each projected grid is determined, and an index for each projected grid is determined.
In one possible implementation manner, the determining the region boundary corresponding to each missing point cloud array to obtain a plurality of collapse regions, and determining the region boundary corresponding to each missing point cloud array to obtain a plurality of filling regions include:
Determining a convex polygon boundary point corresponding to the missing point cloud array according to the coordinates of the point clouds in the missing point cloud array aiming at each missing point cloud array, and determining a region boundary corresponding to the missing point cloud array by utilizing the determined convex polygon boundary point to obtain a collapse region;
And determining a convex polygon boundary point corresponding to the missing point cloud array according to the coordinates of the point clouds in the missing point cloud array aiming at each missing point cloud array, and determining a region boundary corresponding to the missing point cloud array by utilizing the determined convex polygon boundary point to obtain a filling region.
According to another aspect of the present disclosure, there is provided a terrain analysis apparatus based on point cloud data, including:
the point cloud processing module is used for acquiring a first point cloud array obtained by carrying out laser scanning on a target area in a first time period and a second point cloud array obtained by carrying out laser scanning on the target area in a second time period; extracting the ground point clouds in the first point cloud array and the second point cloud array respectively to obtain a first ground point cloud array and a second ground point Yun Shuzu; wherein the first period of time is earlier than the second period of time;
The deformation area preliminary determination module is used for determining a point cloud main direction according to the first ground point cloud array and the second ground point cloud array and determining a point cloud projection plane according to the point cloud main direction; respectively projecting the first ground point cloud array and the second ground point cloud array onto the point cloud projection plane to obtain projection grids; determining a missing point cloud of the second ground point Yun Shuzu relative to the first ground point cloud array and determining a missing point cloud of the second ground point Yun Shuzu relative to the first ground point cloud array according to the obtained projected grid;
The point cloud segmentation module is used for determining the concentration degree of the missing point cloud by using a clustering grouping method, segmenting the missing point cloud with the concentration degree higher than a preset concentration degree into a plurality of missing point cloud arrays, determining the concentration degree of the missing point cloud by using a clustering grouping method, and segmenting the missing point cloud with the concentration degree higher than the preset concentration degree into a plurality of missing point cloud arrays;
The deformation area determining module is used for determining area boundaries corresponding to the missing point cloud arrays to obtain a plurality of collapse areas, and determining area boundaries corresponding to the missing point cloud arrays to obtain a plurality of filling areas;
The deformation area processing module is used for dividing the area into a plurality of grids aiming at each area in the collapse areas and the filling areas, filling the grids without point clouds with low points, constructing a triangular mesh by using the filled grids, and superposing the volumes of all triangular posts in the triangular mesh to obtain the volume of the area;
the output module is used for outputting the region boundary and the volume of each collapse region; and outputting the region boundary and the volume of each filled region.
According to another aspect of the present disclosure, there is provided an electronic device comprising a memory, a processor and a computer program stored on the memory, the processor implementing the method of any one of the above when the computer program is executed.
According to another aspect of the present disclosure, there is provided a computer readable storage medium having stored therein a computer program which, when executed by a processor, implements the method of any one of the above.
The topographic analysis method, the topographic analysis device, the electronic equipment and the medium based on the point cloud data comprise the steps of firstly acquiring point cloud arrays in two time periods, and respectively extracting the ground point clouds in the point cloud arrays; then, a point cloud projection plane is determined, and two groups of ground point clouds are projected onto the point cloud projection plane respectively to obtain a projection grid; determining a missing point cloud and a missing point cloud according to the projection grid; grouping the missing point clouds with high concentration, determining corresponding collapse areas, and grouping the missing point clouds with high concentration, and determining corresponding filling areas; then, determining the volume of the collapse area according to the boundary of the collapse area, and determining the volume of the filling area according to the boundary of the filling area; and finally outputting the boundary and the volume of each region. The terrain analysis scheme based on the point cloud can efficiently and accurately obtain the terrain variation analysis result, and solves the problems of high field operation, unsafe investigation, low efficiency and the like existing in artificial analysis of the terrain variation; meanwhile, the method and the device provide clear analysis results and a general analysis result output mode, can support multi-platform presentation of analysis results, and can be widely applied.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is a flow chart of a terrain analysis method based on point cloud data according to the present disclosure;
FIG. 2 is a schematic structural view of a terrain analysis device based on point cloud data according to the present disclosure;
fig. 3 is a schematic structural view of an electronic device according to the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The utility model provides a topography analysis method and device, electronic equipment, medium based on point cloud data, the scheme of this disclosure carries out the condition analysis of topography change based on the point cloud data of different period, improves the efficiency of topography change condition analysis, alleviates the work load of manual on-the-spot operation, improves the accuracy of analysis result, personnel's operation safety and the degree of automation of operation in the defect that degree of automation is low, manual work intensity is big, the security is poor and accuracy, the efficiency all exists in present topography analysis scheme.
The technical scheme of the present disclosure is described below by means of specific examples.
As shown in fig. 1, a flowchart of a terrain analysis method based on point cloud data according to the present embodiment is shown, where the execution subject of the present embodiment is a computing device or a component with data processing capability, and the method of the present embodiment may include the following steps:
S110, acquiring a first point cloud array obtained by carrying out laser scanning on a target area in a first time period, and a second point cloud array obtained by carrying out laser scanning on the target area in a second time period; extracting the ground point clouds in the first point cloud array and the second point cloud array respectively to obtain a first ground point cloud array and a second ground point Yun Shuzu; wherein the first period of time is earlier than the second period of time.
The first period of time may be a certain period of time of the history, and the second period of time may be a current period of time. The target area is a research area, namely an area needing to be subjected to terrain variation analysis.
Specifically, the unmanned aerial vehicle can be carried with a laser radar scanning system to scan a research area, and two-stage point cloud data of the research area are collected.
The point cloud data collected by the lidar scanning system includes all objects of the investigation region, including available and unavailable portions, and even noise points, and after the original point cloud data is obtained, the point cloud data needs to be classified, that is, the content represented by the mark point cloud mass or the region. And filtering the useless points according to the classification, and extracting and dividing the useful parts to obtain available point cloud data, namely the ground point cloud.
The classification method can be manual classification or automatic classification according to a point cloud clustering mode or a point cloud mass shape, and manual repair can be performed after classification.
S120, determining a point cloud main direction according to the first ground point cloud array and the second ground point cloud array, and determining a point cloud projection plane according to the point cloud main direction; respectively projecting the first ground point cloud array and the second ground point cloud array onto the point cloud projection plane to obtain projection grids; from the resulting projected grid, a missing point cloud of the second ground point Yun Shuzu relative to the first ground point cloud array is determined, and a missing point cloud of the second ground point Yun Shuzu relative to the first ground point cloud array is determined.
The method comprises the steps of firstly reading the first ground point cloud array and the second ground point cloud array, then determining a point cloud main direction according to the first ground point cloud array and the second ground point cloud array, wherein the point cloud main direction can be the extending direction from a small-size power line tower to a large-size power line tower, the small-size power line tower is a power line tower pole with a small number, and the large-size power line tower is a power line tower pole with a large number.
The above-mentioned point cloud projection plane is the point cloud main direction XY plane, and this step may include the elevation value of each point cloud. In addition, the step can calculate the azimuth of each projection grid, calculate the index of each projection grid and acquire the maximum elevation value of the projection point cloud in each projection grid.
The missing point cloud may be determined here using the following steps: and comparing the projection grids corresponding to the first ground point cloud array and the second ground point cloud array, and taking the point cloud corresponding to the projection grid in which the first ground point cloud array exists and the projection grid in which the second ground point cloud array does not exist as the missing point cloud. The missing point cloud herein may be referred to as a slump point cloud.
The missing point cloud may be determined here using the following steps: and comparing the projection grids of the first ground point cloud array and the second ground point cloud array, and taking the point cloud corresponding to the projection grid in which the second ground point cloud array exists and the projection grid in which the first ground point cloud array does not exist as the missing point cloud. The missing point cloud may also be referred to herein as a fill point cloud.
S130, determining the concentration degree of the missing point cloud by using a clustering grouping method, dividing the missing point cloud with the concentration degree higher than a preset concentration degree into a plurality of missing point cloud arrays, determining the concentration degree of the missing point cloud by using a clustering grouping method, and dividing the missing point cloud with the concentration degree higher than the preset concentration degree into a plurality of missing point cloud arrays.
In this step, the missing point cloud and the portion in which the missing point cloud is relatively concentrated are respectively divided into a plurality of groups, and the groups correspond to the terrain change areas. Specifically, in the step, a clustering-based grouping method is established, the point cloud concentration degree of the missing point cloud and the point cloud to be missing is obtained, the relatively concentrated parts of the missing point cloud and the point cloud to be missing are grouped by identifying the point cloud concentration degree, and the point cloud groups are divided into a plurality of point cloud arrays, and the point cloud arrays correspond to the terrain change areas.
And S140, determining the region boundaries corresponding to the missing point cloud arrays to obtain a plurality of collapse regions, and determining the region boundaries corresponding to the missing point cloud arrays to obtain a plurality of filling regions.
Specifically, the collapse area and the filling area can be determined by the following steps:
And aiming at each missing point cloud array, determining a convex polygon boundary point corresponding to the missing point cloud array according to the coordinates of the point clouds in the missing point cloud array, and determining a region boundary corresponding to the missing point cloud array by utilizing the determined convex polygon boundary point to obtain a collapse region. And determining a convex polygon boundary point corresponding to the missing point cloud array according to the coordinates of the point clouds in the missing point cloud array aiming at each missing point cloud array, and determining a region boundary corresponding to the missing point cloud array by utilizing the determined convex polygon boundary point to obtain a filling region.
The collapse area and the filling area are areas with topography change.
This step may further comprise: for each of the plurality of collapse areas and the plurality of fill areas, a distance from the area to the forward direction start side is determined, and the respective areas are ordered in order of the distance from the smaller to the larger. And the front direction initial side is the position of the power line tower pole with the minimum number.
The step is that convex polygon boundary points of various terrain change areas are obtained through an algorithm, and area boundaries are determined based on the convex polygon boundary points; the distance of each terrain variation area from the forward direction start side can then be determined by spatial analysis and the terrain variation areas or the identifiers of the terrain variation areas are ordered in order from small to large.
Steps S120-S140 implement: extracting and calculating missing point cloud data of the ground point cloud acquired in the second time period relative to the ground point cloud acquired in the first time period, and determining a collapse area based on the missing point cloud data; and extracting and calculating point cloud data of missing ground point clouds acquired in the first time period relative to the ground point clouds acquired in the second time period, and determining a filling area based on the point cloud data.
S150, dividing the area into a plurality of grids aiming at each area in the collapse areas and the filling areas, filling the grids without point clouds with low points, constructing a triangular mesh by using the filled grids, and superposing the volumes of triangular columns in the triangular mesh to obtain the volume of the area;
S160, outputting the region boundary and the volume of each collapse region; and outputting the region boundary and the volume of each filled region.
The step can output the region boundary of each terrain change region in the form of shp file so as to be presented in each large three-dimensional software, system and scene.
According to the embodiment, the unmanned aerial vehicle can be carried with the laser radar scanning system to obtain a multi-period point cloud array, then the multi-period point cloud array is classified to extract ground point clouds, then the point clouds of two-period ground point clouds are compared, the changed point clouds are extracted, and the changed point clouds are analyzed and identified to obtain a terrain change area.
In some embodiments, the missing point cloud may also be determined using the following steps: determining a first difference value of the maximum elevation value in each projection grid of the first ground point cloud array minus the maximum elevation value in the corresponding projection grid of the second ground point cloud array, and taking the point cloud corresponding to the projection grid with the first difference value larger than a first preset threshold value as a missing point cloud.
In some embodiments, the missing point cloud may also be determined using the following steps: and determining a second difference value of the maximum elevation value in the corresponding projection grids in the first ground point cloud array subtracted from the maximum elevation value in each projection grid in the second ground point cloud array, and taking the point cloud corresponding to the projection grid with the second difference value larger than a second preset threshold value as the missing point cloud.
Based on the same inventive concept, the present disclosure provides a terrain analysis device based on point cloud data, where the steps executed by the components of the device are the same as or similar to those of the above method, so that similar parts are not described in detail, as shown in fig. 2, the terrain analysis device based on point cloud data in this embodiment includes:
The point cloud processing module 210 is configured to obtain a first point cloud array obtained by performing laser scanning on a target area in a first period of time, and a second point cloud array obtained by performing laser scanning on the target area in a second period of time; extracting the ground point clouds in the first point cloud array and the second point cloud array respectively to obtain a first ground point cloud array and a second ground point Yun Shuzu; wherein the first period of time is earlier than the second period of time.
The deformation region preliminary determination module 220 is configured to determine a point cloud main direction according to the first ground point cloud array and the second ground point cloud array, and determine a point cloud projection plane according to the point cloud main direction; respectively projecting the first ground point cloud array and the second ground point cloud array onto the point cloud projection plane to obtain projection grids; from the resulting projected grid, a missing point cloud of the second ground point Yun Shuzu relative to the first ground point cloud array is determined, and a missing point cloud of the second ground point Yun Shuzu relative to the first ground point cloud array is determined.
The point cloud segmentation module 230 is configured to determine a concentration degree of missing point clouds by using a clustering grouping method, segment the missing point clouds with a concentration degree higher than a preset concentration degree into a plurality of missing point cloud arrays, determine a concentration degree of missing point clouds by using a clustering grouping method, and segment the missing point clouds with a concentration degree higher than the preset concentration degree into a plurality of missing point cloud arrays.
The deformation region determining module 240 is configured to determine a region boundary corresponding to each missing point cloud array to obtain a plurality of collapse regions, and determine a region boundary corresponding to each missing point cloud array to obtain a plurality of filling regions.
The deformed region processing module 250 is configured to divide the region into a plurality of grids for each of the plurality of collapse regions and the plurality of filling regions, fill the grids without point clouds with low points, construct a triangular mesh by using the filled grids, and superimpose volumes of triangular columns in the triangular mesh to obtain a volume of the region.
An output module 260 for outputting region boundaries and volumes of each collapse region; and outputting the region boundary and the volume of each filled region.
In some embodiments, the point cloud primary direction includes a direction of extension of a power line small-size tower to a large-size tower.
In some embodiments, the deformation region preliminary determining module 220 is specifically configured to, when determining the missing point cloud of the second ground point Yun Shuzu with respect to the first ground point cloud array according to the obtained projection grid:
Comparing the projection grids corresponding to the first ground point cloud array and the second ground point cloud array, and taking the point cloud corresponding to the projection grid in which the first ground point cloud array exists and the projection grid in which the second ground point cloud array does not exist as a missing point cloud;
Determining a first difference value of the maximum elevation value in each projection grid of the first ground point cloud array minus the maximum elevation value in the corresponding projection grid of the second ground point cloud array, and taking the point cloud corresponding to the projection grid with the first difference value larger than a first preset threshold value as a missing point cloud.
In some embodiments, the deformation region preliminary determination module 220 is specifically configured to, when determining the missing point cloud of the second ground point Yun Shuzu with respect to the first ground point cloud array:
Comparing the projection grids of the first ground point cloud array and the second ground point cloud array, and taking the point cloud corresponding to the projection grid in which the second ground point cloud array exists and the projection grid in which the first ground point cloud array does not exist as the missing point cloud;
And determining a second difference value of the maximum elevation value in the corresponding projection grids in the first ground point cloud array subtracted from the maximum elevation value in each projection grid in the second ground point cloud array, and taking the point cloud corresponding to the projection grid with the second difference value larger than a second preset threshold value as the missing point cloud.
In some embodiments, the deformation region determination module 240 is further to:
Determining, for each of the plurality of collapse areas and the plurality of fill areas, a distance of the area from a forward direction start side;
the regions are ordered in order of distance from small to large.
In some embodiments, the deformation region preliminary determination module 220 is further configured to:
the orientation of each projected grid is determined, and an index for each projected grid is determined.
In some embodiments, the deformation region determining module 240 is configured to, when determining a region boundary corresponding to each missing point cloud array to obtain a plurality of collapse regions, and determining a region boundary corresponding to each missing point cloud array to obtain a plurality of filling regions:
Determining a convex polygon boundary point corresponding to the missing point cloud array according to the coordinates of the point clouds in the missing point cloud array aiming at each missing point cloud array, and determining a region boundary corresponding to the missing point cloud array by utilizing the determined convex polygon boundary point to obtain a collapse region;
And determining a convex polygon boundary point corresponding to the missing point cloud array according to the coordinates of the point clouds in the missing point cloud array aiming at each missing point cloud array, and determining a region boundary corresponding to the missing point cloud array by utilizing the determined convex polygon boundary point to obtain a filling region.
According to an embodiment of the disclosure, the disclosure also provides an electronic device, a computer-readable storage medium.
FIG. 3 illustrates a schematic block diagram of an example electronic device 300 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 3, the apparatus 300 includes a computing unit 310 that may perform various suitable actions and processes according to a computer program stored in a Read Only Memory (ROM) 320 or a computer program loaded from a storage unit 380 into a Random Access Memory (RAM) 330. In RAM330, various programs and data required for the operation of device 300 may also be stored. The computing unit 310, ROM 320, and RAM330 are connected to each other by a bus 340. An input/output (I/O) interface 350 is also connected to bus 340.
Various components in device 300 are connected to I/O interface 350, including: an input unit 360 such as a keyboard, a mouse, etc.; an output unit 370 such as various types of displays, speakers, and the like; a storage unit 380 such as a magnetic disk, an optical disk, or the like; and a communication unit 390, such as a network card, modem, wireless communication transceiver, etc. The communication unit 390 allows the device 300 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunications networks.
The computing unit 310 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 310 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 310 performs the various methods and processes described above. For example, in some embodiments, any of the methods above may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 380. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 300 via the ROM320 and/or the communication unit 390. When the computer program is loaded into RAM330 and executed by computing unit 310, one or more steps of any of the methods described above may be performed. Alternatively, in other embodiments, computing unit 310 may be configured to perform any of the methods described above in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel or sequentially or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (10)
1. The terrain analysis method based on the point cloud data is characterized by comprising the following steps of:
Acquiring a first point cloud array obtained by carrying out laser scanning on a target area in a first time period, and a second point cloud array obtained by carrying out laser scanning on the target area in a second time period; extracting the ground point clouds in the first point cloud array and the second point cloud array respectively to obtain a first ground point cloud array and a second ground point Yun Shuzu; wherein the first period of time is earlier than the second period of time;
Determining a point cloud main direction according to the first and second ground point cloud arrays, and determining a point cloud projection plane according to the point cloud main direction; respectively projecting the first ground point cloud array and the second ground point cloud array onto the point cloud projection plane to obtain projection grids; determining a missing point cloud of the second ground point Yun Shuzu relative to the first ground point cloud array and determining a missing point cloud of the second ground point Yun Shuzu relative to the first ground point cloud array according to the obtained projected grid;
Determining the concentration degree of missing point clouds by using a clustering grouping method, dividing the missing point clouds with the concentration degree higher than a preset concentration degree into a plurality of missing point cloud arrays, determining the concentration degree of missing point clouds by using a clustering grouping method, and dividing the missing point clouds with the concentration degree higher than the preset concentration degree into a plurality of missing point cloud arrays;
Determining the region boundaries corresponding to the missing point cloud arrays to obtain a plurality of collapse regions, and determining the region boundaries corresponding to the missing point cloud arrays to obtain a plurality of filling regions;
dividing the area into a plurality of grids aiming at each area in the collapse areas and the filling areas, filling the grids without point clouds with low points, constructing a triangular mesh by using the filled grids, and superposing the volumes of triangular columns in the triangular mesh to obtain the volume of the area;
Outputting the region boundary and the volume of each collapse region;
and outputting the region boundary and the volume of each filling region.
2. The method of claim 1, wherein the point cloud primary direction comprises a direction of extension of a power line small-size tower to a large-size tower.
3. The method of claim 1, wherein determining a missing point cloud of the second ground point Yun Shuzu relative to the first ground point cloud array based on the resulting projected grid comprises:
Comparing the projection grids corresponding to the first ground point cloud array and the second ground point cloud array, and taking the point cloud corresponding to the projection grid in which the first ground point cloud array exists and the projection grid in which the second ground point cloud array does not exist as a missing point cloud;
Determining a first difference value of the maximum elevation value in each projection grid of the first ground point cloud array minus the maximum elevation value in the corresponding projection grid of the second ground point cloud array, and taking the point cloud corresponding to the projection grid with the first difference value larger than a first preset threshold value as a missing point cloud.
4. The method of claim 1, wherein determining a missing point cloud of the second ground point Yun Shuzu relative to the first ground point cloud array comprises:
Comparing the projection grids of the first ground point cloud array and the second ground point cloud array, and taking the point cloud corresponding to the projection grid in which the second ground point cloud array exists and the projection grid in which the first ground point cloud array does not exist as the missing point cloud;
And determining a second difference value of the maximum elevation value in the corresponding projection grids in the first ground point cloud array subtracted from the maximum elevation value in each projection grid in the second ground point cloud array, and taking the point cloud corresponding to the projection grid with the second difference value larger than a second preset threshold value as the missing point cloud.
5. The method as recited in claim 1, further comprising:
Determining, for each of the plurality of collapse areas and the plurality of fill areas, a distance of the area from a forward direction start side;
the regions are ordered in order of distance from small to large.
6. The method as recited in claim 1, further comprising:
the orientation of each projected grid is determined, and an index for each projected grid is determined.
7. The method of claim 1, wherein determining the region boundaries for each missing point cloud array to obtain a plurality of collapse regions, and determining the region boundaries for each missing point cloud array to obtain a plurality of fill regions, comprises:
Determining a convex polygon boundary point corresponding to the missing point cloud array according to the coordinates of the point clouds in the missing point cloud array aiming at each missing point cloud array, and determining a region boundary corresponding to the missing point cloud array by utilizing the determined convex polygon boundary point to obtain a collapse region;
And determining a convex polygon boundary point corresponding to the missing point cloud array according to the coordinates of the point clouds in the missing point cloud array aiming at each missing point cloud array, and determining a region boundary corresponding to the missing point cloud array by utilizing the determined convex polygon boundary point to obtain a filling region.
8. A terrain analysis device based on point cloud data, comprising:
the point cloud processing module is used for acquiring a first point cloud array obtained by carrying out laser scanning on a target area in a first time period and a second point cloud array obtained by carrying out laser scanning on the target area in a second time period; extracting the ground point clouds in the first point cloud array and the second point cloud array respectively to obtain a first ground point cloud array and a second ground point Yun Shuzu; wherein the first period of time is earlier than the second period of time;
The deformation area preliminary determination module is used for determining a point cloud main direction according to the first ground point cloud array and the second ground point cloud array and determining a point cloud projection plane according to the point cloud main direction; respectively projecting the first ground point cloud array and the second ground point cloud array onto the point cloud projection plane to obtain projection grids; determining a missing point cloud of the second ground point Yun Shuzu relative to the first ground point cloud array and determining a missing point cloud of the second ground point Yun Shuzu relative to the first ground point cloud array according to the obtained projected grid;
The point cloud segmentation module is used for determining the concentration degree of the missing point cloud by using a clustering grouping method, segmenting the missing point cloud with the concentration degree higher than a preset concentration degree into a plurality of missing point cloud arrays, determining the concentration degree of the missing point cloud by using a clustering grouping method, and segmenting the missing point cloud with the concentration degree higher than the preset concentration degree into a plurality of missing point cloud arrays;
The deformation area determining module is used for determining area boundaries corresponding to the missing point cloud arrays to obtain a plurality of collapse areas, and determining area boundaries corresponding to the missing point cloud arrays to obtain a plurality of filling areas;
The deformation area processing module is used for dividing the area into a plurality of grids aiming at each area in the collapse areas and the filling areas, filling the grids without point clouds with low points, constructing a triangular mesh by using the filled grids, and superposing the volumes of all triangular posts in the triangular mesh to obtain the volume of the area;
the output module is used for outputting the region boundary and the volume of each collapse region; and outputting the region boundary and the volume of each filled region.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory, the processor implementing the method of any one of claims 1-7 when the computer program is executed.
10. A computer readable storage medium having stored therein a computer program which, when executed by a processor, implements the method of any of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410391358.5A CN118212370A (en) | 2024-04-02 | 2024-04-02 | Terrain analysis method and device based on point cloud data, electronic equipment and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410391358.5A CN118212370A (en) | 2024-04-02 | 2024-04-02 | Terrain analysis method and device based on point cloud data, electronic equipment and medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN118212370A true CN118212370A (en) | 2024-06-18 |
Family
ID=91455265
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410391358.5A Pending CN118212370A (en) | 2024-04-02 | 2024-04-02 | Terrain analysis method and device based on point cloud data, electronic equipment and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118212370A (en) |
-
2024
- 2024-04-02 CN CN202410391358.5A patent/CN118212370A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP3852008A2 (en) | Image detection method and apparatus, device, storage medium and computer program product | |
| CN112652036B (en) | Road data processing method, device, equipment and storage medium | |
| CN110675635B (en) | Method and device for acquiring external parameters of camera, electronic equipment and storage medium | |
| CN111539347B (en) | Method and device for detecting target | |
| CN113947188A (en) | Training method of target detection network and vehicle detection method | |
| CN113971723B (en) | Method, device, equipment and storage medium for constructing three-dimensional map in high-precision map | |
| CN113506368B (en) | Map data fusion method, map data fusion device, electronic device, map data fusion medium, and program product | |
| CN113724388B (en) | High-precision map generation method, device, equipment and storage medium | |
| CN114186007A (en) | High-precision map generation method and device, electronic equipment and storage medium | |
| CN114627257A (en) | Three-dimensional road network map construction method and device, electronic equipment and storage medium | |
| CN113377890A (en) | Map quality inspection method and device, electronic equipment and storage medium | |
| CN114299242A (en) | Method, device and equipment for processing images in high-precision map and storage medium | |
| CN113569911A (en) | Vehicle identification method and device, electronic equipment and storage medium | |
| CN112580666A (en) | Image feature extraction method, training method, device, electronic equipment and medium | |
| CN116596750A (en) | Point cloud processing method and device, electronic equipment and storage medium | |
| CN113920273B (en) | Image processing method, device, electronic equipment and storage medium | |
| CN115830268A (en) | Data acquisition method and device for optimizing perception algorithm and storage medium | |
| CN113516013B (en) | Target detection method, target detection device, electronic equipment, road side equipment and cloud control platform | |
| CN118212370A (en) | Terrain analysis method and device based on point cloud data, electronic equipment and medium | |
| US20200242819A1 (en) | Polyline drawing device | |
| CN117739993B (en) | Robot positioning method and device, robot and storage medium | |
| CN114882461B (en) | Equipment environment recognition method and device, electronic equipment and automatic driving vehicle | |
| CN115638798A (en) | Map data generation method and device, electronic equipment and medium | |
| CN117853614A (en) | Method and device for detecting change condition of high-precision map element and vehicle | |
| CN118262313A (en) | Road area detection method and device and electronic equipment |
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 |